Electric Power Multi-Step Attack Detection with Heterogeneous Graphs
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Solution Overview
Problem
Existing deep learning-based end-to-end detection methods for complex multi-step attacks in electric power systems suffer from low detection success rates due to long time intervals and understated features, as traditional neural networks fail to allocate higher weights to important connection relationships.
Innovation Solution
A method involving data preprocessing using a heterogeneous graph to construct nodes and edges, incorporating Time2Vec and Heteformer layers for time embedding and self-attention mechanisms to learn and aggregate neighbor information, enhancing the detection of complex multi-step attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural networks are used for attack detection, then the detection process is simple, but the detection accuracy is low due to inability to allocate higher weights to important connection relationships
Solution Approach 1:
The patent segments the attack detection problem into multiple processing stages: heterogeneous graph construction from interaction behavior data, Time2Vec time embedding layer for temporal feature extraction, Heteformer self-attention layer for relationship weighting, and final detection module. This segmentation allows each component to specialize in specific aspects, improving overall detection accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate representations including heterogeneous graph structures that model entity relationships, Time2Vec embeddings that capture temporal patterns, and self-attention mechanisms that identify critical connections. These intermediaries transform raw interaction behavior data into structured features that enhance detection accuracy without requiring the entire system to be overly complex.
2Extent of automation
If end-to-end detection methods based on deep learning are used, then feature engineering is automated, but detection success rate remains low due to long time intervals and understated features of complex multi-step attacks
Solution Approach 1:
The patent performs preliminary actions by constructing heterogeneous graphs that pre-organize interaction behavior data into meaningful entity relationships before detection. The Time2Vec layer pre-processes temporal information into embeddings that capture time interval patterns. These preliminary structuring operations enable the model to automatically recognize complex multi-step attack patterns without manual feature engineering, improving both automation and reliability.
Solution Approach 2:
The patent transforms the detection problem into additional dimensions by creating heterogeneous graph structures that add relational dimensionality, Time2Vec time embedding that adds temporal dimensionality, and self-attention mechanisms that add importance weighting dimensionality. These dimensional transformations enable the automated detection system to capture complex attack patterns that would be invisible in traditional flat feature spaces.
Data Source
AI summary
Disclosed are a method and system for detecting a complex multi-step attack in an electric power system. The method includes: collecting interaction behavior data of a network entity; preprocessing the interaction behavior data of the network entity based on a heterogeneous graph to obtain input data; and inputting the input data into a complex multi-step attack detection module to obtain an attack detection result. Information is extracted from the interaction behavior data to construct a node and an edge of the heterogeneous graph. Timestamp information of a destination node and an adjacent source node of the destination node is input into a Time2Vec layer to obtain a first time embedding representation. Data that fuses node feature information and the first time embedding representation is input into a Heteformer layer, and a second node embedding representation is obtained as input data and input into the complex multi-step attack detection module.