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.