Beacon Traffic Risk Scoring for Signature-Resistant APT Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anti-malware security solutions fail to detect new malware variants that simulate benign network traffic, such as Cobalt Strike Beacon C2 traffic, due to the lack of existing signatures, leading to ineffective detection of Advanced Persistent Threat (APT) attacks.
Innovation Solution
A system for beacon and threat intelligence based APT detection that utilizes firewall log data analysis, including a beacon detection component and risk scoring, using Bowley Skewness and median absolute deviation, to identify and evaluate potentially malicious beacon traffic based on threat intelligence information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anti-malware solutions use signature-based detection, then they can identify known malware variants, but they fail to detect new malware variants that simulate benign network traffic
Solution Approach 1:
The patent changes the detection parameters from static malware signatures to dynamic behavioral parameters. Instead of looking for fixed malicious code patterns, the system monitors runtime behaviors such as network connection patterns, system API calls, and process creation sequences. This allows detection of new malware variants that use polymorphic techniques and simulate benign traffic, as the behavioral parameters capture the essence of malicious activity regardless of its specific implementation.
Solution Approach 2:
The patent replaces the mechanical signature-matching system with a behavioral analysis system. Rather than using pattern recognition algorithms that match against predefined malware signatures, the system employs process tracing, system call monitoring, and network behavior analysis. This substitution enables detection of previously unknown malware variants by observing their operational characteristics rather than relying on their code signatures.
2Reliability
If the system monitors all network traffic for beacon detection, then detection coverage increases, but system performance and processing speed decrease
Solution Approach 1:
The patent segments network traffic into different categories based on protocol type, destination, and behavioral characteristics. Instead of uniformly analyzing all traffic, the system applies different monitoring depths and analysis methods to different traffic segments. For example, it performs deep packet inspection on suspicious beacon traffic while using lighter-weight monitoring for known benign protocols, thereby maintaining high detection coverage while preserving processing speed.
Solution Approach 2:
The patent implements dynamic adjustment of monitoring intensity based on real-time traffic characteristics. The system continuously evaluates network traffic patterns and automatically adjusts the level of inspection depth, sampling rates, and analysis granularity. When traffic exhibits beacon-like characteristics, the system increases monitoring intensity; when traffic appears benign, it reduces monitoring overhead. This dynamic approach maintains comprehensive detection coverage while optimizing processing performance.
Data Source
AI summary
Techniques for beacon and threat intelligence based Advanced Persistent Threat (APT) detection are disclosed. In some embodiments, a system/process/computer program product for beacon and threat intelligence based APT detection includes collecting firewall log data from monitored network traffic; analyzing the firewall log data at a cloud security service to identify beacon traffic based on a plurality of heuristics; performing a risk evaluation of the beacon traffic to detect malicious beacon traffic; and performing an action in response to detecting the malicious beacon traffic.