Patent Publication Number: US-2023164182-A1

Title: Cloud-based deception technology utilizing zero trust to identify threat intelligence, telemetry, and emerging adversary tactics and techniques

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure relates generally to networking and computing. More particularly, the present disclosure relates to systems and methods for cloud-based deception technology to identify threat intelligence, telemetry, and emerging adversary tactics and techniques. 
     BACKGROUND OF THE DISCLOSURE 
     Cyberthreats are evolving and becoming advanced as well as critically impacting business. For example, the Colonial Pipeline ransomware attack shut down operations for days and there was a need for immediate, high-confidence detections to drive effective response. Deception technology can provide rich threat intelligence, telemetry, and emerging adversary tactics and techniques. Conventional deception technology involves placing decoys (also known as honeypots, traps, etc.) on user devices and enterprise networks. These are typically placed at critical infrastructure where there cannot be a breach. The deception technology is able to monitor malicious actors to catch them and provide insight into how they operate. Security teams cannot separate the signal from the noise to take a proactive stance against the stealthiest attackers. Security analysts lose time chasing ghosts, the role of active defense (deception technology) has never been more critical. By taking the fight to the attacker, leading them down false paths with decoys deployed across networks, endpoints, and applications, and gathering the highest-fidelity security telemetry, it is possible to dramatically speed up threat hunting and containment. 
     Existing deception technology solutions typically require agents that are executed on user devices as well as on-site appliances located in the enterprise network. Cloud-based security solutions have emerged, such as Zscaler Internet Access (ZIA) and Zscaler Private Access (ZPA), available from Zscaler, Inc., the applicant and assignee of the present application. The problem with hardware-based solutions (with appliances) is they do not scale and require infrastructure in the customer network. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     The present disclosure relates to systems and methods for cloud-based deception technology to identify threat intelligence, telemetry, and emerging adversary tactics and techniques. Variously, the present disclosure includes integration of deception technology with a cloud-based security system, namely without on-premises appliances. Further, the present disclosure includes targeted threat detection where activity is logged only related to breadcrumbs (fake data intentionally put on the user device). Also, the present disclosure includes dynamic risk scoring where all hits on a breadcrumb/honeypot are malicious by definition—this risk scoring covers a unique way to convey the actual threat. The present disclosure includes an approach to rapidly deploy breadcrumbs/honeypots to make each one look unique and look like a customer environment, so the attackers do not know. Even further, the present disclosure enables the breadcrumbs/honeypots based on user type, e.g., sales, marketing, legal, R&amp;D, etc. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure is illustrated and described herein with reference to the various drawings, in which like reference numbers are used to denote like system components/method steps, as appropriate, and in which: 
         FIG.  1    is a network diagram of a cloud-based system offering security as a service. 
         FIG.  2    is a network diagram of an example implementation of the cloud-based system. 
         FIG.  3    is a block diagram of a server that may be used in the cloud-based system of  FIGS.  1  and  2    or the like. 
         FIG.  4    is a block diagram of a user device that may be used with the cloud-based system of  FIGS.  1  and  2    or the like. 
         FIG.  5    is a network diagram of the cloud-based system illustrating an application on user devices with users configured to operate through the cloud-based system. 
         FIG.  6    is a network diagram of the cloud-based system of  FIGS.  1  and  2    with various cloud tunnels, labeled as cloud tunnels, for forwarding traffic. 
         FIGS.  7  and  8    are flow diagrams of a cloud tunnel illustrating a control channel ( FIG.  7   ) and a data channel ( FIG.  8   ), with the tunnel illustrated between a client and a server. 
         FIG.  9    is a diagram illustrating various techniques to forward traffic to the cloud-based system. 
         FIG.  10    is a diagram illustrating signatures vs. behavior vs. deception. 
         FIG.  11    is a network diagram of a deception system with endpoint agents such as the application and with appliances in an enterprise network. 
         FIG.  12    is a network diagram of a deception system utilizing the cloud-based system in lieu of on-premises appliances. 
         FIG.  13    is a flowchart of a cloud-based deception process. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     Again, the present disclosure relates to systems and methods for cloud-based deception technology to identify threat intelligence, telemetry, and emerging adversary tactics and techniques. Variously, the present disclosure includes integration of deception technology with a cloud-based security system, namely without on-premises appliances. Further, the present disclosure includes targeted threat detection where activity is logged only related to breadcrumbs (fake data intentionally put on the user device). Also, the present disclosure includes dynamic risk scoring where all hits on a breadcrumb/honeypot are malicious by definition—this risk scoring covers a unique way to convey the actual threat. The present disclosure includes an approach to rapidly deploy breadcrumbs/honeypots to make each one look unique and look like a customer environment, so the attackers do not know. Even further, the present disclosure enables the breadcrumbs/honeypots based on user type, e.g., sales, marketing, legal, R&amp;D, etc. 
     § 1.0 Example Cloud-Based System Architecture 
       FIG.  1    is a network diagram of a cloud-based system  100  offering security as a service. Specifically, the cloud-based system  100  can offer a Secure Internet and Web Gateway as a service to various users  102 , as well as other cloud services. In this manner, the cloud-based system  100  is located between the users  102  and the Internet as well as any cloud services  106  (or applications) accessed by the users  102 . As such, the cloud-based system  100  provides inline monitoring inspecting traffic between the users  102 , the Internet  104 , and the cloud services  106 , including Secure Sockets Layer (SSL) traffic. The cloud-based system  100  can offer access control, threat prevention, data protection, etc. The access control can include a cloud-based firewall, cloud-based intrusion detection, Uniform Resource Locator (URL) filtering, bandwidth control, Domain Name System (DNS) filtering, etc. The threat prevention can include cloud-based intrusion prevention, protection against advanced threats (malware, spam, Cross-Site Scripting (XSS), phishing, etc.), cloud-based sandbox, antivirus, DNS security, etc. The data protection can include Data Loss Prevention (DLP), cloud application security such as via a Cloud Access Security Broker (CASB), file type control, etc. 
     The cloud-based firewall can provide Deep Packet Inspection (DPI) and access controls across various ports and protocols as well as being application and user aware. The URL filtering can block, allow, or limit website access based on policy for a user, group of users, or entire organization, including specific destinations or categories of URLs (e.g., gambling, social media, etc.). The bandwidth control can enforce bandwidth policies and prioritize critical applications such as relative to recreational traffic. DNS filtering can control and block DNS requests against known and malicious destinations. 
     The cloud-based intrusion prevention and advanced threat protection can deliver full threat protection against malicious content such as browser exploits, scripts, identified botnets and malware callbacks, etc. The cloud-based sandbox can block zero-day exploits (just identified) by analyzing unknown files for malicious behavior. Advantageously, the cloud-based system  100  is multi-tenant and can service a large volume of the users  102 . As such, newly discovered threats can be promulgated throughout the cloud-based system  100  for all tenants practically instantaneously. The antivirus protection can include antivirus, antispyware, antimalware, etc. protection for the users  102 , using signatures sourced and constantly updated. The DNS security can identify and route command-and-control connections to threat detection engines for full content inspection. 
     The DLP can use standard and/or custom dictionaries to continuously monitor the users  102 , including compressed and/or SSL-encrypted traffic. Again, being in a cloud implementation, the cloud-based system  100  can scale this monitoring with near-zero latency on the users  102 . The cloud application security can include CASB functionality to discover and control user access to known and unknown cloud services  106 . The file type controls enable true file type control by the user, location, destination, etc. to determine which files are allowed or not. 
     The cloud-based system  100  can provide other security functions, including, for example, micro-segmentation, workload segmentation, API security, Cloud Security Posture Management (CSPM), user identity management, and the like. That is, the cloud-based system  100  provides a network architecture that enables delivery of any cloud-based security service, including emerging frameworks. 
     For illustration purposes, the users  102  of the cloud-based system  100  can include a mobile device  110 , a headquarters (HQ)  112  which can include or connect to a data center (DC)  114 , Internet of Things (IoT) devices  116 , a branch office/remote location  118 , etc., and each includes one or more user devices (an example user device  300  is illustrated in  FIG.  5   ). The devices  110 ,  116 , and the locations  112 ,  114 ,  118  are shown for illustrative purposes, and those skilled in the art will recognize there are various access scenarios and other users  102  for the cloud-based system  100 , all of which are contemplated herein. The users  102  can be associated with a tenant, which may include an enterprise, a corporation, an organization, etc. That is, a tenant is a group of users who share a common access with specific privileges to the cloud-based system  100 , a cloud service, etc. In an embodiment, the headquarters  112  can include an enterprise&#39;s network with resources in the data center  114 . The mobile device  110  can be a so-called road warrior, i.e., users that are off-site, on-the-road, etc. Those skilled in the art will recognize a user  102  has to use a corresponding user device  300  for accessing the cloud-based system  100  and the like, and the description herein may use the user  102  and/or the user device  300  interchangeably. 
     Further, the cloud-based system  100  can be multi-tenant, with each tenant having its own users  102  and configuration, policy, rules, etc. One advantage of the multi-tenancy and a large volume of users is the zero-day/zero-hour protection in that a new vulnerability can be detected and then instantly remediated across the entire cloud-based system  100 . The same applies to policy, rule, configuration, etc. changes—they are instantly remediated across the entire cloud-based system  100 . As well, new features in the cloud-based system  100  can also be rolled up simultaneously across the user base, as opposed to selective and time-consuming upgrades on every device at the locations  112 ,  114 ,  118 , and the devices  110 ,  116 . 
