Abstract: Approaches to deactivating evasive malware. In an approach, a computer system installs an imitating resource in the computer system and the imitating resource creates an imitating environment of malware analysis, wherein the imitating resource causes the evasive malware to respond to the imitating environment of the malware analysis as to a real environment of the malware analysis. In the imitating environment of malware analysis, the evasive malware determines not to perform malicious behavior. In another approach, a computer system intercepts a call from the evasive malware to a resource on the computer system and returns a virtual resource to the call, wherein in the virtual resource one or more values of the resource on the computer system are modified.

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: 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 malicious servers. The method includes analyzing network traffic data to generate a main similarity measure and a secondary similarity measure for each server pair found in the network traffic data, extracting a main subset and a secondary subset of servers based on the main similarity measure and the secondary similarity measure, identifying a server that belongs to the main subset and the secondary subset, and determining a suspicious score of the server based on at least a first similarity density measure of the main subset, a second similarity density measure of the secondary subset, and a commonality measure of the main subset and the secondary subset.