Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework.
- Resource Type
- Article
- Authors
- Jullian, Olivia; Otero, Beatriz; Rodriguez, Eva; Gutierrez, Norma; Antona, Héctor; Canal, Ramon
- Source
- Journal of Network & Systems Management. Apr2023, Vol. 31 Issue 2, p1-24. 24p.
- Subject
- *SMART devices
*INTERNET of things
*COMPUTER network security
*SECURITY systems
*DEEP learning
*INTRUSION detection systems (Computer security)
- Language
- ISSN
- 1064-7570
The widespread use of smart devices and the numerous security weaknesses of networks has dramatically increased the number of cyber-attacks in the internet of things (IoT). Detecting and classifying malicious traffic is key to ensure the security of those systems. This paper implements a distributed framework based on deep learning (DL) to prevent many different sources of vulnerability at once, all under the same protection system. Two different DL models are evaluated: feed forward neural network and long short-term memory. The models are evaluated with two different datasets (i.e.NSL-KDD and BoT-IoT) in terms of performance and identification of different kinds of attacks. The results demonstrate that the proposed distributed framework is effective in the detection of several types of cyber-attacks, achieving an accuracy up to 99.95% across the different setups. [ABSTRACT FROM AUTHOR]