| Autor: |
Kumar Lingamallu Raghu, Balasubramani Pradeep, Arvind S., Rao P. Srinivasa, Ammisetty Veeraswamy, Gurnadha Gupta Koppuravuri, Sharath M.N., Nagendra Kumar Y.J., Mittal Vaibhav |
| Jazyk: |
English<br />French |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
MATEC Web of Conferences, Vol 392, p 01103 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2261-236X |
| DOI: |
10.1051/matecconf/202439201103 |
| Popis: |
Ensuring device security is a significant obstacle to effectively implementing the Internet of Things (IoT) and fog computing in today's Information Technology (IT) landscape. Researchers and IT firms have investigated many strategies to safeguard systems against unauthorized device assaults, often known as outside device assaults. Cyber-attacks and data thefts have significantly risen in many corporations, organizations, and sectors due to exploiting vulnerabilities in safeguarding IoT gadgets. The rise in the variety of IoT gadgets and their diverse protocols has increased zero-day assaults. Deep Learning (DL) is very effective in big data and cyber-security. Implementing a DL-based Gated Recurrent Unit (GRU) on IoT devices with constrained resources is unfeasible due to the need for substantial computational power and robust storage capacities. This study introduces an IoT-based Malicious Device Detection (IoT-MDD) that is dispersed, resilient, and has a high detecting rate for identifying various IoT cyber-attacks using deep learning. The suggested design incorporates an Intrusion Detection System (IDS) on fog nodes because of its decentralized structure, substantial processing capabilities, and proximity to edge gadgets. Tests demonstrate that the IoT-MDD model surpasses the performance of the other models. The study found that the cybersecurity architecture effectively detects malicious gadgets and decreases the percentage of false IDS alarms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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