Machine Learning Malware Detection Through Beacon Interval Patterns
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Solution Overview
Problem
Detecting malware beacons in network data is challenging due to their intermittent and disguised communications, which vary in check-in intervals and employ techniques to avoid detection, such as adding random jitter to callback times, making them difficult to distinguish from normal network traffic.
Innovation Solution
A system and method using machine learning to differentiate between benign and malware network data by generating dyads for each source-destination pair, analyzing communication patterns, and training a machine learning engine to classify network data as either benign or malware, incorporating features like communication interval skew and kurtosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used to identify malware beacons, then false positives increase due to disguised communications, but detection precision deteriorates because malware beacons are difficult to distinguish from normal network traffic
Solution Approach 1:
The patent transforms the detection approach by changing from analyzing individual network packets to analyzing temporal patterns of communication intervals. By extracting statistical parameters (mean, standard deviation, skewness, kurtosis) from sequences of beacon intervals and comparing them against trained models, the system achieves accurate malware detection while maintaining low false positive rates, resolving the contradiction between reliability and measurement precision.
2Measurement precision
If machine learning models are trained with extensive features to improve detection accuracy, then detection precision improves, but device complexity increases due to the need for sophisticated analysis engines
Solution Approach 1:
The patent extracts only the essential temporal features from network traffic - specifically the sequence of communication intervals between beacons. By focusing on this single critical dimension and deriving four statistical parameters from it, the system achieves high detection accuracy without requiring complex multi-feature analysis engines, thus resolving the contradiction between measurement precision and device complexity.
3Object-affected harmful factors
If random jitter is added to beacon intervals to avoid detection, then malware evades traditional detection methods, but detection becomes more difficult because check-in intervals vary
Solution Approach 1:
The patent converts the harmful effect of random jitter into a beneficial detection feature. Instead of treating interval variation as noise to be filtered, the system extracts statistical parameters including skewness and kurtosis that specifically capture the nature of jitter patterns. Malware-induced jitter creates distinct statistical signatures that differ from normal traffic patterns, allowing the machine learning model to detect malware more effectively, thus resolving the contradiction between malware evasion capability and detection difficulty.
Data Source
AI summary
A method comprises obtaining a training set of network data that includes benign network data and malware network data; engaging a feature extraction engine to generate a set of dyads for each source-destination pair in the training set of network data; and training, using the set of dyads, a machine learning engine to differentiate between the benign network data and the malware network data.