Ensemble Intrusion Detection for Multi-Stage Network Attacks
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Solution Overview
Problem
Conventional network intrusion detection systems fail to effectively detect multiple stages of network attacks and identify the specific type of sub-attacks occurring, as they are typically designed for single types of attacks and rely on narrow datasets, limiting their ability to detect complex multi-stage attacks.
Innovation Solution
The system combines network traffic information and system log information using multiple machine learning techniques, including Deep Neural Networks and ensemble methods, to detect and classify sub-attacks such as reconnaissance, initial attack, pivoting, and data exfiltration, by processing data in a relatively short time frame and utilizing a Combined Intrusion Detection System that integrates features of both Network IDS and Host-based IDS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning approaches use a single algorithm tuned on a narrow dataset, then the algorithm can be optimized for that specific dataset, but it cannot detect multiple types of sub-attacks equally well
Solution Approach 1:
The patent combines multiple machine learning algorithms (Random Forest, XGBoost, LightGBM, Neural Networks) into an ensemble system that processes network traffic data collectively. Each algorithm contributes its strengths to detect different sub-attack types, resolving the contradiction by merging individual algorithm outputs into a comprehensive detection system that achieves both precision for specific attacks and versatility across multiple attack types.
Solution Approach 2:
The ensemble machine learning system is designed to perform multiple detection functions simultaneously - it can detect reconnaissance, initial attack, pivoting, lateral movement, and data exfiltration sub-attacks using the same integrated system. This multi-functional design allows a single system to adapt to various attack types without requiring separate specialized algorithms for each.
2Reliability
If conventional intrusion detection systems are designed for single types of attacks, then they can specialize in detecting that specific attack type, but they fail to detect multiple stages of network attacks
Solution Approach 1:
The patent segments the attack detection process into multiple specialized analysis procedures, each configured to detect specific sub-attack stages (reconnaissance, initial attack, pivoting, lateral movement, data exfiltration). Each procedure focuses on particular attack characteristics while the ensemble combines them to provide comprehensive multi-stage attack detection, maintaining reliability for each stage while achieving versatility across the entire attack lifecycle.
Solution Approach 2:
The system adds a temporal dimension to attack detection by analyzing network traffic across multiple time windows and stages. Instead of detecting attacks in a single snapshot, the ensemble processes sequential traffic data to identify multi-stage attack patterns over time, transforming the detection from a static single-point analysis to a dynamic multi-dimensional temporal analysis that captures the evolution of attacks.
3Productivity
If conventional systems use narrow datasets for training, then the training process is simpler and faster, but the system cannot effectively detect complex multi-stage attacks
Solution Approach 1:
The patent performs preliminary data preparation by collecting and preprocessing network traffic data from multiple sources and formats before training the ensemble models. This includes data normalization, feature extraction, and labeling of various attack types in advance. By preparing comprehensive training data beforehand, the system enables the ensemble algorithms to learn complex attack patterns effectively without sacrificing training efficiency, as the preprocessing work is done once before model training begins.
Data Source
AI summary
Systems and methods for detecting intrusions, attacks, and sub-attacks launched against a network under observations are provided. A method, according to one implementation, includes obtaining network traffic information regarding data traffic in a network under observation and obtaining system log information regarding operations of the network under observation. The method further includes the step of inserting the network traffic information and system log information into one or more analysis procedures, where each analysis procedure is configured to detect a respective sub-attack of a multi-stage attack to which the network under observation is susceptible. Also, the method includes the step of combining the outputs of the one or more analysis procedures to detect whether one or more sub-attacks have been launched against the network under observation. In response to detecting that one or more sub-attacks have been launched, the methods include the step of determining the type of the one or more sub-attacks.