Retracted: A Hybrid Multistage DNN-Based Collaborative IDPS for High-Risk Smart Factory Networks
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Abstract
New industrial control systems (ICSs) that have been modernized with the industrial Internet of Things (IIoT) are exposed to cyber-attacks that exploit IIoT vulnerabilities. Numerous intrusion detection systems (IDSs) have therefore been proposed to secure ICSs, many of which are based on machine learning, specifically deep neural networks (DNNs). Most of the proposed DNN-based solutions rely on single deep learning models and could be less costly in terms of ICS latency. However, they might have difficulties understanding the increasingly complex data distribution of intrusion patterns. Moreover, single deep learning models may not be effective in capturing the specific patterns of minority classes in highly imbalanced datasets, which is usually the case in cyber-security. Therefore, this paper proposes a novel hybrid multistage DNN-based intrusion detection and prevention system (IDPS) with better accuracy for critical ICSs that cannot afford to compromise on security to improve latency. The proposed approach sequentially learns the decision boundaries of the data that were misclassified or classified with low confidence by previous DNNs. Moreover, it incorporates a collaborative intrusion prevention system (IPS) with an emergency response schema that automatically mitigates attacks as soon as anomalies are detected. The results of experimental evaluations performed on different datasets demonstrate the effectiveness of the proposed solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- IEEE Transactions on Network and Service Management
- Topic
- Network Security and Intrusion Detection
- Field
- Computer Science
- Canadian institutions
- École de Technologie Supérieure
- Funders
- Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
- Keywords
- Computer scienceExploitIntrusion detection systemIndustrial control systemLatency (audio)Artificial intelligenceMachine learningDeep neural networksLow latency (capital markets)Schema (genetic algorithms)Artificial neural networkDeep learningThe InternetClassifier (UML)Data miningComputer securityComputer networkControl (management)
- Has abstract in OpenAlex
- yes