Abstract: Disclosed is an improved method, system, and computer program product for detecting hosts and connections between hosts that are being used as relays by an actor to gain control of hosts in a network. It can further identify periods of time within the connection when the relay activities occurred. In some embodiments, the invention can also chain successive relays to identify the true source and true target of the relay.

Abstract: Disclosed is an improved approach for detecting potentially malicious activity on a network. The present improved approach generates a multi-dimensional activity model based on captured network activity. Additional network activity is captured, and relative activity values are determined therefor. Determination of whether the additional network activity corresponds to potentially malicious activity is obtained by fitting the relative activity values of the additional network activity to the multi-dimensional relative activity model.

Abstract: Disclosed is an improved approach for identifying suspicious administrative host activity within a network. Network traffic is examined to learn the behavior of hosts within a network. This provides an effective way of determining whether or not a host is performing suspicious activity over an administrative protocol.

Abstract: An approach for high-volume network threat tracing and detection may be implemented by storing network communications received from a plurality of hosts in an initial recording data structure, such as a rolling buffer. Identifiers may be generated for the plurality of hosts associated with the network communications by according to host identity or the behavior of a given host. Extended trace time values may be assigned to a portion of the plurality of hosts based at least in part on the identifiers, and storing the portion of the network communications that have extended trace time values may be recorded as packet capture files in long term memory.

Abstract: Disclosed is an improved method, system, and computer program product for detecting hosts and connections between hosts that are being used as relays by an actor to gain control of hosts in a network. It can further identify periods of time within the connection when the relay activities occurred. In some embodiments, the invention can also chain successive relays to identify the true source and true target of the relay.

Abstract: Disclosed is an approach to detect insider threats, by tracking unusual access activity for a specific user or computer with regard to accessing key assets over time. In this way, malicious activity and the different preparation phases of attacks can be identified.

Abstract: Disclosed is an improved method, system, and computer program product for learning representations or embeddings of network flow traffic. The disclosed invention operates on network flow data which are then used as inputs to a deep-learning architecture that learns to embed the data into a vector space.

Abstract: A method and system for identifying insider threats within an organization is provided. The approach constructs an internal connectivity graph to identify communities of hosts/users, and checks for abnormal behavior relative to past behaviors.

Abstract: An approach for detecting network threats is disclosed, that may involve receiving network traffic, plotting the network traffic in a n-dimensional feature space to form a network map, generating a client signature at least by placing new client points in the map, setting a threshold, and generating an alarm if one or more client activity points exceed the threshold. In some embodiments, the network map and the client signature are updated using sliding windows and distance calculations.

Abstract: A system for categorizing malware threat names comprising a malware correlator and a frequency graph constructor engine based on a malware virus predicate. The malware correlator can categorize malware threat names based on a malware virus predicate or malware virus network behavior. The frequency graph constructor engine can construct a graphical representation of the malware threat family.

Abstract: A bot detection engine to determine whether hosts in an organization's network are performing bot-related activities is disclosed. A bot detection engine can receive network traffic between hosts in a network, and/or between hosts across several networks. The bot engine may parse the network traffic into session datasets and discard the session datasets that were not initiated by hosts in a given network. The session datasets may be analyzed and state data may be accumulated. The state data may correspond to actions performed by the hosts, such as requesting a website or clicking ads, or requesting content within the website (e.g. clicking on a image which forms a HTTP request/response transaction for the image file).

Abstract: Disclosed is an improved approach for identifying suspicious administrative host activity within a network. Network traffic is examined to learn the behavior of hosts within a network. This provides an effective way of determining whether or not a host is performing suspicious activity over an administrative protocol.

Abstract: Disclosed is an improved method, system, and computer program product for identifying malicious payloads. The disclosed approach identifies potentially malicious payload exchanges which may be associated with payload injection or root-kit magic key usage.

Abstract: Disclosed is an improved approach to implement a system and method for detecting insider threats, where models are constructed that is capable of defining what constitutes the normal behavior for any given hosts and quickly find anomalous behaviors that could constitute a potential threat to an organization. The disclosed approach provides a way to identify abnormal data transfers within and external to an organization without the need for individual monitoring software on each host, by leveraging metadata that describe the data exchange patterns observed in the network.

Abstract: An approach for detecting network attacks using metadata vectors may initially involve receiving network communications or packets, extracting metadata items from the packets. The metadata items describe the communications without requiring deep content inspection of the data payload or contents. The communications may be clustered into groups using the metadata items. If a cluster exceeds a threshold, an alarm may be generated.

Abstract: A host identification engine receives network traffic from a network and uses one or more artifact extractors to extract artifact data items that can identify a host. The artifact data items can be stored in a host signature database. Network addresses to which the hosts correspond can be stored in a network address database. A mapping table can be implemented to match the data in the signature database and network database to generate durable host identification data that can accurately track hosts as they use different identification data and/or move between hosts.

Abstract: A method and system for detecting algorithm-generated domains (AGDs) is disclosed wherein domain names requested by an internal host are categorized or classified using curated data sets, active services (e.g. Internet services), and certainty scores to match domain names to domain names or IP addresses used by command and control servers.

Abstract: A system and method for detecting malicious relay communications is disclosed. Network communications can be received and analyzed using such network components as a network switch. The received traffic can be parsed into sessions. Relay metadata can be extracted from the sessions and further be used to categorize the sessions into one or more types of relay metadata behaviors. Once a significant amount of sessions are detected an alarm may be triggered and/or alarm data may be generated for analysis by network security administrators.

Abstract: A method and system for detecting network reconnaissance is disclosed wherein network traffic can be parsed into unidirectional flows that correspond to sessions. A learning module may categorize computing entities inside the network into assets and generate asset data to monitor the computing entities. If one or more computing entities address a flow to an address of a host that no longer exists, ghost asset data may be recorded and updated in the asset data. When a computing entity inside the network contacts an object in the dark-net, the computing entity may be recorded a potential mapper. When the computing entity tries to contact a number of objects in the dark-net, such that a computed threshold is exceeded, the computing entity is identified a malicious entity performing network reconnaissance.

Abstract: Approaches for detecting network intrusions, such as malware infection, Trojans, worms, or bot net mining activities includes: identifying one or more threat detections in session datasets, the session datasets corresponding to network traffic from a plurality of hosts; determining a layered detection score, the layered detection score corresponding to a certainty score and threat score; determining a layered host score, the layered host score corresponding to a certainty score and threat score; and generating alarm data comprising the layered detection score and the layered host score. In some embodiments, the network traffic may be received passively through a network switch; for example, by “tapping” the switch. Other additional objects, features, and advantages of the invention are described in the detailed description, figures and claims.