A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems (2023-04-04T00:00:00.000000Z)

TL;DR

Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision,0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

Abstract

The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

Authors

Sepideh Bahadoripour

1 papers

Ethan MacDonald

1 papers

H. Karimipour

1 papers

TL;DR

Abstract

Authors

References30 items

An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things

Enhancing IIoT networks protection: A robust security model for attack detection in Internet Industrial Control Systems

A Self-Tuning Cyber-Attacks’ Location Identification Approach for Critical Infrastructures

A new intrusion detection method for cyber-physical system in emerging industrial IoT

FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid

Deep Federated Learning-Based Cyber-Attack Detection in Industrial Control Systems

An intelligent cognitive computing based intrusion detection for industrial cyber-physical systems

Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems

Physical layer attack identification and localization in cyber-physical grid: An ensemble deep learning based approach

Unsupervised Stacked Autoencoders for Anomaly Detection on Smart Cyber-physical Grids

Anomaly detection in Industrial Control Systems using Logical Analysis of Data

A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT

Relaxation-based anomaly detection in cyber-physical systems using ensemble kalman filter

Attack Detection for Securing Cyber Physical Systems

Smart Grid Cyber Attacks Detection Using Supervised Learning and Heuristic Feature Selection

Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks

A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks

Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process Data

Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks

Distributed Quickest Detection of Cyber-Attacks in Smart Grid

Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism

A Dataset to Support Research in the Design of Secure Water Treatment Systems

Learning from imbalanced data: open challenges and future directions

Industrial Control System Network Intrusion Detection by Telemetry Analysis

Coordinated data-injection attack and detection in the smart grid: A detailed look at enriching detection solutions

50,000 security disasters waiting to happen: The problem of America’s water supplies

Artificial Intelligence and Security of Industrial Control Systems

A Framework for Attack-Resilient Industrial Control Systems: Attack Detection and Controller Reconfiguration

Field of Study

Journal Information

Name

Page

Venue Information

Name

Type

URL

Alternate Names