Event-Triggered Reauthentication of At-Risk and Compromised Systems and Accounts

A system and method that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified threat or potential security risk.

BACKGROUND OF THE INVENTION

Field of the Invention

The disclosure relates to the field of network security, particularly to user authentication.

Discussion of the State of the Art

With the advent of the log4j vulnerability discovered in late 2021, it is clear that reactions to zero-day exploits are still too slow, yet an immediate response is vital to the health of the economy at large. As it stands, most US citizens, businesses, and governments have an indispensable online presence that depends on the safe and secure operation of the internet and the world wide web. Identity hacks, ransomware, zero-day exploits, and other security vulnerabilities all pose a threat to this economic nexus. While the economic impact of log4j is due to the exploit affecting computers worldwide, more targeted attacks still promise devastation for individuals and enterprise environments where exploits can quickly spread between accounts and systems. Whether comprised accounts or systems are personal, business, or governmental, there needs to be a system in place for securing a collective of accounts and systems and minimizing damage during an immediate threat such as log4j and other exploits.

What is needed is a system that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified or potential security risk.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived, and reduced to practice, a system and method that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified or potential security risk.

According to one aspect of the invention, a system for event-triggered reauthentication of at-risk and compromised systems and accounts is disclosed, comprising: a computing device comprising a memory and a processor connected to a computer network; and a threat mitigation module comprising a plurality of programming instructions stored in the memory of, and operable on the processor of, the computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to: monitor the computing device's event logs; retrieve a dataset of cyber vulnerabilities from a plurality of vulnerability databases; identify a potential or ongoing cybersecurity threat on the computing device by comparing events within the event logs against the dataset of cyber vulnerabilities; and trigger a forced reauthentication of a service associated with the potential or ongoing cybersecurity threat.

According to a second preferred embodiment, a method for event-triggered reauthentication of at-risk and compromised systems and accounts is disclosed, comprising the steps of: monitoring a computing device's event logs; retrieving a dataset of cyber vulnerabilities from a plurality of vulnerability databases; identifying a potential or ongoing cybersecurity threat on the computing device by comparing events within the event logs against the dataset of cyber vulnerabilities; and triggering a forced reauthentication of a service associated with the potential or ongoing cybersecurity threat.

According to various aspects; the system further comprising a validation module comprising a plurality of programming instructions stored in the memory of, and operable on the processor of, the computing device, wherein the plurality of programming instructions, when operating on the processor, cause the computing device to establish a baseline usage profile of the computing device; wherein the established usage profile is used along with the event logs to identify a potential or ongoing cybersecurity threat; wherein the computing device is implemented as a cloud-based service that monitors a remote computing device using a software agent and enforces authentication over a network; wherein the computing device is implemented as a server on an enterprise network that monitors all computing devices on the enterprise network and enforces authentication over a network; wherein the service associated with the potential or ongoing cybersecurity threat is an operating system service; wherein the service associated with the potential or ongoing cybersecurity threat is a cloud-based service; wherein the service associated with the potential or ongoing cybersecurity threat is a user account service, forcing the computing device's user account to reauthenticate; and wherein the forced reauthentication uses two-factor authentication; wherein the forced reauthentication is selected from a plurality of verification methods, wherein: each verification method is associated with a number of points; the successful completion of a verification method awards the number of points associated with that verification method; the total number of points available for successful completion of the plurality of verification methods is equal to or greater than the verification score; and at least one of the verification methods is a non-automated verification method requiring a manual input.

DETAILED DESCRIPTION OF THE INVENTION

The inventor has conceived, and reduced to practice, a system and method that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified or potential security risk.

Conceptual Architecture

FIG. 1 is a diagram of an exemplary architecture of an advanced cyber decision platform 100 according to an embodiment of the invention. Client access to network resource or system 105 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system's distributed, extensible high bandwidth cloud interface 110 which uses a versatile, robust web application driven interface for both input and display of client-facing information and a data store 112 such as, but not limited to MONGODB™, COUCHDB™, CASSANDRA™ or REDIS™ depending on the embodiment. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud based sources 107, public or proprietary such as, but not limited to: subscribed business field specific data services, external remote sensors, subscribed satellite image and data feeds and web sites of interest to business operations both general and field specific, also enter the system through the cloud interface 110, data being passed to the connector module 135 which may possess the API routines 135a needed to accept and convert the external data and then pass the normalized information to other analysis and transformation components of the system, the directed computational graph module 155, high volume web crawler module 115, multidimensional time series database 120 and a graph stack service 145. Directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. High-volume web crawling module 115 may use multiple server-hosted preprogrammed web spiders which, while autonomously configured, may be deployed within a web scraping framework 115a of which SCRAPY™ is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology. Multiple dimension time series data store module 120 may receive streaming data from a large plurality of sensors that may be of several different types. Multiple dimension time series data store module 120 may also store any time series data encountered by system 100 such as, but not limited to, environmental factors at insured client infrastructure sites, component sensor readings and system logs of some or all insured client equipment, weather and catastrophic event reports for regions an insured client occupies, political communiques and/or news from regions hosting insured client infrastructure and network service information captures (such as, but not limited to, news, capital funding opportunities and financial feeds, and sales, market condition), and service related customer data. Multiple dimension time series data store module 120 may accommodate irregular and high-volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Inclusion of programming wrappers 120a for languages—examples of which may include, but are not limited to, C++, PERL, PYTHON, and ERLANG™—allows sophisticated programming logic to be added to default functions of multidimensional time series database 120 without intimate knowledge of the core programming, greatly extending breadth of function. Data retrieved by multidimensional time series database 120 and high-volume web crawling module 115 may be further analyzed and transformed into task-optimized results by directed computational graph 155 and associated general transformer service 160 and decomposable transformer service 150 modules. Alternately, data from the multidimensional time series database and high-volume web crawling modules may be sent, often with scripted cuing information determining important vertices 145a, to graph stack service module 145 which, employing standardized protocols for converting streams of information into graph representations of that data, for example open graph internet technology (although the invention is not reliant on any one standard). Through the steps, graph stack service module 145 represents data in graphical form influenced by any pre-determined scripted modifications 145a and stores it in a graph-based data store 145b such as GIRAPH™ or a key-value pair type data store REDIS™, or RIAK™, among others, any of which are suitable for storing graph-based information.

Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the data already available in automated planning service module 130, which also runs powerful information theory-based predictive statistics functions and machine learning algorithms 130a to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Then, using all or most available data, automated planning service module 130 may propose business decisions most likely to result in favorable business outcomes with a usably high level of certainty. Closely related to the automated planning service module 130 in the use of system-derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, action outcome simulation module 125 with a discrete event simulator programming module 125a coupled with an end user-facing observation and state estimation service 140, which is highly scriptable 140b as circumstances require and has a game engine 140a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data.

FIG. 2 is a flow diagram of an exemplary function 200 of the advanced cyber decision platform in the detection and mitigation of predetermining factors leading to and steps to mitigate ongoing cyberattacks. The system continuously retrieves network traffic data, at step 201, which may be stored and preprocessed by the multidimensional time series data store 120 and its programming wrappers 120a. All captured data are then analyzed to predict the normal usage patterns of network nodes such as internal users, network connected systems and equipment and sanctioned users external to the enterprise boundaries for example off-site employees, contractors and vendors, just to name a few likely participants. Of course, normal other network traffic may also be known to those skilled in the field, the list given is not meant to be exclusive and other possibilities would not fall outside the design of the invention. Analysis of network traffic may include graphical analysis of parameters such as network item to network usage using specifically developed programming in the graphstack service 145, 145a, analysis of usage by each network item may be accomplished by specifically predeveloped algorithms associated with the directed computational graph module 155, general transformer service module 160 and decomposable service module 150, depending on the complexity of the individual usage profile at step 201. These usage pattern analyses, in conjunction with additional data concerning an enterprise's network topology; gateway firewall programming; internal firewall configuration; directory services protocols and configuration; and permissions profiles for both users and for access to network resources and/or sensitive information, just to list a few non-exclusive examples may then be analyzed further within the automated planning service module 130, where machine learning techniques which include but are not limited to information theory statistics 130a may be employed and the action outcome simulation module 125, specialized for predictive simulation of outcome based on current data 125a may be applied to formulate a current, up-to-date and continuously evolving baseline network usage profile at step 202. This same data would be combined with up-to-date known cyberattack methodology reports, possibly retrieved from several divergent and exogenous sources through the use of the multi-application programming interface aware connector module 135 to present preventative recommendations to the enterprise decision makers for network infrastructure changes, physical and configuration-based to cost effectively reduce the probability of a cyberattack and to significantly and most cost effectively mitigate data exposure and loss in the event of attack at steps 203 and 204.

