Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing
A Generative Adversarial Network-Based Approach
Bibliographic Data
| ID | 22106942 |
|---|---|
| Authors | Laisen Nie (0000-0001-8813-7738, Northwestern Polytechnical University), Yixuan Wu (0009-0004-9844-970X, Northwestern Polytechnical University), Xiaojie Wang (0000-0001-5570-6121, Chongqing University of Posts and Telecommunications), Lei Guo (0000-0003-0537-7280, Chongqing University of Posts and Telecommunications), Guoyin Wang (0000-0002-8521-5232, Chongqing University of Posts and Telecommunications), Xinbo Gao (0000-0002-2412-2841, Chongqing University of Posts and Telecommunications), Shengtao Li (0000-0002-4397-0694, Shandong Normal University) |
| Year | 2022 |
| Volume | 9 |
| Issue | 1 |
| Pages | 134-145 |
| Publication date | 2022-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3063538 |
| OpenAlex | W3137750955 |
| Language | EN |
| Citations received | 3 |
| References cited | 40 |
The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users’ privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-in-the-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method
Adversarial system · Computer network · Computer security · Edge computing · Enhanced Data Rates for GSM Evolution · Generative grammar · Internet of Things · Internet privacy · Intrusion detection system · The Internet · World Wide Web · Advanced Malware Detection Techniques · Computer Science · Internet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |