Abstract: Systems and methods may simulate traffic to identify malicious activity. A dynamic analysis system comprising a processor in communication with a network may receive a copy of a malware program and load the copy of the malware program into a simulated endpoint. The system may monitor simulated endpoint network traffic to or from the simulated endpoint, assess the simulated endpoint network traffic to determine a source and/or destination for the simulated endpoint network traffic and/or content of the simulated endpoint network traffic, and capture and store metadata associated with the simulated endpoint network traffic. A comparison system may compare simulated network traffic metadata to observed network traffic metadata to determine whether the metadata are statistically similar. When the metadata are not statistically similar, the system may generate a low infection confidence score. When the metadata are statistically similar, the system may generate a high infection confidence score.

Abstract: Systems and methods of classifying network traffic may monitor network traffic. Monitored traffic may be compared with a control protocol template (CPT). When a similarity between the monitored traffic and the CPT exceeds a match threshold, the monitored traffic may be associated with the CPT.

Abstract: A method and system of detecting a malicious and/or botnet-related domain name, comprising: reviewing a domain name used in Domain Name System (DNS) traffic in a network; searching for information about the domain name, the information related to: information about the domain name in a domain name white list and/or a domain name suspicious list; and information about the domain name using an Internet search engine, wherein the Internet search engine determines if there are no search results or search results with a link to at least one malware analysis site; and designating the domain name as malicious and/or botnet-related based on the information.

Abstract: A computerized system and method for performing behavioral clustering of malware samples, comprising: executing malware samples in a controlled computer environment for a predetermined time to obtain Hypertext Transfer Protocol. HTTP traffic; clustering the malware samples into at least one cluster based on network behavioral information from the HTTP traffic; and extracting, using the at least one processor, network signatures from the HTTP traffic information for each cluster, the network signatures being indicative of malware infection.

Abstract: System and method for detecting a domain generation algorithm (DGA), comprising: performing processing associated with clustering, utilizing a name-based features clustering module accessing information from an electronic database of NX domain information, the randomly generated domain names based on the similarity in the make-up of the randomly generated domain names; performing processing associated with clustering, utilizing a graph clustering module, the randomly generated domain names based on the groups of assets that queried the randomly generated domain names; performing processing associated with determining, utilizing a daily clustering correlation module and a temporal clustering correlation module, which clustered randomly generated domain names are highly correlated in daily use and in time; and performing processing associated with determining the DGA that generated the clustered randomly generated domain names.

Abstract: Systems and methods may determine suspicious network traffic. A monitoring system comprising a processor in communication with a network may monitor network traffic to or from an asset associated with the network. The monitoring system may assess the network traffic to determine a source and/or destination for the network traffic anchor content of the network traffic. The monitoring system may determine whether the network traffic is suspicious network traffic based on the assessed source and/or destination and/or content. When the network traffic is determined to be suspicious network traffic, the monitoring system may capture metadata associated with the suspicious network traffic and store the metadata in a database in communication with the processor. When the network traffic is not determined to be suspicious network traffic, the monitoring system may disregard metadata associated with the network traffic.

Abstract: A system and method of analysis. NX domain names are collected from an asset in a real network. The NX domain names are domain names that are not registered. The real network NX domain names are utilized to create testing vectors. The testing vectors are classified as benign vectors or malicious vectors based on training vectors. The asset is then classified as infected if the NX testing vector created from the real network NX domain names is classified as a malicious vector.

Abstract: A method and system for detecting a malicious domain name, comprising: collecting domain name statistical information from a non-recursive domain name system name server (RDNS NS); and utilizing the collected domain name statistical information to determine if a domain name is malicious or benign.

Abstract: Systems and methods may use historical analysis to identify malicious activity. A discovery/recovery system may comprise a processor in communication with a network and in communication with a database. The discovery/recovery system may gather filtered historical network data associated with an asset associated with the network. The discovery/recovery system may analyze the filtered historical network data to determine whether a subset of the filtered historical network data is associated with a malware infection of the asset.

Abstract: Systems and methods of classifying network traffic may monitor network traffic. Monitored traffic may be compared with a control protocol template (CPT). When a similarity between the monitored traffic and the CPT exceeds a match threshold, the monitored traffic may be associated with the CPT.

