Detection of Attacks and Anomalies in The Internet of Things System Using Neural Networks Based on Training with PSO and TLBO Algorithms

Authors

1 Department of Information Technology Management, Islamic Azad University, Central Tehran Branch, Tehran, Iran

2 Assistant Professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran

Abstract

Detecting attacks and anomalies is one of the new challenges in commercializing and advancing IOT technology. One of the most effective methods for detecting attacks is the machine learning algorithms. Until now, many ML models have been suggested to detect attacks and anomalies, all of them use experimental data to model the detection process. One of the most popular and efficient ML algorithms is the artificial neural network. Neural networks also have different classical learning methods. But all of these classic learning methods are problematic for systems that have a lot of local optimized points or have a very complex target function so that they get stuck in local optimal points and are unable to find the global optimal point. The use of evolutionary optimization algorithms for neural network training can be an effective and interesting method. These algorithms have the capability to solve very complex problems with multi-purposed functions and high constraints. Among the evolutionary algorithms, the particle swarm optimization algorithm is fast and popular. Hence, in this article, we use this algorithm to train the neural network to detect attacks and anomalies of the Internet of Things system. Although the PSO algorithm has so many merits, in some cases it may reduce population diversity, resulting in premature convergence. So, in order to solve this problem, we make use of the TLBO algorithm and also, we show that in some cases, up to 90% accuracy of attack detection can be obtained.

Keywords

Main Subjects

Signal Processing and Renewable Energy

Volume 4, Issue 4
Autumn 2020
Pages 81-94

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History

  • Receive Date: 27 September 2020
  • Revise Date: 20 October 2020
  • Accept Date: 21 October 2020

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