While some of these options may have been partially available as piecemeal solutions in the past, we believe the ability to intelligently integrate the large volume of data from a plurality of sources on an ongoing basis followed by predictive simulation and analysis of outcome based upon that current data such that actionable, business practice efficient recommendations can be presented is both novel and necessary in this field.

Once a comprehensive baseline profile of network usage using all available network traffic data has been formulated, the specifically tasked advanced cyber decision platform continuously polls the incoming traffic data for activities anomalous to that baseline as determined by pre-designated boundaries at step 205. Examples of anomalous activities may include a user attempting to gain access several network resources such as workstations or servers in rapid succession, or a user attempting to gain access to a domain server with sensitive information using random userIDs or another user's userID and password, or attempts by any user to brute force crack a privileged user's password, or replay of recently issued ACTIVE DIRECTORY™/Kerberos ticket granting tickets, or the presence on any known, ongoing exploit on the network or the introduction of known malware to the network, just to name a very small sample of the cyberattack profiles known to those skilled in the field. The invention, being predictive as well as aware of known exploits is designed to analyze any anomalous network behavior, formulate probable outcomes of the behavior, and to then issue any needed alerts regardless of whether the attack follows a published exploit specification or exhibits novel characteristics deviant to normal network practice. Once a probable cyberattack is detected, the system then is designed to get needed information to responding parties at step 206, and tailored, where possible, to each role in mitigating the attack and damage arising from it at step 207. This may include the exact subset of information included in alerts and updates and the format in which the information is presented which may be through the enterprise's existing security information and event management system. Network administrators, then, might receive information such as but not limited to where on the network the attack is believed to have originated, what systems are believed currently affected, predictive information on where the attack may progress, what enterprise information is at risk and actionable recommendations on repelling the intrusion and mitigating the damage, whereas a chief information security officer may receive alert including but not limited to a timeline of the cyberattack, the services and information believed compromised, what action, if any has been taken to mitigate the attack, a prediction of how the attack may unfold and the recommendations given to control and repel the attack at step 207, although all parties may access any network resources and cyberattack information for which they have granted access at any time, unless compromise is suspected. Other specifically tailored updates may be issued by the system at steps 206 and 207.

FIG. 3 is a process diagram showing advanced cyber decision platform functions 300 in use to mitigate cyberattacks. Input network data which may include network flow patterns 321, the origin and destination of each piece of measurable network traffic 322, system logs from servers and workstations on the network 323, endpoint data 323a, any security event log data from servers or available security information and event (SIEM) systems 324, identity and assessment contexts 325, external network health or cybersecurity feeds 326, Kerberos domain controller or ACTIVE DIRECTORY™ server logs or instrumentation 327, business unit performance related data 328, and external threat intelligence feeds 329, among many other possible data types for which the invention was designed to analyze and integrate, may pass into 315 the advanced cyber decision platform 310 for analysis as part of its cyber security function. These multiple types of data from a plurality of sources may be transformed for analysis 311, 312 using at least one of the specialized cybersecurity, risk assessment or common functions of the advanced cyber decision platform in the role of cybersecurity system, such as, but not limited to network and system user privilege oversight 331, network and system user behavior analytics 332, attacker and defender action timeline 333, SIEM integration and analysis 334, dynamic benchmarking 335, and incident identification and resolution performance analytics 336 among other possible cybersecurity functions; value at risk (VAR) modeling and simulation 341, anticipatory vs. reactive cost estimations of different types of data breaches to establish priorities 342, work factor analysis 343 and cyber event discovery rate 344 as part of the system's risk analytics capabilities; and the ability to format and deliver customized reports and dashboards 351, perform generalized, ad hoc data analytics on demand 352, continuously monitor, process and explore incoming data for subtle changes or diffuse informational threads 353 and generate cyber-physical systems graphing 354 as part of the advanced cyber decision platform's common capabilities. Output 317 can be used to configure network gateway security appliances 361, to assist in preventing network intrusion through predictive change to infrastructure recommendations 362, to alert an enterprise of ongoing cyberattack early in the attack cycle, possibly thwarting it but at least mitigating the damage 362, to record compliance to standardized guidelines or SLA requirements 363, to continuously probe existing network infrastructure and issue alerts to any changes which may make a breach more likely 364, suggest solutions to any domain controller ticketing weaknesses detected 365, detect presence of malware 366, and perform one time or continuous vulnerability scanning depending on client directives 367. These examples are, of course, only a subset of the possible uses of the system, they are exemplary in nature and do not reflect any boundaries in the capabilities of the invention.