     Logically, the cloud-based system  100  can be viewed as an overlay network between users (at the locations  112 ,  114 ,  118 , and the devices  110 ,  116 ) and the Internet  104  and the cloud services  106 . Previously, the IT deployment model included enterprise resources and applications stored within the data center  114  (i.e., physical devices) behind a firewall (perimeter), accessible by employees, partners, contractors, etc. on-site or remote via Virtual Private Networks (VPNs), etc. The cloud-based system  100  is replacing the conventional deployment model. The cloud-based system  100  can be used to implement these services in the cloud without requiring the physical devices and management thereof by enterprise IT administrators. As an ever-present overlay network, the cloud-based system  100  can provide the same functions as the physical devices and/or appliances regardless of geography or location of the users  102 , as well as independent of platform, operating system, network access technique, network access provider, etc. The cloud-based system  100  can be viewed as providing Zero Trust Network Access (ZTNA). 
     There are various techniques to forward traffic between the users  102  at the locations  112 ,  114 ,  118 , and via the devices  110 ,  116 , and the cloud-based system  100 . Typically, the locations  112 ,  114 ,  118  can use tunneling where all traffic is forward through the cloud-based system  100 . For example, various tunneling protocols are contemplated, such as GRE, L2TP, IPsec, customized tunneling protocols, etc. The devices  110 ,  116 , when not at one of the locations  112 ,  114 ,  118  can use a local application that forwards traffic, a proxy such as via a Proxy Auto-Config (PAC) file, and the like. An application of the local application is the application  350  described in detail herein as a connector application. A key aspect of the cloud-based system  100  is all traffic between the users  102  and the Internet  104  or the cloud services  106  is via the cloud-based system  100 . As such, the cloud-based system  100  has visibility to enable various functions, all of which are performed off the user device in the cloud. 
     The cloud-based system  100  can also include a management system  120  for tenant access to provide global policy and configuration as well as real-time analytics. This enables IT administrators to have a unified view of user activity, threat intelligence, application usage, etc. For example, IT administrators can drill-down to a per-user level to understand events and correlate threats, to identify compromised devices, to have application visibility, and the like. The cloud-based system  100  can further include connectivity to an Identity Provider (IDP)  122  for authentication of the users  102  and to a Security Information and Event Management (SIEM) system  124  for event logging. The system  124  can provide alert and activity logs on a per-user  102  basis. 
       FIG.  2    is a network diagram of an example implementation of the cloud-based system  100 . In an embodiment, the cloud-based system  100  includes a plurality of enforcement nodes (EN)  150 , labeled as enforcement nodes  150 - 1 ,  150 - 2 ,  150 -N, interconnected to one another and interconnected to a central authority (CA)  152 . Note, the nodes  150  are called “enforcement” nodes  150  but they can be simply referred to as nodes  150  in the cloud-based system  100 . Also, the nodes  150  can be referred to as service edges. The nodes  150  and the central authority  152 , while described as nodes, can include one or more servers, including physical servers, virtual machines (VM) executed on physical hardware, etc. An example of a server is illustrated in  FIG.  4   . The cloud-based system  100  further includes a log router  154  that connects to a storage cluster  156  for supporting log maintenance from the enforcement nodes  150 . The central authority  152  provide centralized policy, real-time threat updates, etc. and coordinates the distribution of this data between the enforcement nodes  150 . The enforcement nodes  150  provide an onramp to the users  102  and are configured to execute policy, based on the central authority  152 , for each user  102 . The enforcement nodes  150  can be geographically distributed, and the policy for each user  102  follows that user  102  as he or she connects to the nearest (or other criteria) enforcement node  150 . Of note, the cloud-based system is an external system meaning it is separate from tenant&#39;s private networks (enterprise networks) as well as from networks associated with the devices  110 ,  116 , and locations  112 ,  118 . 
     The enforcement nodes  150  are full-featured secure internet gateways that provide integrated internet security. They inspect all web traffic bi-directionally for malware and enforce security, compliance, and firewall policies, as described herein, as well as various additional functionality. In an embodiment, each enforcement node  150  has two main modules for inspecting traffic and applying policies: a web module and a firewall module. The enforcement nodes  150  are deployed around the world and can handle hundreds of thousands of concurrent users with millions of concurrent sessions. Because of this, regardless of where the users  102  are, they can access the Internet  104  from any device, and the enforcement nodes  150  protect the traffic and apply corporate policies. The enforcement nodes  150  can implement various inspection engines therein, and optionally, send sandboxing to another system. The enforcement nodes  150  include significant fault tolerance capabilities, such as deployment in active-active mode to ensure availability and redundancy as well as continuous monitoring. 
     In an embodiment, customer traffic is not passed to any other component within the cloud-based system  100 , and the enforcement nodes  150  can be configured never to store any data to disk. Packet data is held in memory for inspection and then, based on policy, is either forwarded or dropped. Log data generated for every transaction is compressed, tokenized, and exported over secure Transport Layer Security (TLS) connections to the log routers  154  that direct the logs to the storage cluster  156 , hosted in the appropriate geographical region, for each organization. In an embodiment, all data destined for or received from the Internet is processed through one of the enforcement nodes  150 . In another embodiment, specific data specified by each tenant, e.g., only email, only executable files, etc., is processed through one of the enforcement nodes  150 . 
     Each of the enforcement nodes  150  may generate a decision vector D=[d 1 , d 2 , . . . , dn] for a content item of one or more parts C=[c 1 , c 2 , . . . , cm]. Each decision vector may identify a threat classification, e.g., clean, spyware, malware, undesirable content, innocuous, spam email, unknown, etc. For example, the output of each element of the decision vector D may be based on the output of one or more data inspection engines. In an embodiment, the threat classification may be reduced to a subset of categories, e.g., violating, non-violating, neutral, unknown. Based on the subset classification, the enforcement node  150  may allow the distribution of the content item, preclude distribution of the content item, allow distribution of the content item after a cleaning process, or perform threat detection on the content item. In an embodiment, the actions taken by one of the enforcement nodes  150  may be determinative on the threat classification of the content item and on a security policy of the tenant to which the content item is being sent from or from which the content item is being requested by. A content item is violating if, for any part C=[c 1 , c 2 , . . . , cm] of the content item, at any of the enforcement nodes  150 , any one of the data inspection engines generates an output that results in a classification of “violating.” 
     The central authority  152  hosts all customer (tenant) policy and configuration settings. It monitors the cloud and provides a central location for software and database updates and threat intelligence. Given the multi-tenant architecture, the central authority  152  is redundant and backed up in multiple different data centers. The enforcement nodes  150  establish persistent connections to the central authority  152  to download all policy configurations. When a new user connects to an enforcement node  150 , a policy request is sent to the central authority  152  through this connection. The central authority  152  then calculates the policies that apply to that user  102  and sends the policy to the enforcement node  150  as a highly compressed bitmap. 
     The policy can be tenant-specific and can include access privileges for users, websites and/or content that is disallowed, restricted domains, DLP dictionaries, etc. Once downloaded, a tenant&#39;s policy is cached until a policy change is made in the management system  120 . The policy can be tenant-specific and can include access privileges for users, websites and/or content that is disallowed, restricted domains, DLP dictionaries, etc. When this happens, all of the cached policies are purged, and the enforcement nodes  150  request the new policy when the user  102  next makes a request. In an embodiment, the enforcement node  150  exchange “heartbeats” periodically, so all enforcement nodes  150  are informed when there is a policy change. Any enforcement node  150  can then pull the change in policy when it sees a new request. 
     The cloud-based system  100  can be a private cloud, a public cloud, a combination of a private cloud and a public cloud (hybrid cloud), or the like. Cloud computing systems and methods abstract away physical servers, storage, networking, etc., and instead offer these as on-demand and elastic resources. The National Institute of Standards and Technology (NIST) provides a concise and specific definition which states cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing differs from the classic client-server model by providing applications from a server that are executed and managed by a client&#39;s web browser or the like, with no installed client version of an application required. Centralization gives cloud service providers complete control over the versions of the browser-based and other applications provided to clients, which removes the need for version upgrades or license management on individual client computing devices. The phrase “Software as a Service” (SaaS) is sometimes used to describe application programs offered through cloud computing. A common shorthand for a provided cloud computing service (or even an aggregation of all existing cloud services) is “the cloud.” The cloud-based system  100  is illustrated herein as an example embodiment of a cloud-based system, and other implementations are also contemplated. 
