Network Beaconing Detection Using Ensemble ML Classification
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Solution Overview
Problem
Malicious beaconing in networks poses significant security threats through covert communication between malware and attacker-controlled servers, facilitating data breaches and network disruptions, requiring advanced detection and mitigation.
Innovation Solution
A cloud-based system employs machine learning models to classify beaconing sequences as clean, malicious, suspicious, or unknown by extracting features from network log data and using ensemble models for accurate classification, with pre-filtering to distinguish beaconing activities from generic webpage loading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to classify beaconing sequences, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the detection system into multiple specialized machine learning models (e.g., random forest classifier, neural network, support vector machine) that each process different aspects of beaconing detection. This segmentation allows the system to achieve high detection accuracy through specialized algorithms while managing complexity by dividing the overall detection task into manageable components.
Solution Approach 2:
The patent introduces an ensemble model as an intermediary that aggregates predictions from multiple individual ML models. This intermediary layer coordinates the outputs of various complex algorithms, providing a unified detection decision that maintains high accuracy while simplifying the overall system architecture and making it more manageable.
2Reliability
If pre-filtering is applied to distinguish beaconing activities from generic webpage loading, then false positives are reduced, but processing time increases
Solution Approach 1:
The patent applies pre-filtering rules before the main ML classification process to identify and filter out obvious beaconing patterns. By performing this preliminary action, the system reduces false positives early in the detection pipeline without requiring full ML model processing for all traffic, thus balancing reliability improvement with acceptable processing time.
Solution Approach 2:
The patent implements a two-stage detection approach where only suspicious traffic that passes through the pre-filtering stage undergoes full ML model analysis. This partial action strategy applies comprehensive processing only where necessary, reducing false positives for critical cases while minimizing overall processing time by avoiding exhaustive analysis of all network traffic.
3Measurement precision
If ensemble models with weighted votes are used for classification, then classification accuracy is improved, but computational overhead increases
Solution Approach 1:
The patent dynamically adjusts the weights of individual ML models in the ensemble based on their performance metrics and confidence levels. By changing these parameters adaptively, the system achieves higher classification accuracy through optimized model combinations while reducing computational overhead by giving more weight to higher-performing, more efficient models rather than uniformly processing all models equally.
Data Source
AI summary
Systems and methods for malicious beaconing detection include extracting one or more beaconing sequences from log data associated with a network; performing feature extraction for the one or more extracted beaconing sequences; and implementing one or more Machine Learning (ML) models for classifying each of the one or more beaconing sequences as any of clean, malicious, suspicious, and unknown. The one or more ML models can be associated with an ensemble model, where a final classification of a beaconing sequence can be based on results of each of the one or more ML models.