Along with the features discussed above, advanced cyber decision platform 100 functions may be configured to operate as a server 405 that utilizes contextual and risk-based multi-factor authentication. FIG. 4 is an illustration of an example architecture system 400 used for contextual and risk-based multi-factor authentication as used in various embodiments of the present invention. System 400 comprises a server 405, a plurality of users 410[a-n], and a plurality of verification methods 415[a-g]. Although, system 400 illustrates a direct connection between users and server, it should be understood that this is not indicative of a limitation of the system. Server 405 may be an authentication server for security device, such as a badge reader or biometric scanner or a security terminal, that may need to check a database on the server. Examples may include initiating a peer-to-peer connection, accessing a protected computer, gaining access to restricted physical locations, or the like. For simplicity, intermediate security devices are omitted in the examples used in the present disclosure.

In system 400, users 410[a-n], connects to server 405. In addition to a primary authentication method, such as a user identification and password, the user may be required to undergo additional verification. Server 405 may be configured to run advanced cyber decision platform 100, and further configured to dynamically determine a required verification score based at least on the circumstances of the connection before granting access to the user. Circumstances that may affect the score may include, but is not limited to, origin of the user's connection, whether the access request is determined to be anomalous using the cybersecurity functions of advanced cyber decision platform 100, accessing files or drives with a higher-level security assignment, and the like. Verification points may be abstained via one or more verification methods 415[a-g], which may include, without limitation, sensors 415a, trusted parties 415b, untrusted parties 415c, video or picture 415d, network monitoring 415e, device ID 415f, and one-time-use codes 415g.

To provide to some specific examples of the various verification methods, sensors 415a may include biometrics scans, such as fingerprint scan, iris scan, facial recognition, and the like; voice recognition; and employee badge scanning using some near-field technology such as radio-frequency identification (RFID), or near field communication (NFC). Sensors may be sensors built into a user's mobile device, or it may be installed semi-permanently at a secure location, for example, at a security desk at an office.

Trusted parties 415b may include a user's co-worker or security personnel that may have received a request by server 405 during the additional verification step to verify whether the user requesting access is actually the user, and not a malicious party. For example, a user may request access from a server, and once the server requires additional verification it may send an alert to a random co-worker in the proximity of the user. The co-worker may verify, for example, with their own badge scanner or biometric scanner, or taking and submitting a photograph or video.

Untrusted parties 415c may be verification via a third party not normally associated with the user. For example, the third party may be a member of a rewards program incentivizing submission of pictures, posting comments, or the like at the request of the server. The rewards program may additionally be disguised so that it may appear as a simple activity the third party may participate in to earn rewards without overtly making it a means of verifying the user. For example, the rewards program may be disguised as an augmented reality game that requests players to submitting pictures and videos, or commenting on their surroundings to earn points. Penalties may also be implemented to deter wrongful verification by untrusted parties.

Video or picture 415d may include videos or pictures taken with the camera on a laptop, desktop computer, or mobile device; cameras installed at secure locations at an office; video or pictures taken by an autonomous drone sent by the server; or the like.

Network monitoring 415e may be passive verification by the server based on information regarding the connection requesting access, and analyzed using the cybersecurity functions of advanced cyber decision platform 100. Such information may include, for instance, access or traffic compared to a pre-established network baseline, origin of the user connection, time of access request, and the like. For example, a user connecting from within an office, perhaps determined through determining the IP address of the user, during normal work hours may be provided more verification points during verification than a user who is connecting using an airport's Wi-Fi network during odd hours.

Device ID 415f may be another passive verification by the server that takes into account the user's connecting device, such as, a MAC address, or a device fingerprint generated by the server based on the hardware and software configuration of the user's device.

