Attack Detection in IoT using Machine Learning

Authors

  • M. Anwer Department of Computer Science & Information Technology, NED University of Engineer and Technology, Pakistan
  • S. M. Khan Department of Computer Science and IT, NED University of Engineering and Technology, Pakistan
  • M. U. Farooq Department of Computer Science & Information Technology, NED University of Engineer and Technology, Pakistan
  • . Waseemullah Department of Computer Science & Information Technology, NED University of Engineer and Technology, Pakistan

Abstract

Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

Keywords:

cyber security, artificial intelligence, IoT, machine learning

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How to Cite

[1]
M. Anwer, S. M. Khan, M. U. Farooq, and . Waseemullah, “Attack Detection in IoT using Machine Learning”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 3, pp. 7273–7278, Jun. 2021.

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