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A Systematic Approach for IoT Cyber-Attacks Detection in Smart Cities Using Machine Learning Techniques

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 226))

Abstract

In these last years, the widespread adoption of the Internet of Things (IoT) concept led to the invention of intelligent cities. Smart cities operate in real time to promote lightness and life quality to citizens in urban cities. Smart city network traffic through IoT systems is growing exponentially though it presents new cyber-security threats. To deal with cyber-security in smart cities, developers need to improve new methods and approaches for detecting infected IoT devices and cyber-attacks. In this paper, we address IoT cyber security challenges, threats and solutions in intelligent cities. We propose an approach for anomaly detection in smart cities applications, networks and systems. Our solution relies on intelligent anomalies as vulnerabilities and threats detection based on different methods and machine learning algorithms. The proposed solution helps in effectively detecting and localizing infected IoT devices as well as generating alerts and reports. To experiment our solution, we used the dataset NSL-KDD to evaluate the accuracy of the model. Obtained results show that our model achieved a high classification accuracy of 99.31% with a low false positive rate.

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Author information

Authors and Affiliations

  1. Higher School of Communication of Tunis (Sup’Com), LR18TIC01 Digital Security Research Lab, University of Carthage, Tunis, Tunisia

    Mehdi Houichi & Faouzi Jaidi

  2. National School of Engineers of Carthage, University of Carthage, Tunis, Tunisia

    Faouzi Jaidi

  3. Department of Next-Generation Computing, College of Graduate Studies, Arabian Gulf University, Manama, Kingdom of Bahrain

    Adel Bouhoula

Authors
  1. Mehdi Houichi
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  2. Faouzi Jaidi
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  3. Adel Bouhoula
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Corresponding author

Correspondence to Mehdi Houichi .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Houichi, M., Jaidi, F., Bouhoula, A. (2021). A Systematic Approach for IoT Cyber-Attacks Detection in Smart Cities Using Machine Learning Techniques. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_17

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