One-time-use codes 415g may be uniquely generated codes that are sent to the user through a text message or email, or generated on-demand on the user's mobile device. The code may also take the form of a uniquely generated hyperlink that the user may simply click on to verify. Various implementations of the one-time-use code are presently used in the art.

The various verification methods may be configured so that each method may grant different amounts of verification points based on metrics defined by the user, such as how secure the method is. For example, a badge reader at an office that has a security personnel keeping watch may grant the user more points than a fingerprint scan on a mobile device.

FIG. 5 is a sequence flow diagram summarizing one method 500 for a user to connect to a server used in various embodiments of the invention. For the purposes of this sequence flow diagram, it will be presumed that the user is successfully verified at all authentication and verification steps. At an initial step 505, a user requests access from a server. The server may prompt the user for some initial form of authentication, such as a login and password. At step 510, the server dynamically determines a verification score required for the user to be granted access. At step 515, the server may request that the user use a plurality of verification methods to reach the verification score needed before access is granted. The various verification methods are discussed above in system 400. Depending on the verification method used, the method may be initiated by either the user or the server. Once verification is successful, the user is granted access by the server at step 520. In some embodiments, instead of using points, the system may be configured to require a certain number of verification methods to be used, or requiring a particular verification method to be used in conjunction with a number of other verification methods. Other embodiments may use a combination of the points-based system, and the method-count system.

FIG. 8 is an illustration of an example architecture system for a cloud-based threat mitigation service 800 used for forced reauthentication during a perceived cybersecurity threat. Along with the features discussed above, advanced cyber decision platform 100 functions may be configured to operate as a cloud-based threat mitigation service 800 that utilizes computing system logs 803, baseline network usage profiles (not shown—see FIG. 4-7), and external vulnerability databases 805a-n to mitigate cybersecurity threats. Cloud-based threat mitigation service 800 comprises a software agent 802 residing on a personal computing device 801 that relays event and security operating system logs 803 over the internet 804 to the cloud-based threat mitigation service 800 for monitoring and detection of anomalous events that may be cybersecurity attacks. The software agent may also relay other system information (hardware profiles, operating system configurations, etc.) to the cloud-based threat mitigation service 800. Additionally, the cloud-based threat mitigation service 800 may generate and maintain a baseline network usage profile of the personal computing device 801, as described in FIG. 4-7. The cloud-based threat mitigation service 800 also retrieves and ingests known exploits—including zero-day exploits—from a plurality of vulnerability databases 805a-n, such as ISS X-Force database, Symantec/SecurityFocus BID database, Open Source Vulnerability Database (OSVDB), Common Vulnerabilities and Exposures (CVE) database, and the National Vulnerability Database (NVD). The cloud-based threat mitigation service 800 may also use API functions with services such as Have I Been Pwned to determine if one or more accounts or services used on the personal computing device 801 have been compromised in a data breach.

The cloud-based threat mitigation service 800 continuously compares the stream of logs 803, system information, and baseline network usage profile to the vulnerability and breach database information 805a-n to detect both potential and active cybersecurity attacks.

Upon detection of a perceived attack, the cloud-based threat mitigation service 800 may send a signal to the software agent 802 to take immediate remedial action to subvert an ongoing attack or to prevent a future attack. The remedial action may take different courses according to the severity and urgency of an attack. For example, if the personal computing device 801 is a Microsoft Windows machine, a Windows security event log ID 1102 relates to the clearing of the audit log. Attackers often clear audit logs to cover their tracks, thus, this is typically a clear indication of account takeover. Upon detection of an event 1102, the cloud-based threat mitigation service 800 may trigger the software agent 802 to clear all browser sessions and cookies—removing the ability of the attacker to use already authenticated services 806a-n, as well as forcing a reauthentication of the Microsoft Windows account either by performing a soft reset (power cycle), disabling all administrative accounts, actively disabling ports on a firewall, requiring a renewed two-factor authentication, or another reauthentication or attack subversion technique known in the art.

The cloud-based threat mitigation service 800 may also comprise an API or use APIs from cloud-based services 806a-n such that in the event that a customer of both the cloud-based threat mitigation service 800 and some cloud-based service 806a-n is compromised, a trigger can be sent to the cloud-based service 806a-n to end all open sessions with that individual and require a new 2FA (two-factor authentication). These examples are meant to illustrate a few methods of detecting and mitigating attacks and are not meant to be limiting in any way.

