Systems and methods for detecting and thwarting attacks on an IT environment

Systems and methods for detecting and thwarting attacks on a computing system. The methods comprise: collecting timestamped data from different software products comprising a unified end point management product, an SBC/ADV product, an application delivery controller product, a content collaboration product, and/or a software defined WAN product; analyzing the collected timestamped data to determine if an observed user behavior matches a learned normal user behavior of an authorized user associated with a user account; determining a risk classification level associated with a credential used by a user to log into the user account, when the observed user behavior does not match the learned normal user behavior of the authorized user; and causing at least one security related action to be performed when the risk classification level is greater than a threshold level or the risk classification level is one of a top N highest risk classification levels.

BACKGROUND

Statement of the Technical Field

The present disclosure relates generally to computing systems. More particularly, the present disclosure relates to implementing systems and methods for detecting and thwarting attacks on an Information Technology (“IT”) environment.

Description of the Related Art

There are manner techniques for comprising the security of computing systems.

Many methods for thwarting such attacks on computing systems have been derived. However, these conventional methods are not desirably effective in certain scenarios.

SUMMARY

The present disclosure concerns implementing systems and methods for detecting and thwarting attacks on a computing system. The methods comprise collecting, by a first computing device (e.g., a server), timestamped data from a plurality of different software products. The plurality of different software products include, but are not limited to, a unified end point management product, a Server Based Computing (“SBC”) and App and Desktop Virtualization (“ADV”) product, an application delivery controller product, a content collaboration product, and/or a software defined wide area network product. The timestamped data can specify a newly observed user behavior, the second computing device's location, the second computing device's unique identifier, and/or a device type. The newly observed user behavior is defined by a type of network the second computing device is connecting from, a type of input device being used by a user of the second computing device, a type of user-software interaction, and/or a type of action caused by the user-software interaction.

The first computing device analyzes the collected timestamped data to determine if an observed user behavior matches a learned normal user behavior of an authorized user associated with a user account. A risk classification level is determined when the observed user behavior does not match the learned normal user behavior of the authorized user. The risk classification level is associated with a credential used by a user of a second computing device (e.g., a mobile phone or other client device) to log into the user account. At least one security related action is caused to be performed by the first computing device or the second computing device when the risk classification level is greater than a threshold level or the risk classification level is one of a top N (e.g., 5) highest risk classification levels.

In some scenarios, the analysis of the collected timestamped data involves increasing a numerical risk value when (a) the second computing device's unique identifier is a black listed or unusual unique identifier, (b) the credential is being used from two distant geographic locations at the same time, (c) the credential is being used again from a different geographic location within a given time period from a last use of the credential, (d) the second computing device is a rooted or jail-broken device, (d) the second computing device is a non-enterprise issued device, and/or (e) data is being provided to a peripheral device of the second computing device. The risk classification level is determined based on the numerical risk value.

The security related action includes, but is not limited to, disconnecting the second computing device from the enterprise system, presenting a multi-factor challenge to the user of the second computing device, disconnecting the session established with the second computing device, activating session recording for the second computing device, and/or alerting an administrator. Notably, another user's use of the credential to remain logged into the user account via a third computing device is unaffected by the security related action(s).

DETAILED DESCRIPTION

The present solution may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the present solution is, therefore, indicated by the appended claims rather than by this detailed description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

The present document concerns systems and methods for detecting and thwarting attacks on a computing system by an unauthorized individual who is using compromised login credentials of an authorized individual (e.g., via phishing). Data is collected from a plurality of software products. The collected data is used to differentiate a normal user behavior from a malicious attacker trying to steal data. Also, once a user credential is understood to be at a high risk, at least one action is triggered to thwart the malicious attack and increase the vigilance on other high risk user credentials.

The present solution has many novel features. The manner in which data is emitted by different software products is correlated to infer if user credentials are compromised. For example, the following operations are correlated with each other: a user login via a gateway; Global Positioning System (“GPS”) from a client computing device; cloud-based remote application launches; user behavior for interacting with hosted applications; abnormal file copy using Universal Serial Bus (“USB”) redirection; and abnormal file downloads via file sharing.

Although there exist conventional techniques for user risk scores, the present solution uses a unique combination of data and sequences of events to quantify a risk profile for a given username. The present solution has the ability to detect and take actions to thwart attacks on IT environments. These actions include, but are not limited to, sending alerts to administrators for further action; triggering session recordings; logging off users from hosted applications; and remotely wiping data from end user devices. This ability adds value to customers, such as business entities and end users.

Referring now toFIG. 1, there is provided an illustration of an illustrative system100. System100implements methods for detecting and thwarting malicious attacks on computing devices. In this regard, system100comprises end user infrastructure130and cloud or on-premises infrastructure132. The end user infrastructure130can be associated with a customer, such as a business organization (e.g., a hospital or real estate firm). The customer has a plurality of end users102. Each end user can include, but is not limited to, an employee. Each end user102uses one or more Computing Devices (“CDs”)1041. . . , or104Nfor a variety of purposes, such as accessing and using software programs made available via cloud services provided by a cloud service provider. In this regard, each of the CDs1041-104Nincludes, but is not limited to, a smart phone, a smart watch, a portable computer, a personal digital assistant, a tablet computer, a desktop computer, and/or laptop computer. The CDs1041-104Nare configured to facilitate access to applications and virtual desktops without interruptions resulting from connectivity loss. Accordingly, the CDs1041-104Nhave installed thereon and execute various software applications. These software applications include, but are not limited to, Web Browsers1161-116N, Web Receivers1181-118N, electronic mail applications, and/or editor applications. Each of the listed types of applications are well known in the art, and therefore will not be described herein. Any known or to be known software application can be used herein without limitation.

In some scenarios, the Web Receivers1181-118Ncan respectively include, but are not limited to, Citrix Receivers available from Citrix Systems, Inc. of Florida and Citrix Receivers for a web site available from Citrix Systems, Inc. of Florida. Citrix Receivers comprise client software that is required to access applications and full desktops hosted by servers remote from client devices (e.g., CDs). The present solution is not limited in this regard.

The CDs1041-104Nalso have various information stored internally. This information includes, but is not limited to, account records1201-120N. The CDs1041-104Nare able to communicate with each other via an Intranet and with external devices via the Internet. The Intranet and Internet are shown inFIG. 1as a network106.

The external devices include one or more application delivery controllers134and/or servers108located remotely from the CDs (e.g., at a cloud service provider facility). Application delivery controllers are well known in the art, and therefore will not be described herein. The server(s)108is(are) configured to facilitate access to applications and virtual desktops without interruptions resulting from connectivity loss. Accordingly, the server108has installed thereon and executes various software applications. The software applications include, but are not limited to, a StoreFront and a Desktop Delivery Controller (“DDC”). StoreFronts and DDCs are well known in the art, and therefore will not be described herein. Any known or to be known StoreFront and/or DDC can be employed herein.

The server108is also configured to access the datastore110in which various information160is stored, and is also able to write data to and read data from the datastore(s)110. The various information160includes, but is not limited to, software applications, code, media content (e.g., text, images, videos, etc.), user account information, user authentication information (e.g., a user name and/or facial feature information), machine learning algorithms, and/or machine learning models.

During operation, an unauthorized individual152surreptitiously obtains credentials of an end user102(e.g., an employee). The individual152tries to access an enterprise environment using the compromised credentials in order to steal sensitive and confidential information from the enterprise. The individual152logs into the enterprise environment using a computing device150located at location A (e.g., Romania) and accesses at least one secure software product. The individual152then causes data to be downloaded to a USB drive of the computing device150.

The end user102is unaware of this identity theft. The end user102is online from a customer site located at location B (e.g., Brazil) at the same time as the unauthorized individual152. The end user102is simultaneously accessing enterprise resources using the compromised login credentials.

The present solution provides a way to differentiate the two users102,152and prevent the malicious attack on the enterprise environment. In this regard, the present solution uses data from a plurality of software products to sense the malicious attack and systematically raise a user-risk profile based on triggers from across the software products.

As noted above, the unauthorized individual152is located at location A (e.g., Romania) while the end user102is located at location B (e.g., Brazil). When both individuals access the same software product (e.g., a secure mail) at the same time, the computing devices'150,1041locations are tracked by a remote server108. The computing devices'150,1041locations are tracked based on GPS information and/or unique addresses of end points used by the end user102and the individual152. Based on the tracked locations, the remote server108determines that the same user credential (e.g., username) is being used from two distant geographical locations with a given time period. If the tracked location information indicates that the distance between computing devices150,1041exceeds a threshold value (e.g., a difference in latitude >20°) within a given time period (e.g., an hour), then the server108raises the risk profile level or value associated with the user credential (e.g., username). The server108further raises the risk profile level or value associated with the user credential (e.g., username) if: the computing device150comprises a rooted or jail-broken device; the computing device1041comprises a company-issued computing device and the computing device150comprises a non-company-issued computing device; and/or the computing device150performs operations to copy a given number of files to a USB drive connected thereto. Each time the risk profile level or value is increased, the server108causes certain actions to be taken. Alternatively or additionally, the server108causes certain security related actions to be taken when the risk profile level or value exceeds a threshold level or value.

Each user102,152is classified into one of a plurality of risk categories (e.g., low, medium or high) based on his(her) behavior. A machine learning algorithm (e.g., a Support Vector Machine (“SVM”) based algorithm) is employed here to learn behavior patterns associated with each risk category. Machine learning algorithms are well known in the art, and therefore will not be described herein. Any known or to be known machine learning algorithm can be used herein without limitation. The machine learning algorithm is trained with how to detect a series of events for a user, what weight each event carries, and how multiple of these events impact the risk. More specifically, the machine learning algorithm is trained with a list of triggers to classify the risk into each of the risk categories. For example, the following series of events triggers a risk classification of high: a logon from an unusual location followed by a copy over USB. The present solution is not limited to the particulars of this example.

The security related actions are performed in order to preempt or minimize damage from a malicious attack, raise vigilance, and/or provide alerts of possible malicious attacks (while the authorized user102continues to work with little or no interruption). As the risk profile threshold is breached, multiple actions are triggered to increase the security around the compromised credential (e.g., username). These actions include, but are not limited to: terminating active sessions; adding the compromised credential (e.g., username) to a special group that mandates multi-factor authentication of the end user(s) (e.g., via smart card technology or biometric based technology); alerting an administrator about the risk profile breach (e.g., via an email); prompting any end user using the compromised credential (e.g. username) to re-login using the multi-factor authentication; and/or activating session recording for the username and/or other risky users within the enterprise environment. Thus, by pooling data about events, check and hints from across different software products, the present solution is able to prevent any damage due to compromised credentials without disrupting the productivity of the actual authorized user102.

Referring now toFIG. 2, there is provided an illustration of an exemplary architecture for an Mobile Communication Device (“MCD”)200. CDs1041-104Nand/or150ofFIG. 1can be the same as or similar to MCD200. As such, the discussion of MCD200is sufficient for understanding CDs1041-104Nand/or150ofFIG. 1.

MCD200may include more or less components than those shown inFIG. 2. However, the components shown are sufficient to disclose an illustrative embodiment implementing the present solution. Some or all of the components of the MCD200can be implemented in hardware, software and/or a combination of hardware and software. The hardware includes, but is not limited to, one or more electronic circuits. The electronic circuits can include, but are not limited to, passive components (e.g., resistors and capacitors) and/or active components (e.g., amplifiers and/or microprocessors). The passive and/or active components can be adapted to, arranged to and/or programmed to perform one or more of the methodologies, procedures, or functions described herein.

As noted above, the MCD200can include, but is not limited to, a notebook computer, a personal digital assistant, a cellular phone, a mobile phone with smart device functionality (e.g., a Smartphone), and/or a wearable device with smart device functionality (e.g., a smart watch). In this regard, the MCD200comprises an antenna202for receiving and transmitting Radio Frequency (“RF”) signals. A receive/transmit (“Rx/Tx”) switch204selectively couples the antenna202to the transmitter circuitry206and the receiver circuitry208in a manner familiar to those skilled in the art. The receiver circuitry208demodulates and decodes the RF signals received from an external device. The receiver circuitry208is coupled to a controller (or microprocessor)210via an electrical connection234. The receiver circuitry208provides the decoded signal information to the controller210. The controller210uses the decoded RF signal information in accordance with the function(s) of the MCD200. The controller210also provides information to the transmitter circuitry206for encoding and modulating information into RF signals. Accordingly, the controller210is coupled to the transmitter circuitry206via an electrical connection238. The transmitter circuitry206communicates the RF signals to the antenna202for transmission to an external device via the Rx/Tx switch204.

The MCD200also comprises an antenna240coupled to a Short Range Communications (“SRC”) transceiver214for receiving SRC signals. SRC transceivers are well known in the art, and therefore will not be described in detail herein. However, it should be understood that the SRC transceiver214processes the SRC signals to extract information therefrom. The SRC transceiver214may process the SRC signals in a manner defined by the SRC application254installed on the MCD200. The SRC application254can include, but is not limited to, a Commercial Off the Shelf (“COTS”) application (e.g., a Bluetooth application). The SRC transceiver214is coupled to the controller210via an electrical connection236. The controller uses the extracted information in accordance with the function(s) of the MCD200.

The controller210may store received and extracted information in memory212of the MCD200. Accordingly, the memory212is connected to and accessible by the controller210through electrical connection242. The memory212may be a volatile memory and/or a non-volatile memory. For example, memory212can include, but is not limited to, a Random Access Memory (“RAM”), a Dynamic RAM (“DRAM”), a Read Only Memory (“ROM”) and a flash memory. The memory212may also comprise unsecure memory and/or secure memory. The memory212can be used to store various other types of data260therein, such as authentication information, cryptographic information, location information, and various work order related information.

The MCD200also may comprise a barcode reader232. Barcode readers are well known in the art, and therefore will not be described herein. However, it should be understood that the barcode reader232is generally configured to scan a barcode and process the scanned barcode to extract information therefrom. The barcode reader232may process the barcode in a manner defined by the barcode application256installed on the MCD200. Additionally, the barcode scanning application can use camera218to capture the barcode image for processing. The barcode application256can include, but is not limited to, a COTS application. The barcode reader232provides the extracted information to the controller210. As such, the barcode reader232is coupled to the controller210via an electrical connection260. The controller210uses the extracted information in accordance with the function(s) of the MCD200. For example, the extracted information can be used by MCD200to enable user authentication functionalities thereof.

As shown inFIG. 2, one or more sets of instructions250are stored in memory212. The instructions may include customizable instructions and non-customizable instructions. The instructions250can also reside, completely or at least partially, within the controller210during execution thereof by MCD200. In this regard, the memory212and the controller210can constitute machine-readable media. The term “machine-readable media”, as used herein, refers to a single medium or multiple media that stores one or more sets of instructions250. The term “machine-readable media”, as used here, also refers to any medium that is capable of storing, encoding or carrying the set of instructions250for execution by the MCD200and that causes the MCD200to perform one or more of the methodologies of the present disclosure.

The controller210is also connected to a user interface230. The user interface230comprises input devices216, output devices224and software routines (not shown inFIG. 2) configured to allow a user to interact with and control software applications (e.g., software applications252-256and other software applications) installed on the MCD200. Such input and output devices may include, but are not limited to, a display228, a speaker226, a keypad220, a directional pad (not shown inFIG. 2), a directional knob (not shown inFIG. 2), a microphone222, a touch screen298, and a camera218. The display228may be designed to accept touch screen inputs. As such, user interface230can facilitate a user software interaction for launching applications (e.g., applications252-260and other software applications) installed on the MCD200. The user interface230can facilitate a user-software interactive session for: initiating communications with an external device; writing data to and reading data from memory212; and/or initiating user authentication operations for authenticating a user (e.g., such that a remote session between a nearby client computing device and a remote cloud service server).

The display228, keypad220, directional pad (not shown inFIG. 2) and directional knob (not shown inFIG. 2) can collectively provide a user with a means to initiate one or more software applications or functions of the MCD200. The application software252-260can facilitate the data exchange between (a) a user and the MCD200, and/or (b) the MCD200and another device. In this regard, the application software252-260performs one or more of the following: facilitate verification of that the user of the MCD200is an authorized user via a one-factor or a multi-factor authentication process; present information to the user indicating that (s)he is or is not authorized to use the resource; and/or perform actions to prevent any damage to an enterprise system due to compromised user credentials (e.g., a username).

Referring now toFIG. 3, there is provided an illustration of an exemplary architecture for a computing device300. CDs1041-104N,152and/or server(s)108ofFIG. 1(is)are the same as or similar to server300. As such, the discussion of computing device300is sufficient for understanding these components of system100.

Computing device300may include more or less components than those shown inFIG. 3. However, the components shown are sufficient to disclose an illustrative solution implementing the present solution. The hardware architecture ofFIG. 3represents one implementation of a representative computing device configured to enable watermarking of graphics, as described herein. As such, the computing device300ofFIG. 3implements at least a portion of the method(s) described herein.

Some or all the components of the computing device300can be implemented as hardware, software and/or a combination of hardware and software. The hardware includes, but is not limited to, one or more electronic circuits. The electronic circuits can include, but are not limited to, passive components (e.g., resistors and capacitors) and/or active components (e.g., amplifiers and/or microprocessors). The passive and/or active components can be adapted to, arranged to and/or programmed to perform one or more of the methodologies, procedures, or functions described herein.

As shown inFIG. 3, the computing device300comprises a user interface302, a Central Processing Unit (“CPU”)306, a system bus310, a memory312connected to and accessible by other portions of computing device300through system bus310, and hardware entities314connected to system bus310. The user interface can include input devices and output devices, which facilitate user-software interactions for controlling operations of the computing device300. The input devices include, but are not limited, a physical and/or touch keyboard350. The input devices can be connected to the computing device300via a wired or wireless connection (e.g., a Bluetooth® connection). The output devices include, but are not limited to, a speaker352, a display354, and/or light emitting diodes356.

At least some of the hardware entities314perform actions involving access to and use of memory312, which can be a Random Access Memory (“RAM”), a disk driver and/or a Compact Disc Read Only Memory (“CD-ROM”). Hardware entities314can include a disk drive unit316comprising a computer-readable storage medium318on which is stored one or more sets of instructions320(e.g., software code) configured to implement one or more of the methodologies, procedures, or functions described herein. The instructions320can also reside, completely or at least partially, within the memory312and/or within the CPU306during execution thereof by the computing device300. The memory312and the CPU306also can constitute machine-readable media. The term “machine-readable media”, as used here, refers to a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions320. The term “machine-readable media”, as used here, also refers to any medium that is capable of storing, encoding or carrying a set of instructions320for execution by the computing device300and that cause the computing device300to perform any one or more of the methodologies of the present disclosure.

Referring now toFIG. 4, there is shown a flow diagram of an illustrative method400for detecting and thwarting attacks on a computing system. Method400comprises a plurality of blocks. The present solution is not limited to the order of the blocks shown inFIG. 4. The operations of the blocks can be performed in a different order (than that shown) in accordance with a given application. For example, blocks410-416are shown as being performed in parallel with or at the same time as blocks404-408. The present solution is not limited in this regard. Blocks410-416can be performed within a given time period from the last use of resources accessed using the user account (e.g., a logging out of the user account upon completion of408).

As shown inFIG. 4A, method400begins with402and continues with404-408.404involves receiving by a first CD (e.g., CD1041ofFIG. 1) a first user-software interaction for logging into a user account using at least one credential. For example, the user of the first CD uses a username (e.g., asmith) and/or a password (e.g., $work123) to log into a user account. The present solution is not limited to the particulars of this example. The first user-software interaction can be achieved using an input device (e.g., input device216ofFIG. 2or input device350ofFIG. 3) of the first CD.

Once logged into the user account, the user of the first CD is allowed access to enterprise resources (e.g., a secure mail software program), as shown by406. First timestamped data is collected in408that specifies newly observed user behavior, the first CD's location (e.g., a company building in Brazil), the first CD's unique identifier (e.g., IP address), and/or a device type (e.g., a company issued device). The user behavior data can include but is not limited to, data indicating which type of network the first CD is connecting from, data indicating the type of input device (e.g., a mouse, touch screen or keyboard) being used by the user, data indicating the type is user-software interaction (e.g., keyboard stroke mouse click, swipe gesture, etc.), and/or data indicating the type of action caused by the user-software interaction (e.g., file transfer, file share, print, download, scanning, etc.). The collected first timestamped data may be communicated to a remote server (e.g., server108ofFIG. 1for processing and/or storage in a datastore110ofFIG. 1).

All or some of the operations of blocks410-416are performed at the same time as404-408or within a given time period from the first CD's user logging out of the user account (which can occur in block408).410involves receiving by a second CD (e.g., CD150ofFIG. 1) a second user-software interaction for logging into the user account using the credential(s) which has(have) been compromised. As noted above, the credential(s) can include, but is(are) not limited to, a username and/or a password. The second user-software interaction can be achieved using an input device (e.g., input device216ofFIG. 2or input device350ofFIG. 3) of the second CD. In optional414, data is downloaded to a peripheral device (e.g., a USB drive, a printer, a scanner, etc.) coupled to the second CD. Second timestamped data is collected in416that specifies a newly observed user behavior, the second CD's location (e.g., a home in Romania), the second CD's unique identifier (e.g., IP address), and/or a device type (e.g., a rooted/jail-broken device and/or a non-company issued device). The user behavior data can include, but is not limited to, data indicating which type of network the second CD is connecting from, data indicating the type of input device (e.g., a mouse, touch screen or keyboard) being used by the user, data indicating the type is user-software interaction (e.g., keyboard stoke, mouse click, swipe gesture, etc.), and/or data indicating the type of action caused by the user-software interaction (e.g., file transfer, file share, print, download, scanning, etc.). The collected second timestamped data may be communicated to a remote server (e.g., server108ofFIG. 1for processing and/or storage in a datastore110ofFIG. 1).

Notably, the first and second timestamped data is collected using a plurality of different software programs or products. The different software programs or products include, but are not limited to, a unified end point management product (e.g., XenMobile available from Citrix of Fort Lauderdale, Fla.), an SBC/ADV product (e.g., XenApp and XenDesktop available from Citrix of Fort Lauderdale, Fla.), an application delivery controller product (e.g., Netscaler or Netscaler Gateway available from Citrix of Fort Lauderdale, Fla.), a content collaboration product (e.g., ShareFile available from Citrix of Fort Lauderdale, Fla.), and/or a software defined wide area network product (e.g., NetScaler SD-WAN available from Citrix of Fort Lauderdale, Fla.).

Subsequently in418, the first and second timestamped data is analyzed, for example, by a remote server (e.g., server108ofFIG. 1) using a machine learning algorithm. Machine learning algorithms are well known in the art, and therefore will not be described in detail herein. Any known or to be known machine learning algorithm can be used herein. For example, a binary classification based machine learning algorithm and/or a clustering based machine learning algorithm is(are) employed here. The machine learning algorithm(s) is(are) stored in a location memory (e.g., memory312ofFIG. 3) of the server or in a remote datastore (e.g., datastore110ofFIG. 1).

The machine learning algorithm is trained with normal behavior patterns for an authorized user associated with the user account, as well as abnormal behavior patterns suggesting that the enterprise system may be at risk of a malicious attack. The analysis is performed to detect when abnormal behavior is occurring that requires a security related action to be performed to protect an enterprise system from a malicious attack. In this regard, the analysis involves determining if certain criteria or criterion is(are) met that puts the security of an enterprise system at risk from malicious attacks. In the latter case, each criteria has a particular weight assigned thereto so that two or more of the criterion are not considered with the same level of importance when determining a risk classification level for the credential(s). The criterion include, but are not limited to, type of user behavior, the deviation of newly observed user behavior from known normal user behavior, type of traffic between an end user device and a server, end user device locations, time of user account logins/logouts, distance between two devices used to simultaneously or sequentially access resources via the same credential(s), and/or the type of devices used to simultaneously or sequentially access resources via the same credential(s).

In some scenarios, the analysis is performed to: (a) determine if the user behavior associated with the first CD and/or second CD match(es) a learned normal user behavior for an authorized user associated with the user account, (b) determine if the first or second CD's unique identifier is a black listed unique identifier or an unusual unique identifier, (c) determine if the same credential(s) (e.g., username and/or password) is(are) being used from two distant geographic locations at the same time, and/or (d) determine whether the credential(s) is(are) being used again from a different geographic location within a given time period from the last use.

The learned normal user behavior is made of multiple components with one of those being the pattern the machine learning training model has built from how the user uses the device (e.g., swipes, typing, etc.). Another component of the learned normal user behavior is the location and time of day (and days of the week) the user normally uses a particular device. Other components are combined when determining what is a normal place and time of usage. For example, a typical normal user behavior can be a user who uses a particular device (1) from an office location on non-holiday weekdays during the daytime hours, (2) from home during evenings, weekends and/or holidays. In this case, the place and time components are combined in the determination of normal user behavior relating to those components.

If the user behavior matches a learned normal user behavior [420:YES], then422is performed where method400continues with450ofFIG. 4B.450generally involves determining a risk classification level (e.g., low, medium, or high) associated with the credentials. The risk classification level can be obtained from a datastore (e.g., datastore110ofFIG. 1).450and the following operations thereof will be described in detail below.

If the user behavior does not match learned normal user behavior [420:NO], then method400continues with424. If at least one of the first and second CD's unique identifier is a black listed or unusual unique identifier [424:YES], then method400continues with432ofFIG. 4Bso that a risk level value associated with the credential(s) is raised by a first amount. If neither of the first and second CD's unique identifier is a black listed or unusual unique identifier [424:NO], then method400continues with426.

If the credential(s) is(are) being used from two distant geographic locations at the same time [426:YES], then method400continues with432ofFIG. 4Bso that a risk level value associated with the credential(s) is raised by a second amount (which is the same or different than the first amount). If the credential(s) is(are) not being used from two distant geographic locations at the same time [426:NO], then method400continues with428.

If the credential(s) is(are) not being used again from a different geographic location within a given time period from the last use [428:NO], then422is performed where method400continues with450ofFIG. 4B.450generally involves determining a risk classification level (e.g., low, medium, or high) associated with the credentials. The risk classification level can be obtained from a datastore (e.g., datastore110ofFIG. 1).450and the following operations thereof will be described in detail below.

If the credential(s) is(are) being used again from a different geographic location within a given time period (e.g., 1 hour) from the last use [428:YES], then430is performed to determine if the distance between the two different geographic locations exceeds a threshold distance (e.g., 1500 miles). If not [430:NO], then422is performed where method400continues with450ofFIG. 4Bso that a risk classification level (e.g., low, medium, or high) associated with the credentials is obtained. If so [430:YES], then method400continues with432ofFIG. 4B. As shown inFIG. 4B, 432involves performing operations to raise a risk value associated with the credential(s) by a third amount (where the third amount is the same as or different than the first and/or second amount).

In some scenarios, the risk value comprises a decimal number or an integer number. The decimal or integer number can be incremented when a criteria is met as described above. Each criteria can trigger an increment by an amount that is the same as or different than that triggered by another criteria. For example, the risk value is incremented by a first amount when the first or second CD's unique identifier is a black listed unique identifier, a second amount when the credential(s) are being used from two distant geographic locations at the same time, and a third amount when the credentials are being used again from a different geographic location within a given time period from the last use and the distance between the different geographic locations exceed a threshold distance. The first, second and third amounts are the same or different. The present solution is not limited by the particulars of this example.

After432,434is performed to determine if the first or second CD is a rooted or jail-broken device. The term “rooted device”, as used herein, refers to an Android based device with an unlocked operating system that allows the removal of software restrictions imposed by an enterprise, unapproved applications to be installed thereon, and/or approved applications to be replaced and/or deleted. The term “jail-broken device, as used herein, refers to an iPhone or iPad with an unlocked operating system that allows the removal of software restrictions imposed by an enterprise, unapproved applications to be installed thereon, and/or approved applications to be replaced and/or deleted.

If so [434:YES], method400continues with440which will be discussed below. If not [434:NO],436is performed where a determination is made as to whether the first or second CD is a non-enterprise issued device. If not [436:NO], then438is performed where method400continues to450, which will be discussed below. If so [436:YES], then method continues with optional440where restrictive policies are enforced. Next in442, operations are performed to further raise the risk value associated with the credential(s) by a fourth amount (which is the same as or different than the first, second and/or third amount(s)).

Subsequently,444is performed to determine if data is being provided to a peripheral device of the first or second CD. If not [444:NO], then446is performed where method400continues to450, which will be discussed below. If so [444:YES], then448is performed where the risk value is further raised by a fifth amount (which is the same as or different than the first, second and/or third amount(s)).

Upon competing448,450is performed where a risk classification level (e.g., low, medium or high) is determined based on the risk value. For example, a low risk classification level is assigned to the credential(s) when the risk value is between 0 and 10, a medium classification level is assigned to the credential(s) when the risk value is between 10 and 20, and a high classification level is assigned to the credential(s) when the risk value is greater than 20. The present solution is not limited to the particulars of this example.

In452, the risk classification level is analyzed to determine if it is greater than a threshold level (e.g., a low or medium level) and/or is one of the top N (e.g., 5) highest levels for a given set of credential(s). If so [452:YES], then at least one security related action is taken in454. The security related action can include, but is not limited to, disconnecting the first and/or second CD from the enterprise system, presenting a multi-factor challenge to the user of the first and/or second CD, disconnect the session established with the first and/or second CD, activate session recording for any device logged in using the credential(s), and/or alerting an administrator. In the case that the administrator is alerted, method400may continue with optional456.456involves verifying that the risk classification level is correct for the username, and decrease the risk classification level if such verification is not made. Subsequently,458is performed where method400ends or other processing is performed (e.g., return to402ofFIG. 4A).

Although the present solution has been illustrated and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. In addition, while a particular feature of the present solution may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Thus, the breadth and scope of the present solution should not be limited by any of the above described embodiments. Rather, the scope of the present solution should be defined in accordance with the following claims and their equivalents.