AI Host-Based Lateral Movement Detection Through Traffic Correlation
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Solution Overview
Problem
Current methods struggle to identify unauthorized lateral movement between hosts in enterprise computing systems in real time, as existing tools lack the capability to validate network traffic authenticity and correlate host-to-host communications effectively.
Innovation Solution
A host-based lateral movement identification tool utilizing artificial intelligence algorithms analyzes network communications from a local host, correlating information from directory services, network systems, and event logs to detect potential unauthorized host-to-host connections and generate alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional HIDS solutions search for known patterns in network traffic, then they can detect some malicious activities, but they fail to identify users utilizing legitimate tools to make unauthorized connections (living off the land)
Solution Approach 1:
The system changes the detection parameters from static known patterns to dynamic behavioral analysis. It monitors parameters such as command-line arguments, process creation hierarchies, network connection patterns, and timing characteristics to distinguish between legitimate and malicious use of the same tools, enabling detection of unauthorized legitimate traffic
Solution Approach 2:
The system implements continuous feedback loops where observed network behavior is compared against established baselines and threat intelligence. The feedback mechanism allows the system to learn from new attack patterns and adjust detection criteria dynamically, improving its ability to identify sophisticated lateral movement techniques
2Loss of information
If passive tools such as event logs are used to link messages to users, then they can provide audit trails, but they cannot directly correlate host to host communication in real time
Solution Approach 1:
The system merges multiple data sources including event logs, network packet captures, process monitoring data, and user identity information into a unified correlation engine. This integration enables real-time analysis of host-to-host communications while maintaining comprehensive audit trails, eliminating the trade-off between real-time capability and information correlation
3Reliability
If AI algorithms analyze aggregated network information to identify lateral movement, then detection capability improves, but computational resources and processing complexity increase
Solution Approach 1:
The system segments the network monitoring function into distributed agents deployed at various network layers and hosts. Each agent performs local analysis and only transmits relevant indicators to centralized processing, reducing overall computational complexity while maintaining comprehensive detection capability through distributed intelligence
Data Source
AI summary
A lateral movement identification tool analyzes communications sent and received from a local host to identify potential instances of lateral movement. When the host-based lateral movement identification tool identifies a host to host connection, the tool processes one or more artificial intelligence algorithms to analyze information from local network resources including a directory service, a local network system such as a network basic input/output system, a domain name system, and event logs. The lateral movement identification tool correlates the aggregated information with identified host to host messaging and sends alerts when lateral movement is suspected. Alerts may be either presented locally or provided to a central console based on configuration information.