     As described herein, the terms cloud services and cloud applications may be used interchangeably. The cloud service  106  is any service made available to users on-demand via the Internet, as opposed to being provided from a company&#39;s on-premises servers. A cloud application, or cloud app, is a software program where cloud-based and local components work together. The cloud-based system  100  can be utilized to provide example cloud services, including Zscaler Internet Access (ZIA), Zscaler Private Access (ZPA), and Zscaler Digital Experience (ZDX), all from Zscaler, Inc. (the assignee and applicant of the present application). Also, there can be multiple different cloud-based systems  100 , including ones with different architectures and multiple cloud services. The ZIA service can provide the access control, threat prevention, and data protection described above with reference to the cloud-based system  100 . ZPA can include access control, microservice segmentation, etc. The ZDX service can provide monitoring of user experience, e.g., Quality of Experience (QoE), Quality of Service (QoS), etc., in a manner that can gain insights based on continuous, inline monitoring. For example, the ZIA service can provide a user with Internet Access, and the ZPA service can provide a user with access to enterprise resources instead of traditional Virtual Private Networks (VPNs), namely ZPA provides Zero Trust Network Access (ZTNA). Those of ordinary skill in the art will recognize various other types of cloud services  106  are also contemplated. Also, other types of cloud architectures are also contemplated, with the cloud-based system  100  presented for illustration purposes. 
     § 1.1 Private Nodes Hosted by Tenants or Service Providers 
     The nodes  150  that service multi-tenant users  102  may be located in data centers. These nodes  150  can be referred to as public nodes  150  or public service edges. In embodiment, the nodes  150  can be located on-premises with tenants (enterprise) as well as service providers. These nodes can be referred to as private nodes  150  or private service edges. In operation, these private nodes  150  can perform the same functions as the public nodes  150 , can communicate with the central authority  152 , and the like. In fact, the private nodes  150  can be considered in the same cloud-based system  100  as the public nodes  150 , except located on-premises. When a private node  150  is located in an enterprise network, the private node  150  can be single tenant for the corresponding enterprise; of course, the cloud-based system  100  is still multi-tenant, but these particular nodes are serving only a single tenant. When a private node  150  is located in a service provider&#39;s network, the private node  150  can be multi-tenant for customers of the service provider. Those skilled in the are will recognize various architectural approaches are contemplated. The cloud-based system  100  is a logical construct providing a security service. 
     § 2.0 User Device Application for Traffic Forwarding and Monitoring 
       FIG.  3    is a network diagram of the cloud-based system  100  illustrating an application  350  on user devices  300  with users  102  configured to operate through the cloud-based system  100 . Different types of user devices  300  are proliferating, including Bring Your Own Device (BYOD) as well as IT-managed devices. The conventional approach for a user device  300  to operate with the cloud-based system  100  as well as for accessing enterprise resources includes complex policies, VPNs, poor user experience, etc. The application  350  can automatically forward user traffic with the cloud-based system  100  as well as ensuring that security and access policies are enforced, regardless of device, location, operating system, or application. The application  350  automatically determines if a user  102  is looking to access the open Internet  104 , a SaaS app, or an internal app running in public, private, or the datacenter and routes mobile traffic through the cloud-based system  100 . The application  350  can support various cloud services, including ZIA, ZPA, ZDX, etc., allowing the best-in-class security with zero trust access to internal apps. As described herein, the application  350  can also be referred to as a connector application. 
     The application  350  is configured to auto-route traffic for seamless user experience. This can be protocol as well as application-specific, and the application  350  can route traffic with a nearest or best fit enforcement node  150 . Further, the application  350  can detect trusted networks, allowed applications, etc. and support secure network access. The application  350  can also support the enrollment of the user device  300  prior to accessing applications. The application  350  can uniquely detect the users  102  based on fingerprinting the user device  300 , using criteria like device model, platform, operating system, etc. The application  350  can support Mobile Device Management (MDM) functions, allowing IT personnel to deploy and manage the user devices  300  seamlessly. This can also include the automatic installation of client and SSL certificates during enrollment. Finally, the application  350  provides visibility into device and app usage of the user  102  of the user device  300 . 
     The application  350  supports a secure, lightweight tunnel between the user device  300  and the cloud-based system  100 . For example, the lightweight tunnel can be HTTP-based. With the application  350 , there is no requirement for PAC files, an IPsec VPN, authentication cookies, or user  102  setup. 
     § 3.0 Example Server Architecture 
       FIG.  4    is a block diagram of a server  200 , which may be used in the cloud-based system  100 , in other systems, or standalone. For example, the enforcement nodes  150  and the central authority  152  may be formed as one or more of the servers  200 . The server  200  may be a digital computer that, in terms of hardware architecture, generally includes a processor  202 , input/output (I/O) interfaces  204 , a network interface  206 , a data store  208 , and memory  210 . It should be appreciated by those of ordinary skill in the art that  FIG.  4    depicts the server  200  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 202 ,  204 ,  206 ,  208 , and  210 ) are communicatively coupled via a local interface  212 . The local interface  212  may be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  212  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  212  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  202  is a hardware device for executing software instructions. The processor  202  may be any custom made or commercially available processor, a Central Processing Unit (CPU), an auxiliary processor among several processors associated with the server  200 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the server  200  is in operation, the processor  202  is configured to execute software stored within the memory  210 , to communicate data to and from the memory  210 , and to generally control operations of the server  200  pursuant to the software instructions. The I/O interfaces  204  may be used to receive user input from and/or for providing system output to one or more devices or components. 
     The network interface  206  may be used to enable the server  200  to communicate on a network, such as the Internet  104 . The network interface  206  may include, for example, an Ethernet card or adapter or a Wireless Local Area Network (WLAN) card or adapter. The network interface  206  may include address, control, and/or data connections to enable appropriate communications on the network. A data store  208  may be used to store data. The data store  208  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. 
     Moreover, the data store  208  may incorporate electronic, magnetic, optical, and/or other types of storage media. In one example, the data store  208  may be located internal to the server  200 , such as, for example, an internal hard drive connected to the local interface  212  in the server  200 . Additionally, in another embodiment, the data store  208  may be located external to the server  200  such as, for example, an external hard drive connected to the I/O interfaces  204  (e.g., SCSI or USB connection). In a further embodiment, the data store  208  may be connected to the server  200  through a network, such as, for example, a network-attached file server. 
     The memory  210  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory  210  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  210  may have a distributed architecture, where various components are situated remotely from one another but can be accessed by the processor  202 . The software in memory  210  may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory  210  includes a suitable Operating System (O/S)  214  and one or more programs  216 . The operating system  214  essentially controls the execution of other computer programs, such as the one or more programs  216 , and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The one or more programs  216  may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein. 
     § 4.0 Example User Device Architecture 
       FIG.  5    is a block diagram of a user device  300 , which may be used with the cloud-based system  100  or the like. Specifically, the user device  300  can form a device used by one of the users  102 , and this may include common devices such as laptops, smartphones, tablets, netbooks, personal digital assistants, MP3 players, cell phones, e-book readers, IoT devices, servers, desktops, printers, televisions, streaming media devices, and the like. The user device  300  can be a digital device that, in terms of hardware architecture, generally includes a processor  302 , I/O interfaces  304 , a network interface  306 , a data store  308 , and memory  310 . It should be appreciated by those of ordinary skill in the art that  FIG.  5    depicts the user device  300  in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components ( 302 ,  304 ,  306 ,  308 , and  302 ) are communicatively coupled via a local interface  312 . The local interface  312  can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface  312  can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface  312  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     The processor  302  is a hardware device for executing software instructions. The processor  302  can be any custom made or commercially available processor, a CPU, an auxiliary processor among several processors associated with the user device  300 , a semiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. When the user device  300  is in operation, the processor  302  is configured to execute software stored within the memory  310 , to communicate data to and from the memory  310 , and to generally control operations of the user device  300  pursuant to the software instructions. In an embodiment, the processor  302  may include a mobile optimized processor such as optimized for power consumption and mobile applications. The I/O interfaces  304  can be used to receive user input from and/or for providing system output. User input can be provided via, for example, a keypad, a touch screen, a scroll ball, a scroll bar, buttons, a barcode scanner, and the like. System output can be provided via a display device such as a Liquid Crystal Display (LCD), touch screen, and the like. 
     The network interface  306  enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the network interface  306 , including any protocols for wireless communication. The data store  308  may be used to store data. The data store  308  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store  308  may incorporate electronic, magnetic, optical, and/or other types of storage media. 
     The memory  310  may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory  310  may incorporate electronic, magnetic, optical, and/or other types of storage media. Note that the memory  310  may have a distributed architecture, where various components are situated remotely from one another but can be accessed by the processor  302 . The software in memory  310  can include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of  FIG.  3   , the software in the memory  310  includes a suitable operating system  314  and programs  316 . The operating system  314  essentially controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The programs  316  may include various applications, add-ons, etc. configured to provide end user functionality with the user device  300 . For example, example programs  316  may include, but not limited to, a web browser, social networking applications, streaming media applications, games, mapping and location applications, electronic mail applications, financial applications, and the like. In a typical example, the end-user typically uses one or more of the programs  316  along with a network such as the cloud-based system  100 . 
     § 5.0 Cloud Tunnel 
       FIG.  6    is a network diagram of the cloud-based system  100  with various cloud tunnels  500 , labeled as cloud tunnels  500 A,  500 B,  500 C, for forwarding traffic.  FIGS.  7  and  8    are flow diagrams of a cloud tunnel  500  illustrating a control channel ( FIG.  7   ) and a data channel ( FIG.  8   ), with the tunnel illustrated between a client  510  and a server  520 . The cloud tunnel  500  is a lightweight tunnel that is configured to forward traffic between the client  510  and the server  520 . The present disclosure focuses on the specific mechanisms used in the cloud tunnel  500  between two points, namely the client  510  and the server  520 . Those skilled in the art will recognize the cloud tunnel  500  can be used with the cloud-based system  100  as an example use case, and other uses are contemplated. That is, the client  510  and the server  520  are just endpoint devices that support the exchange of data traffic and control traffic for the tunnel  500 . For description, the server  520  can be referred to as a local node and the client  510  as a remote node, where the tunnel operates between the local and remote nodes. 