FIG. 9 is an illustration of an example architecture system for a stand-alone threat mitigation module used for forced reauthentication during a perceived cybersecurity threat. Along with the features discussed above, advanced cyber decision platform 100 functions may be configured operate as a stand-alone threat mitigation module 900 that utilizes computing system logs 803, baseline network usage profiles (not shown—see FIG. 4-7), and external vulnerability databases 805a-n to mitigate cybersecurity threats. Stand-alone threat mitigation module 900 monitors event and security operating system logs 803 to detect anomalous events that may be cybersecurity attacks. The stand-alone threat mitigation module 900 may also retreive other system information (hardware profiles, operating system configurations, etc.) on top of generating and maintaining a baseline network usage profile of the personal computing device 801, as described in FIG. 4-7. The stand-alone threat mitigation module 900 also retrieves and ingests known exploits—including zero-day exploits—from a plurality of vulnerability databases 805a-n, such as ISS X-Force database, Symantec/SecurityFocus BID database, Open

Source Vulnerability Database (OSVDB), Common Vulnerabilities and Exposures (CVE) database, and the National Vulnerability Database (NVD). The stand-alone threat mitigation module 900 may also use API functions with services such as Have I Been Pwned to determine if one or more accounts or services used on the personal computing device 801 have been compromised in a data breach.

The stand-alone threat mitigation module 900 continuously compares the stream of logs 803, system information, and baseline network usage profile to the vulnerability and breach database information 805a-n to detect both potential and active cybersecurity attacks.

Upon detection of a perceived attack, the stand-alone threat mitigation module 900 may take immediate remedial action to subvert an ongoing attack or to prevent a future attack. The remedial action may take different courses according to the severity and urgency of an attack. For example, if the personal computing device 801 is a Microsoft Windows machine, a Windows security event log ID 1102 relates to the clearing of the audit log. Attackers often clear audit logs to cover their tracks, thus, this is typically a clear indication of account takeover. Upon detection of an event 1102, the cloud-based threat mitigation service 800 may trigger the software agent 802 to clear all browser sessions and cookies—removing the ability of the attacker to use already authenticated services 806a-n, as well as forcing a reauthentication of the Microsoft Windows account either by performing a soft reset (power cycle), disabling all administrative accounts, actively disabling ports on a firewall, requiring a renewed two-factor authentication, or another reauthentication or attack subversion technique known in the art.

The stand-alone threat mitigation module 900 may also comprise an API or use APIs from cloud-based services 806a-n such that in the event that a customer of both the cloud-based threat mitigation service 800 and some cloud-based service 806a-n is compromised, a trigger can be sent to the cloud-based service 806a-n to end all open sessions with that individual and require a new 2FA (two-factor authentication). These examples are meant to illustrate a few methods of detecting and mitigating attacks and are not meant to be limiting in any way.

FIG. 10 is an illustration of an example architecture system for an enterprise-level threat mitigation server 1000 used for forced reauthentication during a perceived cybersecurity threat. Along with the features discussed above, advanced cyber decision platform 100 functions may be configured to operate as an enterprise-level threat mitigation server 1000 that utilizes logs 1003 from enterprise computing systems, network devices, and servers, baseline network usage profiles (not shown—see FIG. 4-7), and external vulnerability databases 805a-n to mitigate cybersecurity threats. An enterprise-level threat mitigation server 1000 may comprise a software agent residing on each enterprise computing device 1001a-n that relays enterprise-level event and security operating system logs 1003 over the enterprise network to the an enterprise-level threat mitigation server 1000 for monitoring and detection of anomalous events that may be cybersecurity attacks. The software agent may also relay other system information (hardware profiles, operating system configurations, etc.) to the enterprise-level threat mitigation server 1000. However, according to various embodiments, software agents need not be installed on networked computing devices 1001a-n, as other protocols such as SNMP and other management protocols and software suites may be used. Additionally, the enterprise-level threat mitigation server 1000 may generate and maintain a baseline network usage profile of the enterprise computing devices 1001a-n, as described in FIG. 4-7. The enterprise-level threat mitigation server 1000 also retrieves and ingests known exploits—including zero-day exploits—from a plurality of vulnerability databases 805a-n, such as ISS X-Force database, Symantec/SecurityFocus BID database, Open Source Vulnerability Database (OSVDB), Common Vulnerabilities and Exposures (CVE) database, and the National Vulnerability Database (NVD). The enterprise-level threat mitigation server 1000 may also use API functions with services such as Have I Been Pwned to determine if one or more accounts or services used on computing devices 1001a-n have been compromised in a data breach.

