Abstract:
The present disclosure is directed towards systems and methods for detecting anomalous network traffic. Network traffic corresponding to an application executed by a server can be received. Application characteristics of the application can be identified to select an anomaly detection profile. The anomaly detection profile can be selected based on the identified application characteristics. The anomaly detection profile can include a set of detection features for the anomaly and one or more predetermined threshold values of the detection features. One or more feature values of the set of one or more detection features can be determined. An anomaly in the network traffic can be detected responsive to comparing the feature values and the predetermined threshold values of the detection features.
Abstract:
The present disclosure is directed towards systems and methods for evaluating or mitigating a network attack. A device determines one or more client internet protocol addresses associated with the attack on the service. The device assigns a severity score to the attack based on a type of the attack. The device identifies a probability of a user account accessing the service during an attack window based on the type of attack. The device generates an impact score for the user account based on the severity score and the probability of the user account accessing the service during the attack window. The device selects a mitigation policy for the user account based on the impact score.
Abstract:
The present disclosure is directed towards systems and methods for improving anomaly detection using injected outliers. A normalcy calculator of a device may include a set of outliers into a training dataset of data points. The normalcy calculator, using a K-means clustering algorithm applied on the training dataset, identify at least a first cluster of data points. The normalcy calculator of the device may determine a region with a center and an outer radius that covers at least a spatial extent of the first cluster of data points. The normalcy calculator may determine a first normalcy radius for the first cluster by reducing the region around the center until a point at which all artificial outliers are excluded from a region defined by the first normalcy radius. An outlier detector of the device may use the region defined by the first normalcy radius to determine whether a new data point is normal or abnormal.
Abstract:
The present disclosure is directed towards systems and methods for performing multi-level tagging of encrypted items for additional security and efficient encrypted item determination. A device intercepts a message from a server to a client, parses the message and identifies a cookie. The device processes and encrypts the cookie. The device adds a flag to the cookie indicating the device encrypted the cookie. The device re-inserts the modified cookie into the message and transmits the message. The device intercepts a message from a client and determines whether the cookie in the message was encrypted by the device. If the message was not encrypted by the device, the device transmits the message to its destination. If the message was encrypted by the device, the device removes the flag, decrypts the cookie, removes the tag from the cookie, re-inserts the cookie into the message and transmits the message to its final destination.
Abstract:
The disclosure is directed to a system for improving security of SSL communications. The system can include an device intermediary between one or more servers, one or more clients, a plurality of agents, and a web service. The servers can be configured to receive SSL connections and issue SSL certificates. The device can include a virtual server associated with a respective one of the servers, such that the SSL certificate of the respective server is transmitted through the device. The device can generate service fingerprints for the one or more servers. Each service fingerprint can include information corresponding to an SSL certificate of the virtual server, one or more DNS aliases for a virtual IP address of the respective virtual server, one or more port numbers serving the SSL certificate, and an IP address serviced by the device. The device also can transmit the service fingerprints to a web service.
Abstract:
The present disclosure is directed towards systems and methods for scanning of a target range of IP addresses to verify security certificates associated with the target range of IP addresses. Network traffic may be monitored between a plurality of clients and a plurality of servers over an IP address space. Traffic monitors positioned intermediary to the plurality of client and the plurality of servers can identify a target range of IP addresses in the address space for targeted scanning. The target range of IP address may be grouped into a priority queue and a scan can be performed of the target range of IP addresses to verify a security certificate associated with each IP address in the target range of IP addresses. In some embodiments, a rogue security certificate is detected that is associated with at least one IP address in the target range of IP addresses.
Abstract:
The present disclosure is directed towards systems and methods for detecting anomalous network traffic. Network traffic corresponding to an application executed by a server can be received. Application characteristics of the application can be identified to select an anomaly detection profile. The anomaly detection profile can be selected based on the identified application characteristics. The anomaly detection profile can include a set of detection features for the anomaly and one or more predetermined threshold values of the detection features. One or more feature values of the set of one or more detection features can be determined. An anomaly in the network traffic can be detected responsive to comparing the feature values and the predetermined threshold values of the detection features.
Abstract:
The present disclosure is directed towards systems and methods for improving anomaly detection using injected outliers. A normalcy calculator of a device may include a set of outliers into a training dataset of data points. The normalcy calculator, using a K-means clustering algorithm applied on the training dataset, identify at least a first cluster of data points. The normalcy calculator of the device may determine a region with a center and an outer radius that covers at least a spatial extent of the first cluster of data points. The normalcy calculator may determine a first normalcy radius for the first cluster by reducing the region around the center until a point at which all artificial outliers are excluded from a region defined by the first normalcy radius. An outlier detector of the device may use the region defined by the first normalcy radius to determine whether a new data point is normal or abnormal.
Abstract:
The present disclosure is directed towards systems and methods for evaluating or mitigating a network attack. A device determines one or more client internet protocol addresses associated with the attack on the service. The device assigns a severity score to the attack based on a type of the attack. The device identifies a probability of a user account accessing the service during an attack window based on the type of attack. The device generates an impact score for the user account based on the severity score and the probability of the user account accessing the service during the attack window. The device selects a mitigation policy for the user account based on the impact score.
Abstract:
The present disclosure provides solutions for an enterprise providing services to a variety of clients to enable the client to use the resources provided by the enterprise by modifying URLs received and the URLs from the responses from the servers to the client's requests before forwarding the requests and the responses to the intended destinations. An intermediary may identify an access profile for a clients' request to access a server via a clientless SSL VPN session. The intermediary may detect one or more URLs in content served by the server in response to the request using one or more regular expressions of the access profile. The intermediary may rewrite or modify, responsive to detecting, the one or more detected URLs in accordance with a URL transformation specified by one or more rewrite policies of the access profile. The response with modified URLs may be forwarded to the client.