     In an embodiment, the cloud-based system  100  can use the cloud tunnel  500  to forward traffic to the enforcement nodes  150 , such as from a user device  300  with the application  350 , from a branch office/remote location  118 , etc.  FIG.  6    illustrates three example use cases for the cloud tunnel  500  with the cloud-based system  100 , and other uses are also contemplated. In a first use case, a cloud tunnel  500 A is formed between a user device  300 , such as with the application  350 , and an enforcement node  150 - 1 . For example, when a user  102  associated with the user device  300  connects to a network, the application  350  can establish the cloud tunnel  500 A to the closest or best enforcement node  150 - 1  and forward the traffic through the cloud tunnel  500 A so that the enforcement node  150 - 1  can apply the appropriate security and access policies. Here, the cloud tunnel  500 A supports a single user  102 , associated with the user device  300 . 
     In a second use case, a cloud tunnel  500 B is formed between a Virtual Network Function (VNF)  502  or some other device at a remote location  118 A and an enforcement node  150 - 2 . Here, the VNF  502  is used to forward traffic from any user  102  at the remote location  118 A to the enforcement node  150 - 2 . In a third use case, a cloud tunnel  110 C is formed between an on-premises enforcement node, referred to as an Edge Connector (EC)  150 A, and an enforcement node  150 -N. The edge connector  150 A can be located at a branch office  118 A or the like. In some embodiments, the edge connector  150 A can be an enforcement node  150  in the cloud-based system  100  but located on-premises with a tenant. Here, in the second and third use cases, the cloud tunnels  500 B,  500 C support multiple users  102 . 
     There can be two versions of the cloud tunnel  500 , referred to a tunnel  1  and tunnel  2 . The tunnel  1  can only support Web protocols as an HTTP connect tunnel operating on a Transmission Control Protocol (TCP) streams. That is, the tunnel  1  can send all proxy-aware traffic or port 80/443 traffic to the enforcement node  150 , depending on the forwarding profile configuration. This can be performed via CONNECT requests, similar to a traditional proxy. 
     The tunnel  2  can support multiple ports and protocols, extending beyond only web protocols. As described herein, the cloud tunnels  500  are the tunnel  2 . In all of the use cases, the cloud tunnel  500  enables each user device  300  to redirect traffic destined to all ports and protocols to a corresponding enforcement node  150 . Note, the cloud-based system  100  can include load balancing functionality to spread the cloud tunnels  500  from a single source IP address. The cloud tunnel  500  supports device logging for all traffic, firewall, etc., such as in the storage cluster  156 . The cloud tunnel  500  utilizes encryption, such as via TLS or Datagram TLS (DTLS), to tunnel packets between the two points, namely the client  510  and the server  520 . As described herein, the client  510  can be the user device  300 , the VNF  502 , and/or the edge connector  150 A, and the server  520  can be the enforcement node  150 . Again, other devices are contemplated with the cloud tunnel  500 . 
     The cloud tunnel  500  can use a Network Address Translation (NAT) device that does not require a different egress IP for each device&#39;s  300  separate sessions. Again, the cloud tunnel  500  has a tunneling architecture that uses DTLS or TLS to send packets to the cloud-based system  100 . Because of this, the cloud tunnel  500  is capable of sending traffic from all ports and protocols. 
     Thus, the cloud tunnel  500  provides complete protection for a single user  102 , via the application  350 , as well as for multiple users at remote locations  118 , including multiple security functions such as cloud firewall, cloud IPS, etc. The cloud tunnel  500  includes user-level granularity of the traffic, enabling different users  102  on the same cloud tunnel  500  for the enforcement nodes  150  to provide user-based granular policy and visibility. In addition to user-level granularity, the cloud tunnel  500  can provide application-level granularity, such as by mapping mobile applications (e.g., Facebook, Gmail, etc.) to traffic, allowing for app-based granular policies. 
       FIGS.  7  and  8    illustrate the two communication channels, namely a control channel  530  and a data channel  540 , between the client  510  and the server  520 . Together, these two communication channels  530 ,  540  form the cloud tunnel  500 . In an embodiment, the control channel  530  can be an encrypted TLS connection or SSL connection, and the control channel  530  is used for device and/or user authentication and other control messages. In an embodiment, the data channel  540  can be an encrypted DTLS or TLS connection, i.e., the data channel can be one or more DTLS or TLS connections for the transmit and receive of user IP packets. There can be multiple data channels  540  associated with the same control channel  530 . The data channel  540  can be authenticated using a Session Identifier (ID) from the control channel  530 . 
     Of note, the control channel  530  always uses TLS because some locations (e.g., the remote location  118 A, the branch office  118 B, other enterprises, hotspots, etc.) can block UDP port 443, preventing DTLS. Whereas TLS is widely used and not typically blocked. The data channel  540  preferably uses DTLS, if it is available, i.e., not blocked on the client  510 . If it is blocked, the data channel  540  can use TLS instead. For example, DTLS is the primary protocol for the data channel  540  with TLS used as a fallback over TCP port 443 if DTLS is unavailable, namely if UDP port 443 is blocked at the client  510 . 
     In  FIG.  7   , the control channel  530  is illustrated with exchanges between the client  510  and the server  520 . Again, the control channel  530  includes TLS encryption, which is established through a setup or handshake between the client  510  and the server  520  (step  550 - 1 ). The client  510  can send its version of the tunnel  500  to the server  520  (step  550 - 2 ) to which the server  520  can acknowledge (step  550 - 3 ). For example, the version of the tunnel can include a simple version number or other indication, as well as an indication of whether the client  510  supports DTLS for the data channel  540 . Again, the control channel  530  is fixed with TLS or SSL, but the data channel  540  can be either DTLS or TLS. 
     The client  510  can perform device authentication (step  550 - 4 ), and the server  520  can acknowledge the device authentication (step  550 - 5 ). The client  510  can perform user authentication (step  550 - 6 ), and the server  520  can acknowledge the user authentication (step  550 - 7 ). Note, the device authentication includes authenticating the user device  300 , such as via the application  350 , the VNF  502 , the edge connector  150 A, etc. The user authentication includes authenticating the users  102  associated with the user devices  300 . Note, in an embodiment, the client  510  is the sole device  300 , and here the user authentication can be for the user  102  associated with the client  510 , and the device authentication can be for the user device  300  with the application  350 . In another embodiment, the client  510  can have multiple user devices  300  and corresponding users  102  associated with it. Here, the device authentication can be for the VNF  502 , the edge connector  150 A, etc., and the user authentication can be for each user device  300  and corresponding user  102 , and the client  510  and the server  520  can have a unique identifier for each user device  300 , for user-level identification. 
     The device authentication acknowledgment can include a session identifier (ID) that is used to bind the control channel  530  with one or more data channels  540 . The user authentication can be based on a user identifier (ID) that is unique to each user  102 . The client  510  can periodically provide keep alive packets (step  550 - 8 ), and the server  520  can respond with keep alive acknowledgment packets (step  550 - 9 ). The client  510  and the server  520  can use the keep alive packets or messages to maintain the control channel  530 . Also, the client  510  and the server  520  can exchange other relevant data over the control channel  530 , such as metadata, which identifies an application for a user  102 , location information for a user device  300 , etc. 
     In  FIG.  8   , similar to  FIG.  7   , the data channel  540  is illustrated with exchanges between the client  510  and the server  520 . Again, the data channel  540  includes TLS or DTLS encryption, which is established through a setup or handshake between the client  510  and the server  520  (step  560 - 1 ). An example of a handshake is illustrated in  FIG.  11   . Note, the determination of whether to use TLS or DTLS is based on the session ID, which is part of the device authentication acknowledgment, and which is provided over the data channel  540  (steps  560 - 2 ,  560 - 3 ). Here, the client  510  has told the server  520  its capabilities, and the session ID reflects what the server  520  has chosen, namely TLS or DTLS, based on the client&#39;s  510  capabilities. In an embodiment, the server  520  chooses DTLS if the client  510  supports it, i.e., if UDP port 443 is not blocked, otherwise the server  520  chooses TLS. Accordingly, the control channel  530  is established before the data channel  540 . The data channel  540  can be authenticated based on the session ID from the control channel  530 . p The data channel  540  includes the exchange of data packets between the client  510  and the server  520  (step  560 - 4 ). The data packets include an identifier such as the session ID and a user ID for the associated user  102 . Additionally, the data channel  540  can include keep alive packets between the client  510  and the server  520  (steps  560 - 5 ,  560 - 6 ). p The cloud tunnel  500  can support load balancing functionality between the client  510  and the server  520 . The server  520  can be in a cluster, i.e., multiple servers  200 . For example, the server  520  can be an enforcement node  150  cluster in the cloud-based system  100 . Because there can be multiple data channels  540  for a single control channel  530 , it is possible to have the multiple data channels  540 , in a single cloud tunnel  500 , connected to different physical servers  200  in a cluster. Thus, the cloud-based system  100  can include load balancing functionality to spread the cloud tunnels  500  from a single source IP address, i.e., the client  510 . 
