Abstract: The disclosed subject matter includes a system, which when installed in a specific host, such as an end point, or end point computer, will model its behavior over time, score new activities in real time and calculate outliers, by creating and analyzing vectors. The vectors are formed of feature values, extracted from executable processes, and the analysis includes the determining and evaluating the distance between a current vector and a cluster of vectors.

Abstract: Computerized methods and systems determine an entry point or source of an attack on an endpoint, such as a machine, e.g., a computer, node of a network, system or the like. These computerized methods and systems utilize an attack execution/attack or start root, to build an attack tree, which shows the attack on the end point and the damage caused by the attack, as it propagates through the machine, network, system, or the like.

Abstract: Computerized methods and systems determine an initial execution of an attack on an endpoint. An indicator of the attack is obtained by analysis of a first process on the endpoint. A sequence of processes that includes the first process associates the initial execution of the attack with the first process. Each respective process in the sequence of processes is created or executed by at least one of the initial execution or a process in the sequence of processes. The initial execution is identified based on linking from the first process to the initial execution through a combination of executions and creations of the processes in the sequence of processes.

Abstract: Computerized methods and systems determine an entry point or source of an attack on an endpoint, such as a machine, e.g., a computer, node of a network, system or the like. These computerized methods and systems utilize an attack execution/attack or start root, to build an attack tree, which shows the attack on the end point and the damage caused by the attack, as it propagates through the machine, network, system, or the like.

Abstract: Disclosed are methods and systems for detecting malware and potential malware based on using generalized attack trees (generalized attack tree graphs). The generalized attack trees are based on attack trees (attack tree graphs), whose objects, such as links and vertices, have been analyzed, and some of these objects have been generalized, resulting in the generalized attack tree of the invention.

Abstract: Computerized methods and systems identify events associated with an attack initiated on an endpoint client. A listing of processes executed or created on the endpoint during the attack is obtained. The listing of processes includes a first process and at least one subsequent process executed or created by the first process. The computerized methods and systems analyze for the occurrence of at least one event during a time interval associated with the attack. The computerized methods and systems determine whether the listing of processes includes a process that when executed caused the occurrence of the at least one event. If the listing of processes excludes process that when executed caused the occurrence of the at least one event, the at least one event and the causing process are stored, for example, in a database or memory.

Abstract: Computerized methods and systems determine an entry point or source of an attack on an endpoint, such as a machine, e.g., a computer, node of a network, system or the like. These computerized methods and systems utilize an attack execution/attack or start root, to build an attack tree, which shows the attack on the end point and the damage caused by the attack, as it propagates through the machine, network, system, or the like.

Abstract: Computerized methods and systems determine summary events from an attack on an endpoint. The detection and determination of these summary events is performed by a machine, e.g., a computer, node of a network, system or the like.

Abstract: Disclosed are methods and systems for detecting malware and potential malware based on using generalized attack trees (generalized attack tree graphs). The generalized attack trees are based on attack trees (attack tree graphs), whose objects, such as links and vertices, have been analyzed, and some of these objects have been generalized, resulting in the generalized attack tree of the invention.

Abstract: Disclosed are methods and systems for detecting malware and potential malware based on using generalized attack trees (generalized attack tree graphs). The generalized attack trees are based on attack trees (attack tree graphs), whose objects, such as links and vertices, have been analyzed, and some of these objects have been generalized, resulting in the generalized attack tree of the invention.

Abstract: Computerized methods and systems identify events associated with an attack initiated on an endpoint client. A listing of processes executed or created on the endpoint during the attack is obtained. The listing of processes includes a first process and at least one subsequent process executed or created by the first process. The computerized methods and systems analyze for the occurrence of at least one event during a time interval associated with the attack. The computerized methods and systems determine whether the listing of processes includes a process that when executed caused the occurrence of the at least one event. If the listing of processes excludes process that when executed caused the occurrence of the at least one event, the at least one event and the causing process are stored, for example, in a database or memory.

Abstract: Computerized methods and systems determine summary events from an attack on an endpoint. The detection and determination of these summary events is performed by a machine, e.g., a computer, node of a network, system or the like.

Abstract: Disclosed are methods and systems for detecting malware and potential malware based on using generalized attack trees (generalized attack tree graphs). The generalized attack trees are based on attack trees (attack tree graphs), whose objects, such as links and vertices, have been analyzed, and some of these objects have been generalized, resulting in the generalized attack tree of the invention.

Abstract: Computerized methods and systems determine an initial execution of an attack on an endpoint. An indicator of the attack is obtained by analysis of a first process on the endpoint. A sequence of processes that includes the first process associates the initial execution of the attack with the first process. Each respective process in the sequence of processes is created or executed by at least one of the initial execution or a process in the sequence of processes. The initial execution is identified based on linking from the first process to the initial execution through a combination of executions and creations of the processes in the sequence of processes.

Abstract: Computerized methods and systems determine an entry point or source of an attack on an endpoint, such as a machine, e.g., a computer, node of a network, system or the like. These computerized methods and systems utilize an attack execution/attack or start root, to build an attack tree, which shows the attack on the end point and the damage caused by the attack, as it propagates through the machine, network, system, or the like.

Abstract: A network security system employing multiple levels of processing to identify security threats. Multiple host machines may each contain an agent that detects possibilities of security threats based on raw data sensed locally at that host. The hosts may share information obtained from local analysis and each host may use information generated at one or more other hosts, in combination with information generated locally, to identify a security concern, indicating with greater certainty that a security threat exists. Based on security concerns generated by multiple hosts, a security threat may be to indicated and protective action may be taken.

Abstract: Aspects of the subject matter described herein relate to a mechanism for assessing security. In aspects, an analytics engine is provided that manages execution, information storage, and data passing between various components of a security system. When data is available for analysis, the analytics engine determines which security components to execute and the order in which to execute the security components, where in some instances two or more components may be executed in parallel. The analytics engine then executes the components in the order determined and passes output from component to component as dictated by dependencies between the components. This is repeated until a security assessment is generated or updated. The analytics engine simplifies the work of creating and integrating various security components.

Abstract: Tracking malware state information assigned to computers in an enterprise network is described. A computer may transition from a current malware state to a new malware state in accordance with a plurality of stored rules and detection of an anti-malware event on the computer. Examples of anti-malware events include, but are not limited to, detection of new malware on the computer or cleaning of the computer. The malware state information for computers on the network may be mapped to a risk level representing an amount of risk that infected computers present to other computers on the network. The results of a risk level assessment for the computers on the network may be output via a user interface to enable an administrator of the network to prioritize servicing of computers with detected malware.

Abstract: Mapping between object types in an enterprise security assessment sharing (“ESAS”) system enables attacks on an enterprise network and security incidents to be better detected and capabilities to respond to be improved. The ESAS system is distributed among endpoints incorporating different security products in the enterprise network that share a commonly-utilized communications channel. An endpoint publishes a security assessment when a potential security incident is detected. The security assessment identifies the object of interest, the type of security incident and its severity. A level of confidence in the detection is also provided which is expressed by an attribute called the “fidelity”. ESAS is configured with the capabilities to map between objects, including users and machines in the enterprise network, so that security assessments applicable to one object domain can be used to generate security assessments in another object domain.

Abstract: A network security system employing multiple levels of processing to identify security threats. Multiple host machines may each contain an agent that detects possibilities of security threats based on raw data sensed locally at that host. The hosts may share information obtained from local analysis and each host may use information generated at one or more other hosts, in combination with information generated locally, to identify a security concern, indicating with greater certainty that a security threat exists. Based on security concerns generated by multiple hosts, a security threat may be to indicated and protective action may be taken.