The enterprise-level threat mitigation server 1000 continuously compares the stream of logs 1003, system information, and baseline network usage profiles to the vulnerability and breach database information 805a-n to detect both potential and active cybersecurity attacks.

Upon detection of a perceived attack, the enterprise-level threat mitigation server 1000 may take immediate remedial action to subvert an ongoing attack or to prevent a future attack on any at-risk or compromised computing device 1001a-n via a software agent, a terminal command, executing a batch file, isolating network nodes by creating new VLANs on routers, new firewall configurations, or other remedial courses according to the severity and urgency of the attack. For example, if the computing device is a Microsoft Windows machine operating in a Microsoft Active Directory environment, a 4672 event indicates a possible pass-the-hash or other elevation of privilege attack, such as using a tool like Mimikatz. Combined with event 4624—which shows when a user has logged into an account—the enterprise-level threat mitigation server 1000 will recognize this as typical of an attack and will take immediate action to ensure that such attack is subverted.

In such an event, the enterprise-level threat mitigation server 1000 may take any course of action in previous embodiments, but more specifically in an enterprise environment, the enterprise-level threat mitigation server 1000 will trigger the relevant enterprise authentication service 1002a-n, e.g., Kerberos. The various embodiments described herein pose many exemplary attack mitigation solutions but does not cover all possible solutions known within the art.

In another example, a server 1001a-n operating on the network may have the Java Log4j vulnerability. This vulnerability would be immediately detected by the enterprise-level threat mitigation server 1000 after an iterative retrieval of vulnerabilities from at least one of the vulnerability databases, upon which the remediation of updating Java to the latest version may be taken as the remedial action in the form of pushing the update through a WSUS (Windows updates services) server, a silent install over the network, or isolating the at-risk server until other interventions may be made.

The enterprise-level threat mitigation server 1000 may also comprise an API or use APIs from cloud-based services 806a-n such that in the event that a customer of both the enterprise-level threat mitigation server 1000 and some cloud-based service 806a-n is compromised, a trigger can be sent to the cloud-based service 806a-n to end all open sessions with that individual and require a new 2FA (two-factor authentication). These examples are meant to illustrate a few methods of detecting and mitigating attacks and are not meant to be limiting in any way.

FIG. 11 is a flow chart of an example method for securing one or more computing systems or networks by forcing reauthentication during a perceived cybersecurity threat. A computer or multiple computer's event logs (Information, Warning, Error, Success Audit (Security Log) and Failure Audit (Security Log), etc.) are received by a threat mitigation module or server 1101 along with establishing and monitoring a baseline usage profile 1102. The baseline usage profile may comprise both a network usage profile disclose above and a system usage profile generated and used in the same manner as the network usage profile. Those with ordinary skill in the art will appreciate that system usage profiles may be profiling application use, attempts to access configuration or registry settings, among other categorical uses.

A threat mitigation module or server may retrieve a dataset of cyber vulnerabilities from a plurality of vulnerability databases 1103. The dataset comprising information such as descriptions, severity levels, references to advisories, solutions, and tools, weakness enumerations, known affected software configurations, change histories, and more.

A threat mitigation module or server then identifies a potential or ongoing cybersecurity threat on the computing device by comparing events within the event logs against the dataset of cyber vulnerabilities 1104. For example, determining the version of Java installed and comparing that with the versions of Java affected by the Log4j vulnerability. If and when a vulnerability is found, depending on the configuration of the threat mitigation module or server, a forced reauthentication may be triggered. For example, revoking the system credentials for the java executable. A second example is a compromised application on an ANDROID device, wherein a threat mitigation module or server may revoke system write privileges of the offending application.

