Behavioral Feature Time Series for APT Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cybersecurity systems face challenges in detecting advanced persistent threats (APTs) due to the large volumes of data generated at enterprise network perimeters, making it difficult to identify threat patterns over extended periods, especially during the command and control phase of attacks, which often blend with legitimate traffic.
Innovation Solution
A framework combining behavioral analytics, time series formatting, and representation learning algorithms is employed to model and analyze log data, allowing for the detection of attacks spanning several weeks by forming time-based series of behavioral features and using neural network-based systems for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning-based attempts focus on analysis of individual connections, then detection speed is improved, but detection accuracy for command and control threats deteriorates due to inability to capture behavioral patterns over extended periods
Solution Approach 1:
The patent segments the analysis process into two distinct components: (1) extraction of behavioral features from individual log connections, and (2) analysis of time-series sequences of these features using recurrent neural networks. This segmentation allows individual connection processing to maintain speed while the time-series analysis component captures long-term behavioral patterns for accurate C2 detection.
Solution Approach 2:
The patent transitions from analyzing single-dimensional individual connections to analyzing multi-dimensional time-series sequences of behavioral features. By adding the temporal dimension and using recurrent neural networks to process sequences of feature vectors, the system captures evolutionary patterns of command and control behavior that cannot be detected in isolated connections.
2Quantity of substance
If systems monitor and analyze large volumes of log data from perimeter devices, then coverage of threat detection is improved, but computational complexity and storage requirements worsen
Solution Approach 1:
The patent extracts only the most relevant behavioral features from large volumes of raw log data, rather than processing all raw data. By identifying and extracting key behavioral characteristics (such as connection frequency, destination patterns, and temporal behavior), the system reduces computational complexity while maintaining comprehensive threat detection coverage.
Solution Approach 2:
The patent introduces behavioral features as an intermediary representation between raw log data and threat detection analysis. These features serve as a compressed, meaningful summary of complex log data, reducing storage requirements and computational complexity while preserving the essential information needed for detecting command and control threats.
3Object-affected harmful factors
If attackers blend command and control traffic with legitimate web traffic, then evasion of detection is improved, but distinguishability of malicious patterns deteriorates
Solution Approach 1:
The patent performs preliminary extraction of behavioral features that capture the essence of communication patterns before analysis. By pre-processing log data into standardized feature vectors that encode temporal and behavioral characteristics, the system prepares the data in a form that enables subsequent recurrent neural network analysis to distinguish subtle malicious patterns from legitimate traffic, even when attackers attempt to blend their communications.
Data Source
AI summary
Disclosed is a data analysis and cybersecurity method, which forms a time-based series of behavioral features, and analyzes the series of behavioral features for attack detection, new features derivation, and/or features evaluation. Analyzing the time based series of behavioral features may comprise using a Feed-Forward Neural Networks (FFNN) method, a Convolutional Neural Networks (CNN) method, a Recurrent Neural Networks (RNN) method, a Long Short-Term Memories (LSTMs) method, a principal Component Analysis (PCA) method, a Random Forest pipeline method, and/or an autoencoder method. In one embodiment, the behavioral features of the time-based series of behavioral features comprise human engineered features, and/or machined learned features, wherein the method may be used to learn new features from historic features.