Abstract: A system and method of analysis. NX domain names are collected from an asset in a real network. The NX domain names are domain names that are not registered. The real network NX domain names are utilized to create testing vectors. The testing vectors are classified as benign vectors or malicious vectors based on training vectors. The asset is then classified as infected if the NX testing vector created from the real network NX domain names is classified as a malicious vector.

Abstract: A system and method for determining whether at least one domain is legitimate or malicious by obtaining passive DNS query information, using the passive DNS query information to measure statistical features of known malicious domain names and known legitimate domain names, and using the statistical features to determine at least one reputation for at least one new domain, where the reputation indicates whether the at least one new domain is likely to be for malicious or legitimate uses.

Abstract: Systems and methods may use automation discovery to identify malicious activity. An automation discovery system comprising a processor in communication with a network and in communication with a database may receive potentially automated network traffic data. The system may analyze the potentially automated network traffic data to determine whether the potentially automated network traffic data is likely to be automated. When the potentially automated network traffic data is not likely to be automated, the system may generate a low automation confidence score associated with the potentially automated network traffic data. When the potentially automated network traffic data is likely to be automated, the system may generate a high automation confidence score associated with the potentially automated network traffic data.

Abstract: A computerized system and method for performing behavioral clustering of malware samples, comprising: executing malware samples in a controlled computer environment for a predetermined time to obtain HTTP traffic; clustering the malware samples into at least one cluster based on network behavioral information from the HTTP traffic; and extracting, using the at least one processor, network signatures from the HTTP traffic information for each cluster, the network signatures being indicative of malware infection.

Abstract: A method and system for detecting a malicious domain name, comprising: collecting domain name statistical information from a non-recursive domain name system name server (RDNS NS); and utilizing the collected domain name statistical information to determine if a domain name is malicious or benign.

Abstract: A system and method of analysis. NX domain names are collected from an asset in a real network. The NX domain names are domain names that are not registered. The real network NX domain names are utilized to create testing vectors. The testing vectors are classified as benign vectors or malicious vectors based on training vectors. The asset is then classified as infected if the NX testing vector created from the real network NX domain names is classified as a malicious vector.

Abstract: Systems and methods may simulate traffic to identify malicious activity. A dynamic analysis system comprising a processor in communication with a network may receive a copy of a malware program and load the copy of the malware program into a simulated endpoint. The system may monitor simulated endpoint network traffic to or from the simulated endpoint, assess the simulated endpoint network traffic to determine a source and/or destination for the simulated endpoint network traffic and/or content of the simulated endpoint network traffic, and capture and store metadata associated with, the simulated endpoint network traffic. A comparison system may compare simulated network traffic metadata to observed network traffic metadata to determine whether the metadata are statistically similar. When the metadata are not statistically similar, the system may generate a low infection confidence score. When the metadata are statistically similar, the system may generate a high infection confidence score.

Abstract: Systems and methods may use automation discovery to identify malicious activity. An automation discovery system comprising a processor in communication with a network and in communication with a database may receive potentially automated network traffic data. The system may analyze the potentially automated network traffic data to determine whether the potentially automated network traffic data is likely to be automated. When the potentially automated network traffic data is not likely to be automated, the system may generate a low automation confidence score associated with the potentially automated network traffic data. When the potentially automated network traffic data is likely to be automated, the system may generate a high automation confidence score associated with the potentially automated network traffic data.

Abstract: Systems and methods may use historical analysis to identify malicious activity. A discovery/recovery system may comprise a processor in communication with a network and in communication with a database. The discovery/recovery system may gather filtered historical network data associated with an asset associated with the network. The discovery/recovery system may analyze the filtered historical network data to determine whether a subset of the filtered historical network data is associated with a malware infection of the asset.

Abstract: Systems and methods may determine suspicious network traffic. A monitoring system comprising a processor in communication with a network may monitor network traffic to or from an asset associated with the network. The monitoring system may assess the network traffic to determine a source and/or destination for the network traffic anchor content of the network traffic. The monitoring system may determine whether the network traffic is suspicious network traffic based on the assessed source and/or destination and/or content. When the network traffic is determined to be suspicious network traffic, the monitoring system may capture metadata associated with the suspicious network traffic and store the metadata in a database in communication with the processor. When the network traffic is not determined to be suspicious network traffic, the monitoring system may disregard metadata associated with the network traffic.