| Title | Anomaly-based Intrusion Detection using GAN for Industrial Control Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | KK, Sabari, Shrivastava, Saurabh, V, Sangeetha. |
| Conference Name | 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) |
| Date Published | oct |
| Keywords | Adversarial training, Anomaly, classification, generative adversarial network, generative adversarial networks, ICS Anomaly Detection, industrial control, integrated circuits, Intrusion detection, intrusion detection system, Market research, pubcrawl, Real-time Systems, resilience, Resiliency, Scalability, Training |
| Abstract | In recent years, cyber-attacks on modern industrial control systems (ICS) have become more common and it acts as a victim to various kind of attackers. The percentage of attacked ICS computers in the world in 2021 is 39.6%. To identify the anomaly in a large database system is a challenging task. Deep-learning model provides better solutions for handling the huge dataset with good accuracy. On the other hand, real time datasets are highly imbalanced with their sample proportions. In this research, GAN based model, a supervised learning method which generates new fake samples that is similar to real samples has been proposed. GAN based adversarial training would address the class imbalance problem in real time datasets. Adversarial samples are combined with legitimate samples and shuffled via proper proportion and given as input to the classifiers. The generated data samples along with the original ones are classified using various machine learning classifiers and their performances have been evaluated. Gradient boosting was found to classify with 98% accuracy when compared to other |
| DOI | 10.1109/ICRITO56286.2022.9964997 |
| Citation Key | kk_anomaly-based_2022 |
Anomaly-based Intrusion Detection using GAN for Industrial Control Systems