     Also, the use of DTLS for the data channels  540  allows the user devices  300  to switch networks without potentially impacting the traffic going through the tunnel  500 . For example, a large file download could continue uninterrupted when a user device  300  moves from Wi-Fi to mobile, etc. Here, the application  350  can add some proprietary data to the DTLS client-hello server name extension. That proprietary data helps a load balancer balance the new DTLS connection to the same server  200  in a cluster where the connection prior to network change was being processed. So, a newly established DTLS connection with different IP address (due to network change) can be used to tunnel packets of the large file download that was started before the network change. Also, some mobile carriers use different IP addresses for TCP/TLS (control channel) and UDP/DTLS (data channel) flows. The data in DTLS client-hello helps the load balancer balance the control and data connection to the same server  200  in the cluster. 
     § 6.0 Cloud Connectivity 
       FIG.  9    is a diagram illustrating various techniques to forward traffic to the cloud-based system  100 . These include, for example, use of the application  350  as a client connector for forwarding traffic, use of the connector  400  app, use of the VNF  502  or some other device, use of the edge connector  150 A, and use of an eSIM/iSIM/SIM-card  600 . The application  350  can be referred to as a client connector and it is via a native application executed on the user device  300  as well as being user ID-based. The connector  400  can be referred to as an app connector. The edge connector  150 A can be referred to as a private service edge. 
     There is a requirement to get any customer traffic to/from the cloud-based system  100 . However, there is a gap on some devices. The current approach, e.g., with the application  350 , the connector  400 , etc. there is a reliance on the device, namely installation of a forwarding app, a reliance on an operating system, namely virtual interfaces, and a reliance on forwarding gateways, namely the edge connector  150 A. However, these may not be available with other types of devices such as IoT devices and the like. As described herein, the present disclosure utilizes the term client device to include, without limitations IoT devices (e.g., smart scooters, etc.), Operational Technology (OT) platforms (e.g., Supervisory Control and Data Acquisition (SCADA) systems, Industrial Control Systems (ICS), etc.), medical equipment (e.g., CAT and MRI scanners, etc.), connected vehicles, and practically any device that has a Subscriber Identification Module (SIM) in the form of a card, an eSIM, or an iSIM. Those skilled in the art will recognize that a client device differs from the user device  300  as it may not have the ability to implement the application  350 , not support a user ID for identifying the user  102 , etc. 
     The present disclosure includes two additional techniques for cloud connectivity for IoT devices including an eSIM/iSIM/SIM-card  600  based approach and a cloud/branch/thing connector  604 . The ESIM/iSIM/SIM-card  600  based approach can be referred to as a device connector. The ESIM/iSIM/SIM-card  600  based approach is used for forwarding traffic from any SIM-based device (e.g., 2G to 5G and beyond). The key here is identity is based on the ESIM/iSIM/SIM-card  600 , namely the International Mobile Equipment Identity (IMEI), as opposed to a user ID. 
     The connection between the cloud-based system  100  and on-premises connector  400  is dynamic, on-demand, and orchestrated by the cloud-based system  100 . A key feature is its security at the edge—there is no need to punch any holes in the existing on-premises firewall. The connector  400  inside the enterprise or the like (on-premises) “dials out” and connects to the cloud-based system  100  as if too were an endpoint. This on-demand dial-out capability and tunneling authenticated traffic back to the enterprise is a key differentiator for ZTNA. The app connector  400  is used for virtual private access. The paradigm of virtual private access systems and methods is to give users network access to get to an application and/or file share, not to the entire network. If a user is not authorized to get the application, the user should not be able even to see that it exists, much less access it. The virtual private access systems and methods provide an approach to deliver secure access by decoupling applications  402 ,  404  from the network, instead of providing access with a connector  400 , in front of the applications  402 ,  404 , an application on the user device  300 , a central authority  152  to push policy, and the cloud-based system  100  to stitch the applications  402 ,  404  and the software connectors  400  together, on a per-user, per-application basis. 
     With the virtual private access, users can only see the specific applications  402 ,  404  allowed by the central authority  152 . Everything else is “invisible” or “dark” to them. Because the virtual private access separates the application from the network, the physical location of the application  402 ,  404  becomes irrelevant—if applications  402 ,  404  are located in more than one place, the user is automatically directed to the instance that will give them the best performance. The virtual private access also dramatically reduces configuration complexity, such as policies/firewalls in the data centers. Enterprises can, for example, move applications to Amazon Web Services or Microsoft Azure, and take advantage of the elasticity of the cloud, making private, internal applications behave just like the marketing leading enterprise applications. Advantageously, there is no hardware to buy or deploy because the virtual private access is a service offering to end-users and enterprises. 
     § 7.0 Deception Technology 
     As attacks become more advanced, deception technology is becoming critical for early threat detection. Deception technology is a simple but effective approach to building security defenses that detect threats early with low false positives and minimal performance impact on the network. The technology works by creating decoys—realistic-but-fake assets (domains, databases, servers, applications, files, credentials, cookies, sessions, network traffic and more) that are deployed in an enterprise IT environment alongside legitimate assets. For an attacker who has breached the network, there is no way to differentiate the fake from the real. The moment they interact with a decoy, a silent alarm is raised while the systems collect information on the attacker&#39;s actions and intent, As described herein, the term “decoy” can be used interchangeably with breadcrumbs, honeypots, fake assets, lures, honeytokes, bait etc. 
     Modern-day deception technology-based cybersecurity defenses borrow heavily from proven military deception use of deceit, camouflage, and subterfuge. In the context of cybersecurity, defenders use decoys and lures to mislead attackers into believing that they have a foothold in the network and revealing themselves. The beauty of this approach lies in its simplicity. 
     Picture two adversaries with comparable capabilities but differing goals facing off against each other. The odds of either of them winning are usually at a coin toss. What if one of the two had the benefit of speed, flexibility, and targeted information? It is no contest. The speedier, more nimble of the adversaries will usually outcompete their opponent. Even so, the latter has greater strength and access to more information. Because the opponent with more information falls into ‘analysis paralysis,’ They have so much information that analyzing all of it becomes infeasible and some information must be prioritized over the rest. This is how adversaries lacking in strength usually win an adversarial contest. 
     Something very similar happens in cybersecurity. Consider for a minute how things have played out over the past couple of decades. Attacker: Let&#39;s play a game with some simple rules. I&#39;ll try to access your information or other resources. If I&#39;m successful once. I win, Defender: Sounds fair. Attacker: If you prevent me from being able to reach my target at any time including otherwise rough or busy days, after-hours, on weekends and holidays . . . you win. This is a rigged game. As a defender, you need to be right 100% of the time. The attacker just has to be right once. Thankfully, deception flips the table by placing the burden of success on the attackers instead. Once you populate your network with decoys, adversaries need to carry out a flawless attack without interacting with any deceptive assets, triggering any detection controls, or prompting other defensive actions, to succeed in their attack. In other words, the attacker now needs to be right 100% of the time, while a single mistake hands the defenders a win. 
     Deception technology is attack-vector-agnostic. It only ever looks at the intent of the adversary to detect attacks. No legitimate user has any business accessing a decoy system, file or application. Therefore, by design, any interaction with a decoy is suspicious at a minimum and malicious at worst. Since deception-based defenses do not depend on signatures or heuristics for detection, they are able to detect virtually any attack including APTs, zero-days, reconnaissance, lateral movement, malwareless attacks, social engineering, man-in-the-middle attacks, and ransomware in real-time. Decoys are essentially zero trust, they assume all interaction is by default malicious or suspicious. 
     Against the vast majority of adversaries, deception is extremely effective because it leverages the fact that they do not know everything about the network they are trying to move around. Since decoys are essentially just another asset in the network and their strength is functional and not technical, differentiating them from legitimate assets is virtually impossible. Further, by injecting fake records in a user&#39;s browser history, password manager, and other common points of internal reconnaissance, the attacker can be deceived about functional uses of decoys, making them appear like they are either regularly-used assets—indicating they are likely useful and can be used by the attacker to move laterally to what appears to be a valuable target—or an infrequently-used but valuable asset—indicating they can be used to access privileged and/or critical information. 
     It is assumed that deception is a capability deployed only by highly mature security organizations. Deception is becoming a mainstream capability across markets. The mid-market CISO is faced with a conundrum—Her security team and budget are small, but she still has a significant threat and risk perception (perhaps she&#39;s in an industry without compliance regulations driving investment). She&#39;s got some basic security hygiene in place but needs to do something to detect more serious threats. Her wish list from a solution is: i) Fast to get going—a quick win today v/s perfection tomorrow, ii) Easy to use and low maintenance given her small internal security team, iii) Not a point solution, as she can&#39;t invest in multiple technologies, and iv) Wide coverage for areas like cloud and IoT. Deception perfectly checks all the boxes above for her, letting her quickly start punching above her weight when it comes to more advanced, targeted threats. 
     § 7.1 Advantages of Deception Technology 
     In anti-submarine warfare, the sighting of a periscope breaking the water is an unambiguous indicator of an imminent threat. We believe deception alerts are very similar, and term them ‘periscope events’—a behavior that, when detected, clearly indicates that an attacker is in the network. If you place detection classes on a scale of accuracy, at one extreme you have: 
     i) Signature-based detection, which is highly accurate but very threat specific, (such as the propeller signature of a specific submarine). 
     ii) On the other extreme you have behaviors/heuristics which have broad threat coverage, but more prone to false positives (such as a radar contact that may be a submarine or a shoal of fish). 
     Deception&#39;s periscope events are the middle ground—highly accurate, but with broad threat coverage (we can broadly detect any type of submarine with extremely low false positives).  FIG.  10    is a diagram illustrating signatures vs, behavior vs. deception. 
     Most security controls are not aware of current business risks (your antivirus does not know or care that you&#39;re going through an M&amp;A). However, deception is intrinsically aligned with the current business threat perception. For example, if a company is launching a new product, it can create deception around that product launch, aligning security controls tightly to areas where the organization perceives risk. 
     Since deception is a detection class, it can be applied broadly horizontally across the enterprise, including environments that are often neglected blind spots. For example, deception can detect threats at the perimeter, the endpoint, the network, Active Directory, and application layers, as well as offer coverage to more neglected environments such as SCADA/ICS, IoT, and cloud. Unlike point solutions, deception also covers the entire kill-chain; from pre-attack reconnaissance to exploitation, privilege escalation, lateral movement, and data-theft/destruction. 
     False positives cripple security team productivity and drag both IT and security teams through convoluted triage workflows. Often, the process of trying to validate the alert is more time consuming than the actual remediation measure. Deception has an intrinsic low false-positive property—nobody should open a decoy file, log in to a decoy application, use a decoy credential, or scan a decoy server. However, the alerts are also far more contextual; giving insight into the attacker&#39;s intent (“they went for the R&amp;D information, not the financial systems”). 
     Most behavior-based systems try to establish a normal baseline and then classify any activity above the baseline as anomalous, this leads to a number of false positives. Deception establishes a zero-activity normal baseline, where any activity at all is worthy of investigation. It also gives detailed IOCs (indicators of compromise). Everything that happens on a decoy is considered evil; so analysts do not need to weed through the data to segregate legitimate user activity and forensically relevant artifacts. Not only can you detect more reliably, but you can ‘know your enemy’ far better. 
     You can also use deception to qualify medium or ‘warm’ alerts from other platforms. For example, a UEBA system may generate a medium risk score for a user&#39;s behavior, leaving the analyst in no man&#39;s land—“is this a threat, or a false positive?” Dynamic deployment of deception on and around that user&#39;s environment may result in a higher-order detection if the attack is real. 
     Ring-fencing potential problem areas in this manner is also exceptionally useful during incident response in environments where the available logging is limited, but a rapid increase in visibility is required. If the DMZ may be compromised, lay down deception and watch for privilege escalation or lateral movement detection while the root cause of the compromise is being investigated. This can help answer two of the most fundamental questions in incident response, “how far in did they get, and are they still in?”. 
     Orchestrated/automated response is most useful only when the trigger event is 100% certain. While plenty of orchestration tooling is being built (like shovels during the gold rush), not many real-world transformational orchestration use cases exist because there are very few alerts that are 100% certain. The ones that are, typically don&#39;t require orchestration, because the products that generate them already contain remedial capabilities (for example, an antivirus detection quarantining a file). 
     Deception alerts are highly certain, contextual, and real-time, affording opportunities for security teams to orchestrate more complex and invasive scenarios (for example, use of a decoy credential can result in automatic redirection of the compromised asset into a decoy environment, while disabling the logged-on user&#39;s account and access in the real environment). 
     In terms of containment/response use cases, deception alerts can integrate with: 
     Network Access Control—Quarantine a compromised asset 
     Web gateways—Disable the compromised asset&#39;s Internet access, block phishing sites identified by email decoys. 
     Endpoint protection—Kill a suspicious process or quarantine the endpoint 
     EDR—Identify and block IOCs on all other endpoints 
     Directory Services/Identity and Access Management—Disable the user&#39;s account, change a password, and enable/enforce two-factor authentication 
     Firewalls—Dynamically deny access to network segments 
     Zero trust systems as a potential containment mechanism 
     Since deception alerts are contextual, the response can also target the appropriate application. For example, if an attacker targets a decoy SWIFT server in a banking environment, the user&#39;s account can be disabled for the real SWIFT server. 
     § 7.2 Use Cases of Deception Technology 
     Deception can be used to detect threats across the kill-chain starting with reconnaissance going all the way up to data theft. Broadly, there can be three key use cases—1) perimeter deception defense, 2) network deception defense, 3) endpoint deception defense, ) Identity systems (for e.g., Active Directory) and 5) IoT/OT deception. 
     § 7.2.1 Perimeter Deception Defense 
     At a time when it&#39;s possible to scan all IPv4 IPs in under an hour, monitoring all inbound connections—even just the unusual ones—becomes like drinking from the proverbial fire hose. With VMs available in the cloud within minutes, the noise comes not just from suspicious visitors to a page or from adversaries targeting the organization, but from almost anyone that can create a cloud account and run a script or two. Security isn&#39;t a big data problem so much as a good data problem. 
     Setting up deceptive public-facing assets, if done right, can drastically simplify this problem and give you actionable telemetry on who&#39;s targeting you. This is different from simply setting up a traditional honeypot with a number of open ports on a public IP. Such a setup is going to generate noisy alerts as everything from Google and Shodan to scripts for college research projects trying to connect to these honeypots. Instead, deploying decoys that mimic beta/staging applications can create high-confidence alerts telling you that an attacker is attempting to reach specific public-facing (but unannounced) infrastructure. Irrespective of their reason and methodology for doing so—including scripts set up to search for such infrastructure belonging to specific organizations, the alerts produced become a high-confidence indicator of intent to locate sensitive infrastructure belonging to your organization. 
     These alerts become useful pivot points to check for other activity in the valuable but voluminous logs from the WAF (Web Application Firewall) and other sources. For example, other interactions by the source attempting to access public-facing decoys can be looked into and the source optionally blocked. If successful login attempts are discovered, resetting the user&#39;s credentials and enabling 2FA (Two Factor Authentication) for the account are common first steps for a containment. 
     § 7.2.2 Network Deception Defense 
     Barring insider threats, once attackers gain a foothold in an organization, they&#39;re like a new employee on day one except for the onboarding they have a very broad sense of the objective they need to achieve, but no information on the relative location of the things they need or how to get there. A strategically deployed set of decoy internal servers and workstations play well here, being available as targets to the attacker. However, simply deploying decoys leaves the odds of them being targeted at not much better than random chance. Effective network deception requires the decoys to be placed in the various locations an attacker might peruse to identify targets; better still if they can be made to look not just like valuable assets on the network, but systems that the appropriate legitimate users interact with to perform their tasks. 
     Regular users know the hostnames or network locations of the databases they need to administer, the document servers they need to pull files from, the hosts they must remote into. Even forgetful users that connect to a couple of incorrect hosts in the course of looking for the one they want are unlikely to continue once they can see that they have connected to a different host than the one they intended to connect to. 
     An attacker, on the other hand, often has something of value to glean from connecting to a different system than the one they seek to target. This results in different behavior from a regular user, making attacker interactions with decoys easy to differentiate from a user that mistyped an P. As with other types of deception, such alerts then make for great points to begin investigations from. 
     § 7.2.3 Endpoint Deception Defense 
     Consider your usage of the file system on your machine. More often than not, you probably know which file you want and where it&#39;s located. For folks that deal with a large number of local files, you may search through the file system using some keywords you know to be associated with or present in the file you&#39;re looking for. 
     However, if a decoy file is placed on your system, it&#39;s unlikely to affect any tasks you need to perform, particularly if you know it&#39;s a decoy. To an attacker, though, a file that appears to have legitimate and valuable content, and looks like it is accessed by the user, is a candidate for exfiltration. Coupled with fake processes, and breadcrumbs pointing to decoy workstations/servers posing as legitimate systems accessed by the user, endpoint deception can be put to use to detect not just behavior that would be suspicious on the network, but also behavior that would be the norm on the network but has no legitimate place on a particular endpoint at a particular time. 
     Even a malicious insider who might be familiar with weaknesses in existing defenses doesn&#39;t have extensive knowledge of every valuable file on other endpoints. Additionally, since the goal of many attacks is data theft, theft of decoy files becomes a particularly high priority signal because it indicates an attacker may be close to accomplishing their goal—after all, an attacker stealing decoy files is likely to also be stealing real ones. 
     § 7.3 Deception with Other Security Functions 
     Deception technology is a “force multiplier” for a number of existing capabilities that organizations may already have implemented. Here are some examples: 
     Deception+Endpoint Detection &amp; Response (EDR)—Endpoint detection can be significantly improved with deception. An attacker using decoy credentials stored in memory, following a decoy SSH or RDP session, or trying to escalate privileges by exploiting a decoy running process provides extremely reliable indicators of malicious host-based activity. The EDR can ‘fill in the blanks’ into what happened before (which processes ran, what other network connections were created etc.). 
