Abstract:
Machine Learning (ML)-based Intrusion Detection Systems (IDS) is an effective technology to automatically detect cyber attacks in the Internet of Things (IoT) dependent I...Show MoreMetadata
Abstract:
Machine Learning (ML)-based Intrusion Detection Systems (IDS) is an effective technology to automatically detect cyber attacks in the Internet of Things (IoT) dependent Industrial Control Systems (ICS). It is faster, more efficient, and can detect attacks without human intervention. However, ML-based IDSs have introduced another security threat called Adversarial Machine Learning (AML). An AML attack may cause severe industrial infrastructural and production damage resulting in substantial financial loss. This paper presents an exploratory analysis of initiating an AML attack using adversarial samples created using a Fast Gradient Sign Method (FGSM). The research presented in this paper has been conducted from a dataset generated from a full-fledged singular module of a power distribution industry controlled by IoT-enabled ICSs. We explored the AML attack on Gradient Boosting (GB) and Iterative Dichotomiser 3 (ID3) model and discovered the average classification accuracy, precision, recall, and F1-scores are 87%, 88%, 87.5%, and 87%, respectively. The AML attack reduces the average precision, recall, and F1-score by 20.5%, 20.5%, and 22.5%, respectively, when 50% perturbations are added to 10% samples.
Date of Conference: 05-06 February 2023
Date Added to IEEE Xplore: 05 April 2023
ISBN Information:
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- IEEE Keywords
- Index Terms
- Machine Learning ,
- Generative Adversarial Networks ,
- Industrial Control ,
- Adversarial Attacks ,
- Industrial Control Systems ,
- Internet Of Things ,
- Power Distribution ,
- Average Precision ,
- Gradient Boosting ,
- Intrusion Detection ,
- Average Recall ,
- Intrusion Detection System ,
- Infrastructure Damage ,
- Fast Gradient Sign Method ,
- Model Performance ,
- Training Data ,
- Learning Algorithms ,
- Machine Learning Models ,
- Power Generation ,
- Gradient Boosting Model ,
- Types Of Attacks ,
- Attack Success ,
- Circuit Breaker ,
- Feature Engineering ,
- Train Machine Learning Models ,
- Amount Of Noise ,
- Long Short-term Memory ,
- Malware ,
- Experimental Phase
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Machine Learning ,
- Generative Adversarial Networks ,
- Industrial Control ,
- Adversarial Attacks ,
- Industrial Control Systems ,
- Internet Of Things ,
- Power Distribution ,
- Average Precision ,
- Gradient Boosting ,
- Intrusion Detection ,
- Average Recall ,
- Intrusion Detection System ,
- Infrastructure Damage ,
- Fast Gradient Sign Method ,
- Model Performance ,
- Training Data ,
- Learning Algorithms ,
- Machine Learning Models ,
- Power Generation ,
- Gradient Boosting Model ,
- Types Of Attacks ,
- Attack Success ,
- Circuit Breaker ,
- Feature Engineering ,
- Train Machine Learning Models ,
- Amount Of Noise ,
- Long Short-term Memory ,
- Malware ,
- Experimental Phase
- Author Keywords