Detailed Description of Exemplary Aspects

FIG. 6 is a flow chart of an example method 600 to grant user access to a server used in

various embodiments of the invention. At an initial step 603, a user requests access from a server. At step 606, the server requests login credentials from the user. At step 609, if the credential check fails, the connection is terminated at step 612. The server may be configured to institute a certain lockout period after a set number of failed attempts. If the login is successful at step 609, the server dynamically determines a required verification score required before the user can access the server at step 615. The score may be based on, for example, origin of the user connection, whether the connection is determined to be anomalous, security-level of the resource or resources requested by the user, and the like. Other criteria may be found discussed above. At step 618, a plurality of verification methods may be used to verify the user, which may be initiated by either the user or the server, depending on the method used. If the verification is unsuccessful at step 621, the user's connection is terminated at step 612. The verification may fail, for example, if a preset timeout period has been exceeded, or the server has determined that the user attempting to access the server is actually a malicious party. A lockout period may also be instituted here that may take affect after a preset number of attempts. If the verification is successful at step 621, the user is granted access at step 624.

FIG. 7 is a flow chart of an example method 700 for increasing a user's verification score as used in various embodiments of the invention. Method 700 may be viewed as a more in-depth description of steps 615, 618, and 624 from method 600. At an initial step 703, the server dynamically determines a required verification score before the user may access the server. As mentioned above, the amount of score required may be based on such metrics as origin of the user's connection, whether the access request is determined to be anomalous by the server based on a predetermined network baseline, accessing files or drives with a higher-level security assignment, and the like. Other metrics are discussed above. At step 706, the server may request additional verification from the user. At step 709, a plurality of verification methods may be initiated by the user, such as, biometric scan on the user's device, employee badge scan, voice recognition, or the like. At step 712, a plurality of verification methods may be initiated by the server, such as, requesting confirmation from trusted and untrusted parties, sending a drone for visual confirmation, analyzing the user's connection and devices, and the like. Steps 709 and 712 may be executed in parallel, or one of the steps may not be executed at all, depending on the situation. At step 715, the system does a check to see whether the user has collected enough verification points. If not, the flow chart loops back to execute steps 709 and/or 712 again. If the score has reached the required level, the user is granted access at step 718.

For brevity, method 700 does not include a fail step for during verification of the user, but it should be understood that various methods may be instituted that may cause the user to fail the verification check, and, thusly, denied access. For example, if a video or picture submitted during the verification process that shows that an unknown party is attempting to masquerade as another user, the verification check may fail immediately. Another method, for example, may be to institute a timeout period to give the user a limited amount of time to earn enough verification points.

Hardware Architecture

In one aspect, computing device 10 includes one or more central processing units (CPU) 12, one or more interfaces 15, and one or more busses 14 (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one aspect, a computing device 10 may be configured or designed to function as a server system utilizing CPU 12, local memory 11 and/or remote memory 16, and interface(s) 15. In at least one aspect, CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some aspects, processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10. In a particular aspect, a local memory 11 (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU 12. However, there are many different ways in which memory may be coupled to system 10. Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.

Although the system shown in FIG. 12 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices. In one aspect, a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided. In various aspects, different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory 11) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example. Memory 16 or memories 11, 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.

In some aspects, systems may be implemented on a standalone computing system. Referring now to FIG. 13, there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system. Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24. Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operating systems, some variety of the Linux operating system, ANDROID™ operating system, or the like. In many cases, one or more shared services 23 may be operable in system 20, and may be useful for providing common services to client applications 24. Services 23 may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21. Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software. Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to FIG. 12). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to FIG. 14, there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network. According to the aspect, any number of clients 33 may be provided. Each client 33 may run software for implementing client-side portions of a system; clients may comprise a system 20 such as that illustrated in FIG. 13. In addition, any number of servers 32 may be provided for handling requests received from one or more clients 33. Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31, which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other). Networks 31 may be implemented using any known network protocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services 37 may take place, for example, via one or more networks 31. In various aspects, external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications 24 are implemented on a smartphone or other electronic device, client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.

FIG. 15 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein. Central processor unit (CPU) 41 is connected to bus 42, to which bus is also connected memory 43, nonvolatile memory 44, display 47, input/output (I/O) unit 48, and network interface card (NIC) 53. I/O unit 48 may, typically, be connected to keyboard 49, pointing device 50, hard disk 52, and real-time clock 51. NIC 53 connects to network 54, which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system 40 is power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46. Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications, for example Qualcomm or Samsung system-on-a-chip (SOC) devices, or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices).