Abstract: Unknown and reference signatures are accessed. The unknown and reference signatures indicate patterns that correspond to known threats to resources (such as computer systems and/or computer networks) in a computer environment and comprise a multitude of descriptive elements having information describing different aspects of a corresponding signature. A set of similarity measures is created of the unknown and reference signatures from different perspectives, each perspective corresponding to a descriptive element. The set of similarity measures are integrated to generate an overall similarity metric. The overall similarity metric is used to find appropriate categories in the reference signatures into which the unknown signatures should be placed. The unknown signatures are placed into the appropriate categories to create a mapping from the unknown signatures to the reference signatures.

Abstract: A decoder deployed in one or more terminals, includes a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to receiving a noisy message and a noisy hash from the network, searching for a pair of matching candidates for the hash and message from two row spaces of noisy message vectors using a shared secret with an encoder, and outputting, by the decoder, a decoded message if the searching is successful.

Abstract: An encoder including a computer readable storage medium storing program instructions, and a processor executing the program instructions, the processor configured to generating a message by aggregating a plurality of incoming packets, constructing an encoded message using the message and a random matrix, constructing of a hash using a shared secret, and transmitting the encoded message and the hash to a destination, through a network that performs network coding operations.

Abstract: A method (and structure) includes receiving, as input data into a computer-implemented processing procedure, at least one listing of at least one of time series data and potential candidate periods of potential beaconing activity. The input data is processed, using a processor on a computer, to evaluate the input data as if the input data represents data points of an input analog signal subject to principles of communication theory and having determinable statistical characteristics.

Abstract: A method of detecting exploit kits includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic. The HTTP traffic is clustered into a web session tree according to a client IP (Internet Protocol. A client tree structure of the web session tree is generated. The client tree structure is compared with tree structures of exploit kit samples.

Abstract: A method for detecting beaconing behavior includes preprocessing network records to identify candidate source and destination pairs for detecting beaconing behavior, where each source and destination pair is associated with a specific time interval in a plurality of time intervals forming a time range, the time interval and time range having been predefined. The activity time interval information is converted from the time domain into the frequency domain. Candidate frequencies are determined from the source and destination pairs, as likely candidate frequencies/periodicities of beaconing activities.

Abstract: A method includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic and clustering, using a processor on the computer, the HTTP traffic according to a client IP (Internet Protocol) into a web session tree.

Abstract: Identifying malicious servers is provided. Malicious edges between server vertices corresponding to visible servers and invisible servers involved in network traffic redirection chains are determined based on determined graph-based features within a bipartite graph corresponding to invisible server vertices involved in the network traffic redirection chains and determined distance-based features corresponding to the invisible server vertices involved in the network traffic redirection chains. Malicious server vertices are identified in the bipartite graph based on the determined malicious edges between the server vertices corresponding to the visible servers and invisible servers involved in the network traffic redirection chains. Access by client devices is blocked to malicious servers corresponding to the identified malicious server vertices in the bipartite graph.

Abstract: Identifying malicious servers is provided. Malicious edges between server vertices corresponding to visible servers and invisible servers involved in network traffic redirection chains are determined based on determined graph-based features within a bipartite graph corresponding to invisible server vertices involved in the network traffic redirection chains and determined distance-based features corresponding to the invisible server vertices involved in the network traffic redirection chains. Malicious server vertices are identified in the bipartite graph based on the determined malicious edges between the server vertices corresponding to the visible servers and invisible servers involved in the network traffic redirection chains. Access by client devices is blocked to malicious servers corresponding to the identified malicious server vertices in the bipartite graph.

Abstract: A method of detecting exploit kits includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic. The HTTP traffic is clustered into a web session tree according to a client IP (Internet Protocol. A client tree structure of the web session tree is generated. The client tree structure is compared with tree structures of exploit kit samples.

Abstract: A method of detecting exploit kits includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic. The HTTP traffic is clustered into a web session tree according to a client IP (Internet Protocol. A client tree structure of the web session tree is generated. The client tree structure is compared with tree structures of exploit kit samples.

Abstract: A method of detecting exploit kits includes receiving, at an input port of a computer, indication of HTTP (Hypertext Transfer Protocol) traffic. The HTTP traffic is clustered into a web session tree according to a client IP (Internet Protocol. A client tree structure of the web session tree is generated. The client tree structure is compared with tree structures of exploit kit samples.

Abstract: Identifying malicious servers is provided. Malicious edges between server vertices corresponding to visible servers and invisible servers involved in network traffic redirection chains are determined based on determined graph-based features within a bipartite graph corresponding to invisible server vertices involved in the network traffic redirection chains and determined distance-based features corresponding to the invisible server vertices involved in the network traffic redirection chains. Malicious server vertices are identified in the bipartite graph based on the determined malicious edges between the server vertices corresponding to the visible servers and invisible servers involved in the network traffic redirection chains. Access by client devices is blocked to malicious servers corresponding to the identified malicious server vertices in the bipartite graph.

Abstract: Identifying malicious servers is provided. Malicious edges between server vertices corresponding to visible servers and invisible servers involved in network traffic redirection chains are determined based on determined graph-based features within a bipartite graph corresponding to invisible server vertices involved in the network traffic redirection chains and determined distance-based features corresponding to the invisible server vertices involved in the network traffic redirection chains. Malicious server vertices are identified in the bipartite graph based on the determined malicious edges between the server vertices corresponding to the visible servers and invisible servers involved in the network traffic redirection chains. Access by client devices is blocked to malicious servers corresponding to the identified malicious server vertices in the bipartite graph.

Abstract: A method for detecting beaconing behavior includes preprocessing network records to identify candidate source and destination pairs for detecting beaconing behavior, where each source and destination pair is associated with a specific time interval in a plurality of time intervals forming a time range, the time interval and time range having been predefined. The activity time interval information is converted from the time domain into the frequency domain. Candidate frequencies are determined from the source and destination pairs, as likely candidate frequencies/periodicities of beaconing activities.

Abstract: A method for detecting beaconing behavior includes preprocessing network records to identify candidate source and destination pairs for detecting beaconing behavior, where each source and destination pair is associated with a specific time interval in a plurality of time intervals forming a time range, the time interval and time range having been predefined. The activity time interval information is converted from the time domain into the frequency domain. Candidate frequencies are determined from the source and destination pairs, as likely candidate frequencies/periodicities of beaconing activities.