Name
Abstract | ||
|---|---|---|
Owing to the development of industrial production, the hidden danger in industrial control systems (ICSs) has considerably increased, causing challenges in traditional safety defense methods. The combination of machine-learning or deep-learning algorithms and intrusion detection systems (IDSs) has become the mainstream method for solving this problem. However, these methods depend on a massive amount of high-quality attack traffic data, which cannot be obtained easily owing to the independence and unique characteristics of ICSs. In this study, we apply the reconstructed convolutional neural network and a data expansion algorithm named CenterBorderline_SMOTE (CB_SMOTE) to an IDS and propose data expansion intrusion detection system (DEIDS). The DEIDS is an end-to-end detection model that learns representative attack features from raw traffic and classifies them in a unified framework. Moreover, we adopt the classification activation map structure, which can deeply mine the potential characteristics of traffic and enhance the effectiveness of attack features. While enhancing the data quality, we introduce the designed CB_SMOTE algorithm into DEIDS to expand the data and solve the problem of insufficient attack data in the system. Our comprehensive experiments on different open datasets indicate that DEIDS achieves an excellent performance (97
$$\%$$
detection accuracy) and outperforms the state-of-the-art methods. The experimental results also show that our method has high efficiency and high accuracy in processing ICSs datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-022-06965-4 | Neural Computing and Applications |
Keywords | DocType | Volume |
Industrial control system, Intrusion detection system, Data augmentation, SMOTE | Journal | 34 |
Issue | ISSN | Citations |
12 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoran Gu | 1 | 0 | 0.34 |
| Ying-xu. Lai | 2 | 37 | 13.05 |
| Yipeng Wang | 3 | 216 | 25.38 |
| Jing Liu | 4 | 1043 | 115.54 |
| Motong Sun | 5 | 0 | 0.34 |
| Beifeng Mao | 6 | 0 | 0.34 |