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Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework
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DOI：10.1007/s10922-023-09722-7.png)
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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.
Keyword:
Attack detection
Cyber-security
Deep learning
Distributed framework
Feed forward neural network
Long short-term memory
期刊
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3.9
论文数:
1.0K
被引数:
1.3K
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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study网络安全入侵检测的深度学习: 方法、数据集和比较研究
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