     Deception+User Entity and Behavior Analytics—User Entity and Behavior Analytics (UEBA) systems are prone to false positives and data paralysis, especially in larger, geographically disparate networks where analysts may not have the context to validate an alert (Eric in the Paris security team doesn&#39;t know why Nakamura san is uploading a lot of data in Tokyo). However, the UEBA system can provide excellent enrichment to a deception alert, fleshing out the user backstory. For example, if a decoy file is copied and the user is suddenly logging in outside of office hours concurrently from two places, the detection is very likely to be real. 
     Deception+Sandboxes—Sandboxing was deception 1.0, focused on convincing malware to detonate and reveal its cards. Modern deception is sandboxing for the human attacker. By providing a wider virtual ‘attack surface’, and a believable environment for them to progress their attack, higher-order forensics can be developed. 
     Deception+Threat intelligence—Many threat intelligence services offer dumps of malware hashes, domains or IPs that rapidly go stale or are devoid of context (“block these 5000 IPs because they&#39;re from China”). The commercial ramp-up to more specific threat intelligence is significant, and often only consumable by extremely mature security organizations. While deception is typically seen as a behind-the-firewall capability, it is possible to deploy Internet-facing decoys that only engage with targeted threats against a named organization. These ‘private threat intelligence’ decoys give predictive analytics of attacker activity early on in the reconnaissance phase of the kill chain. 
     For example, decoy Amazon S 3  buckets incorporating the company&#39;s name can identify attempted reconnaissance, while decoy UAT, staging and testbed subdomains can identify attackers looking for a weak way in. Decoy login portals/webmail systems/VPNs can also reliably uncover spear-phished credentials (attackers need somewhere to use them). More business-specific use cases include decoy credit cards with a specific bank BIN or fake insurance policyholder information that may be targeted at renewal time. 
     The use of deception lets less mature companies create rather than consume threat intelligence that is specifically relevant to theft vertical or geography. More advanced security teams can leverage advanced counterintelligence deception use cases, such as decoy social media accounts to attract spear-phishing. 
     Deception+Network Traffic Analysis—Network threat detection is a critical piece of the puzzle, especially since attackers cannot avoid using the network to move around. However, network threat analytics tools suffer from scalability challenges in deployment and are increasingly blind as more traffic is encrypted, even east-west in the network. Network deception is the ‘original honeypot’, and it still works exceptionally well at detecting everything from worm-like activity such as network-driven ransomware to targeted attackers in large networks. 
     Through improvements in virtualization and software-defined networking, we can place network decoys at scale in every subnet and ULAN, as well as in the dark IP space of the network to efficiently detect network-driven threats. Moreover, since the network decoy itself is instrumented, it is not blind to encrypted traffic and can provide full packet forensics about the threat. 
     Deception+Threat Hunting Platform—Deception and threat hunting are two pillars of ‘active defense’—proactively attempting to take the fight to the adversary. We find that the champions for deception technology at many of our customers are the threat hunting teams, as they are able to think like the attacker and deploy deception in paths where they perceive they will have to traverse. 
     A deception alert is a perfect trigger to a hunt mission and can let an analyst ring-fence a potential incident with decoys while they pivot on available data to understand the root cause of the threat. The real-world analogy of a hunter laying traps is extremely applicable here. 
     Deception technology can help you cut down your:
         Mean Time To Detect (MTTD): Dwell time usually spans months. When you strategically lay traps across your network, you leave attackers with very limited room to maneuver. This can cut down your MTTD to near-zero.   Mean Time To Know (MTTK): Since deception deployments generate fewer and higher-confidence alerts, the attacker&#39;s activity can be studied much more closely, their TTPs fingerprinted, and their intended target identified in minutes instead of days or hours, particularly with a threat hunting team at the console. In fact, we&#39;ve even seen teams achieve this in single-digit minutes for some attacks.   Mean Time To Respond (MTTR): With the deception deployment in place for a few weeks or months, you can strategize around automated response for known attack types. This frees up man-hours that can then be directed towards defending against more capable adversaries, and other operational requirements.       

     Deception deployments do away with perhaps your most pressing pain point—the flood of alerts from all your security tools. Over the years the industry as a whole seems to have come at the needle-in-the-haystack problem saying “Here&#39;s some more hay”. Its the reason teams miss alerts or tune them out like the OS error dialogues most folks don&#39;t bother reading before dismissing. With deception, you can instead begin with a high-confidence alert and trace it back to the proverbial needle using specific attributes in the SIENA, specific network source, time range, and optionally, a username. 
     Once the response for known attack types is automated, it does away with the tedium of analyzing alerts that any machine could. The boring stuff is automated, leaving you to focus on matters that actually warrant your attention. 
     § 7.4 Deception System 
       FIG.  11    is a network diagram of a deception system  700  with endpoint agents such as the application  350  and with appliances  702  in an enterprise network  704 . The deception system includes a central management console  710 , an aggregator  712 , a decoy farm  714 , the appliance  702 , the endpoint agent  350 , and management system  716 . 
     The central management console  710  is the main component responsible for everything from UI, managing appliances  702  and endpoint agents  350  to event processing and deployment of decoys and policies on the endpoints. The aggregator  712  is configured to terminate tunnels between the appliances  702  and the decoy farm  714 . It is responsible for routing of traffic. 
     The decoy farm  714  can include containers which host the decoys and forward the events and evidence to the central management console  710 . The appliance  702  can be a lightweight VM which resides in the client environment and is responsible for projecting the decoys onto their network. 
     The endpoint agent  350  can be a lightweight agent deployed on the endpoints that is responsible for deploying the endpoint deception and monitoring for the first order detections. It talks to the central management console  710  for the policies and also to send back the events. 
     § 7.5 Cloud-Based Deception System 
       FIG.  12    is a network diagram of a deception system  800  utilizing the cloud-based system  100  in lieu of on-premises physical or virtual appliances. Deployment of deception is dependent on routing attack network traffic to decoys. Traditional architectures as in the deception system  700  require the use of appliances/virtual machines in order to project decoys onto the network. Disadvantageously, this requires on-premises deployment within the enterprise network  704  which requires IT resources, does not scale with distributed users  102 , etc. 
     The present disclosure includes integrating deception which can be referred to as active defense with the cloud-based system  100 . By integrating with the cloud-based system  100  which provides ZTNA, attack traffic that should be destined for decoys can be routed through the cloud-based system  100  ‘switchboard.’ Dynamic routing of traffic based on zero trust principles and modification of the network data path is possible to make on-the-fly decisions on which decoy to send an attacker to. Legitimate traffic can pass through the cloud-based system  100  to ‘known good’ destinations without any redirection, and intruder traffic can be routed to a decoy cloud  802 . 
     By using the cloud-based system  100  as a zero trust policy director as a switchboard, it is possible to deploy deception into an environment without any physical/virtual appliances or engagement virtual machines. Removal of the appliance creates a completely software-defined deception mesh that can route attack traffic from any device in the network that is connected to the cloud-based system  100  to decoys in the decoy cloud  802 . 
     The decoy compute no longer exists in the user environment and is instead hosted in the decoy cloud  802 . Attackers are unaware that their engagement occurs in the decoy cloud  802 , and the deception can be dynamically scaled based on the zero trust policy requirements of the user  102 . The decoy cloud  802  looks like the enterprise network. Deception policies can be dynamically created based on the user&#39;s  102  real application access policy that is captured in the cloud-based system  100 . This makes the deception far more believable as it is intrinsically based on the actual user  102  and application  350  trust policy. That is, the application  350  has rich user information based on existing services in the cloud-based system  100 . Deception alerts gain additional context/intelligence from the cloud-based system  100  for more actionable alerts. Potentially suspicious traffic that would normally be dropped/allowed can be dynamically redirected to decoys in order to ‘play out’ the threat, gain better telemetry and validate whether the traffic is legitimate or malicious. Deception becomes a configurable policy action that can be taken by users  102  to define whether traffic is routed to decoys. 
     Referring to  FIG.  12   , the application  350  can be configured to support deception technology among other features, and the decoy cloud  802  can be a SaaS hosted environment for each customer (i.e., tenant, enterprise, etc.) which is isolated and segregated from the customer&#39;s real IT environment. The decoy cloud  802  includes a reverse-connect connection broker (e.g., the app connector  400 ) deployed in the decoy cloud environment that connects to the cloud-based system  100 . 
     The cloud-based system  100  can include a policy director which includes policies for routing traffic to the decoy cloud  802  environment. The policies are automatically and dynamically created by the policy director within the cloud-based system. Additionally, users can create their own policies to granularly decide when to send traffic to decoys for example, based on traffic profile, time of day, user activity, etc. Traffic is evaluated by the cloud-based system  100  and dynamic decisions are made on whether to route it to legitimate applications or to decoys. The deception system  800  inter-links with the cloud-based system  100 , correlating user/device/application identity mappings for high-context, low false positive alerting. 
     In an example operation, the endpoint agent  300  can plant browser lures, breadcrumbs, fake passwords, fake cookies, decoy files, etc. (collectively referred to as “fake assets”) on the user device  300  (step  810 ), and corresponding decoys associated with the fake assets are deployed in the decoy cloud  802 . For example, fake passwords can be for legitimate sites that can be decoyed in the decoy cloud  802 . When an intruder accesses the fake assets (step  812 ), the cloud-based system  100  blocks the intruder traffic since it is not good traffic and diverts it to the decoys (step  814 ). The decoy cloud  802  monitors the activity of the intruder to gain valuable telemetry, private threat intelligence, indicators of compromise, threat hunting, etc. (step  816 ). Advantageously, this approach has low false positives (i.e., no legitimate use for the fake assets, so assume any access is illegitimate) and rapid containment of the intrusion. 
