Abstract: Infrastructure attacks based on graph edge context are identified by receiving an execution graph constructed by a central service based on behaviors monitored by a plurality of agents deployed on respective systems including a first system. The execution graph comprises a plurality of execution trails. One or more tags are applied to each edge of an execution trail of the execution graph based on at least one of temporal context or spatial context associated with the edge. One or more behaviors associated with the edge of the execution trail happen across an enterprise infrastructure involving the first system. The execution trail enriched with the one or more tags is analyzed. An action that is performed to mitigate security risks in the execution graph is determined based on the analysis.

Abstract: Infrastructure attacks involving lateral movement are identified by monitoring system level activities using software agents deployed on respective operating systems, and constructing, based on the system level activities, an execution graph comprising execution trails. A logon session between a remote connection client executing on a first operating system and a remote connection server executing on a second operating system is identified. Behavior exhibited from the logon session is attributed to a first global execution trail in the execution graph. A reconnection to the logon session between a remote connection client executing on a third operating system and the remote connection server is then identified, and, thereafter, behavior exhibited from the logon session is attributed to a second global execution trail in the execution graph.

Abstract: A novel enterprise security solution allows for precise interception and surgical response to attack progression, in real time, as it occurs across a distributed infrastructure. The solution includes a data monitoring and management framework that continually models system level host and network activities as mutually exclusive infrastructure wide execution sequences and bucketizes them into unique execution trails. A multimodal intelligent security middleware detects indicators of compromise in real-time on top of subsets of each unique execution trail using rule based behavioral analytics, machine learning based anomaly detection, and other sources. Each detection result dynamically contributes to aggregated risk scores at execution trail level granularities. These scores can be used to prioritize and identify highest risk attack trails to end users, along with steps that such end users can perform to mitigate further damage and progression of an attack.

Abstract: A novel enterprise security solution allows for precise interception and surgical response to attack progression, in real time, as it occurs across a distributed infrastructure. The solution includes a data monitoring and management framework that continually models system level host and network activities as mutually exclusive infrastructure wide execution sequences and bucketizes them into unique execution trails. A multimodal intelligent security middleware detects indicators of compromise in real-time on top of subsets of each unique execution trail using rule based behavioral analytics, machine learning based anomaly detection, and other sources. Each detection result dynamically contributes to aggregated risk scores at execution trail level granularities. These scores can be used to prioritize and identify highest risk attack trails to end users, along with steps that such end users can perform to mitigate further damage and progression of an attack.

Abstract: A novel enterprise security solution allows for precise interception and surgical response to attack progression, in real time, as it occurs across a distributed infrastructure. The solution includes a data monitoring and management framework that continually models system level host and network activities as mutually exclusive infrastructure wide execution sequences and bucketizes them into unique execution trails. A multimodal intelligent security middleware detects indicators of compromise in real-time on top of subsets of each unique execution trail using rule based behavioral analytics, machine learning based anomaly detection, and other sources. Each detection result dynamically contributes to aggregated risk scores at execution trail level granularities. These scores can be used to prioritize and identify highest risk attack trails to end users, along with steps that such end users can perform to mitigate further damage and progression of an attack.

Abstract: A novel enterprise security solution allows for precise interception and surgical response to attack progression, in real time, as it occurs across a distributed infrastructure. The solution includes a data monitoring and management framework that continually models system level host and network activities as mutually exclusive infrastructure wide execution sequences and bucketizes them into unique execution trails. A multimodal intelligent security middleware detects indicators of compromise in real-time on top of subsets of each unique execution trail using rule based behavioral analytics, machine learning based anomaly detection, and other sources. Each detection result dynamically contributes to aggregated risk scores at execution trail level granularities. These scores can be used to prioritize and identify highest risk attack trails to end users, along with steps that such end users can perform to mitigate further damage and progression of an attack.

Abstract: A novel enterprise security solution allows for precise interception and surgical response to attack progression, in real time, as it occurs across a distributed infrastructure. The solution includes a data monitoring and management framework that continually models system level host and network activities as mutually exclusive infrastructure wide execution sequences and bucketizes them into unique execution trails. A multimodal intelligent security middleware detects indicators of compromise in real-time on top of subsets of each unique execution trail using rule based behavioral analytics, machine learning based anomaly detection, and other sources. Each detection result dynamically contributes to aggregated risk scores at execution trail level granularities. These scores can be used to prioritize and identify highest risk attack trails to end users, along with steps that such end users can perform to mitigate further damage and progression of an attack.