Enhancing Intrusion Detection in Electric Networks Using Physics-Informed Random Forest

The increasing complexity of electric power networks has heightened their vulnerability to cyber-attacks, challenging traditional Intrusion Detection Systems (IDS) that rely on manually crafted rules. This paper introduces a novel approach that integrates physics-informed features and feature select...

Full description

Saved in:
Bibliographic Details
Main Authors: Bozdal, Mehmet, Savasci, Alper
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
cybersecurity
electric networks
Feature extraction
Impedance
Intrusion detection
intrusion detection systems
physics-informed features
Power systems
Radio frequency
random forest
Random forests
Reliability engineering
Security
Technological innovation
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The increasing complexity of electric power networks has heightened their vulnerability to cyber-attacks, challenging traditional Intrusion Detection Systems (IDS) that rely on manually crafted rules. This paper introduces a novel approach that integrates physics-informed features and feature selection into a Random Forest (RF) model to enhance IDS performance. By deriving features such as complex power and impedance from fundamental electrical principles and applying SelectKBest for optimal feature selection, our method not only improves detection accuracy but also enhances efficiency by using fewer than half the features. Specifically, the featureenriched RF model utilizing 55 features achieves an accuracy of 0.9667 and an F1-score of 0.9664, compared to 0.9576 and 0.9570 for the baseline RF model. This approach demonstrates the effectiveness of advanced feature engineering and selection techniques for improving the security and reliability of power network monitoring systems.
ISSN:2770-7946
DOI:10.1109/ASYU62119.2024.10757087