     Specifically, when an intruder accesses a fake access on an ingress connection, the egress connection is routed back to the decoy cloud  802  via the cloud-based system  100  instead of back out as good traffic. In this manner, the intruder is unaware and performs activity in the decoy cloud  802 . Here, it is possible to gain intelligence about the threat without actually compromising actual resources. 
     Advantageously, this approach can be used to secure the endpoint device  300 , secure applications thereon, secure the cloud, and the user&#39;s  102  identity. 
     § 7.6 Cloud-Based Deception Process 
       FIG.  13    is a flowchart of a cloud-based deception process  850 . The cloud-based deception process  850  contemplates operation via the cloud-based system  100  and the decoy cloud  802 . In an embodiment, the decoy cloud  802  can be part of the cloud-based system  100 , it is show separately to illustrate the functionality. The cloud-based deception process  850  can be a computer-implemented method having steps, implemented via one or more servers having processors configured to implement the steps, and as instructions embodied in a non-transitory computer-readable medium for causing one or processors to implement the steps. 
     The cloud-based deception process  850  includes hosting a decoy cloud environment for a customer that contains a plurality of decoys and that is hosted and separated from a real environment of the customer (step  852 ); receiving traffic from a user associated with the customer (step  854 ); detecting the traffic is related to accessing a fake asset on a user device associated with the user (step  856 ); rerouting the traffic to the decoy cloud environment (step  858 ); and monitoring activity associated with the fake asset in the decoy cloud environment (step  860 ). 
     The steps can further include detecting the traffic is unrelated to any fake asset on the user device ( 300 ) and processing the unrelated traffic. That is, the cloud-based system  100  can perform any functions on the unrelated traffic, in addition to supporting the deception system  800 . The processing can include any of allowing the unrelated traffic, blocking the unrelated traffic, cleaning the unrelated traffic, threat detecting the unrelated traffic, sandboxing the unrelated traffic, and the like. 
     The fake assets can include any of deceptive assets, files, breadcrumbs, lures, bait, network traffic, passwords, keys, session information, and cookies. The key aspect of the fake assets is they are illegitimate and should never be accessed except for malicious purposes. The fake assets are meant to look real and entice any intruder. The fake assets can be based on a role of the user, such as determined based on historical monitoring of the user. Specifically, the cloud-based system  100  has rich user information for specifically tailoring the fake assets. 
     The steps can further include determining an indication of compromise for the user and/or the user device and providing a notification based thereon. Also, there can be various remediation approaches. 
     The rerouting can be based on a set of policies that include traffic profile, time of day, and user activity. The decoy cloud  802  can include the connector  400  configured to dial out only and reject inbound connections. 
     § 8.0 Breach Detection 
     The deception system  800  through the cloud-based system  100  is a high-fidelity, low false positive system to efficiently detect targeted, sophisticated threats both internally and on the perimeter using heuristic filtering and dynamic risk scoring of threats that engage with decoy IT assets including deceptive infrastructure, credentials, files, users, identity management systems and network traffic. The deception system  800  is difficult to evade as it is hidden from the attacker. 
     Advantageously, the deception system  800  provides breach detection technology to 
     1) Identify perimeter application threats while filtering out Internet ‘noise’ in order to identify only targeted threats against the perimeter. 
     2) Identify threats on endpoint devices without the overhead of collecting, transmitting and storing all endpoint activity. 
     3) Prioritize threats based on a dynamic risk scoring algorithm that tracks attacker activity in real-time and modifies the threat score based on attack patterns. The risk score is used for zero-trust conditional access policies. 
     4) Provide a hard to evade breach detection technology leveraging deception and countermeasures. 
     Most traditional security systems categorize alerts into ‘high/medium/low’ buckets (or some other discrete categorization) as they are prone to false positives. With deception-based breach detection technology, false positives are minimal, so we granularly score every attacker action in order to build a risk-based alerting and prioritization model. 
     The risk scoring algorithm operates by monitoring all activity by unique attackers against deceptive/decoy assets that include fake systems, passwords, files, and network traffic. Every additional step taken by the attacker increases the score. For example, starting a network connection, upgrading it to a full connection, sending data, sending data that has a malicious payload. 
     The risk score can be used with thresholds to make automatic policy decisions in a zero-trust system. For example, if the score crosses  100 , an automated containment response is initiated. Of note, all hits on the fake assets are malicious by definition—this risk scoring covers a unique way to convey the actual threat. 
     Using this novel scoring approach, this breach detection system can detect threats against the perimeter using decoy systems on the Internet. Traditional systems would generate too much ‘noise’ for such an approach to be viable, however, by dynamically tracking attacker behavior, the system can generate extremely low false positives and differentiate between targeted attacks v/s non targeted general attacks on the Internet. 
     On the internal network, this system  800  utilizes deceptive assets, files, breadcrumbs, lures, bait, network traffic, passwords, keys and cookies in order to detect attacks without logging all system activity and transmitting it to an analysis system like traditional approaches do. This system can perform all threat detection locally on the endpoint device without the overhead of transmitting, analyzing, and storing data centrally. This distributed detection quality allows it to offer substantially greater scalability to millions of endpoint devices through reduced resource utilization. 
     Similar to ‘trap pins’ designed to make physical locks hard to pick, the system has countermeasures to make it extremely hard to evade. By hiding on the system, creating deceptive versions of itself and other security software and detecting active attempts to probe or discover the threat detection system, an attacker&#39;s attempts to subvert the threat detection not only are thwarted, but raise a high-risk score alert of an evasion attempt. 
     An advantage of the deception system  800  with the cloud-based system  100  is there is no need to log all activity on the user device  300 , this is already managed off-device in the cloud-based system  100 . The user device  300 , with respect to deception, only has to log activity related to the fake assets. This minimizes the resource consumption at the user device  300 . 
     § 9.0 Auto-Decoy and Breadcrumb Creation 
     The present disclosure can include analyzing a user&#39;s activity in order to dynamically create deceptive assets such as files, passwords, breadcrumbs, lures, cookies, sessions etc. that are contextually relevant to the user&#39;s job profile, daily tasks, and designation. The system can auto-configure itself to differentiate between, for example, a vice president of Marketing v/s an IT system administrator. Specifically, the cloud-based system  100  and the application  350  have rich historical user information that can be used to further leverage the fake assets. The system  800  also creates network traps that are logically relevant pointers to the breadcrumbs. For example, a finance user will have breadcrumbs that automatically point to a finance related file server without user configuration. The system  800  can also automatically generate ‘blend in’ and ‘stand out’ decoys that mimic attributes such as the organization&#39;s network naming conventions and types of hardware used. 
     Advantageously, the present disclosure can 
     1) Automate the process of creating deceptive campaigns without requiring any manual user input in order to decide the deception strategy and provision the deceptive assets. 
     2) Substantially improve the realism of the deception to ensure greater probability of engagement by attackers who may know the ‘lay of the land’ either as insiders, or through earlier reconnaissance of the target. 
     3) Ensure that every deception campaign is unique for every organization, making the deception extremely difficult to fingerprint. 
     4) Allow users to immediately and dynamically modify the deception automatically or with the push of a button (“one-click”), where all deceptive assets can change their personalities unaided. 
     An aspect of the breadcrumb creation is the cloud-based system  100  has knowledge of user type. Also, it is possible to multiply fake assets to make each look unique and look like a customer environment so the attackers are unaware. 
     § 10.0 Conclusion 
     It will be appreciated that some embodiments described herein may include one or more generic or specialized processors (“one or more processors”) such as microprocessors; Central Processing Units (CPUs); Digital Signal Processors (DSPs): customized processors such as Network Processors (NPs) or Network Processing Units (NPUs), Graphics Processing Units (GPUs), or the like; Field Programmable Gate Arrays (FPGAs); and the like along with unique stored program instructions (including both software and firmware) for control thereof to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and/or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more Application-Specific Integrated Circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic or circuitry. Of course, a combination of the aforementioned approaches may be used. For some of the embodiments described herein, a corresponding device in hardware and optionally with software, firmware, and a combination thereof can be referred to as “circuitry configured or adapted to,” “logic configured or adapted to,” etc. perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. on digital and/or analog signals as described herein for the various embodiments. 
     Moreover, some embodiments may include a non-transitory computer-readable storage medium having computer-readable code stored thereon for programming a computer, server, appliance, device, processor, circuit, etc. each of which may include a processor to perform functions as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory, and the like. When stored in the non-transitory computer-readable medium, software can include instructions executable by a processor or device (e.g., any type of programmable circuitry or logic) that, in response to such execution, cause a processor or the device to perform a set of operations, steps, methods, processes, algorithms, functions, techniques, etc. as described herein for the various embodiments. 
     The foregoing sections include headers for various embodiments and those skilled in the art will appreciate these various embodiments may be used in combination with one another as well as individually. Although the present disclosure has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and/or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present disclosure, are contemplated thereby, and are intended to be covered by the following claims.