VHDRA: A Vertical and Horizontal Intelligent Dataset Reduction Approach for Cyber-Physical Power Aware Intrusion Detection Systems

Abstract

The Cypher Physical Power Systems (CPPS) became vital targets for intruders because of the large volume of high speed heterogeneous data provided from the Wide Area Measurement Systems (WAMS). The Nonnested Generalized Exemplars (NNGE) algorithm is one of the most accurate classification techniques that can work with such data of CPPS. However, NNGE algorithm tends to produce rules that test a large number of input features. This poses some problems for the large volume data and hinders the scalability of any detection system. In this paper, we introduce VHDRA, a Vertical and Horizontal Data Reduction Approach, to improve the classification accuracy and speed of the NNGE algorithm and reduce the computational resource consumption. VHDRA provides the following functionalities: (1) it vertically reduces the dataset features by selecting the most significant features and by reducing the NNGE’s hyperrectangles. (2) It horizontally reduces the size of data while preserving original key events and patterns within the datasets using an approach called STEM, State Tracking and Extraction Method. The experiments show that the overall performance of VHDRA using both the vertical and the horizontal reduction reduces the NNGE hyperrectangles by 29.06%, 37.34%, and 26.76% and improves the accuracy of the NNGE by 8.57%, 4.19%, and 3.78% using the Multi-, Binary, and Triple class datasets, respectively.

Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method

“…To consider the 5G characteristics, we introduce a hierarchical, scalable, robust, and flexible deployment architecture for our Autonomous Security Management Framework (ASMF) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ] see Figure 4 . The ASMF framework consists of the following components and processes.…”

“…This process extracts a subset of relevant important features from the correlation process to enhance the classification results. More details about this process are listed in [ 54 ];…”

“…This process extracts a subset of relevant important features from the correlation process to enhance the classification results. More details about this process are listed in [54]; (e) Risk Assessment. The risk assessment model assesses the risk in the 5G infrastructure based on the alert level of different events; (f) Autonomic Response and Countermeasures Selection Process.…”