Malware Beaconing Detection via Network Traffic Feature Extraction
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Solution Overview
Problem
Conventional security threat detection methods, relying on static rules and known signatures, are ineffective against dynamic and unknown threats like zero-day attacks and Advanced Persistent Threats (APTs), which change behavior over time and can evade detection.
Innovation Solution
A system and process for detecting malware beaconing activity using a malware beaconing activity detection server that analyzes logs from firewalls, proxies, and DHCP servers to identify anomalous communications between internal devices and external servers, employing feature extraction and statistical models to flag and prioritize potentially malicious conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rules and known signatures are used for detection, then detection reliability is improved for known threats, but adaptability deteriorates against unknown and evolving threats
Solution Approach 1:
The patent transitions from static signature-based detection to dynamic behavior-based detection. The system continuously monitors communication patterns, extracts features from network traffic, and adapts to new threats by learning from observed behaviors. This dynamic approach allows the system to detect both known and unknown threats by identifying anomalous patterns rather than relying solely on predefined signatures.
Solution Approach 2:
The system changes the detection parameters from fixed signatures to variable behavioral features. By extracting multiple features from network communications (such as timing patterns, data volumes, protocol usage) and analyzing their statistical properties, the system can detect threats based on parameter deviations from normal behavior, enabling adaptation to evolving threats while maintaining reliability through multi-parameter analysis.
2Adaptability or versatility
If feature extraction and statistical models are employed to detect unknown threats, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection system into distinct functional modules: feature extraction module, statistical analysis module, and anomaly detection module. Each module performs a specific function - extracting relevant features from network traffic, analyzing statistical patterns, and detecting anomalies. This segmentation reduces overall system complexity by making each component manageable and independently optimizable while maintaining high adaptability through their coordinated operation.
Solution Approach 2:
The system introduces statistical models as intermediary components between raw network traffic and threat detection decisions. These statistical models act as mediators that process complex traffic data, extract meaningful patterns, and provide simplified anomaly scores. This intermediary layer reduces the complexity of direct threat detection by transforming raw data into interpretable statistical indicators that are easier to analyze and decision-making.
Data Source
AI summary
Malware beaconing activity detection is disclosed, including: monitoring a plurality of conversations between an internal device and one or more external destinations; extracting feature sets based at least in part on the plurality of conversations; and determining that a conversation of the plurality of conversations is anomalous based at least in part on the extracted feature sets.