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Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things

  • Maria Habib
  • Ibrahim Aljarah
  • Hossam Faris
  • Seyedali MirjaliliEmail author
Chapter
Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Nowadays, the world witnesses an immense growth in Internet of things devices. Such devices are found in smart homes, wearable devices, retail, health care, industry, and transportation. As we are entering Internet of things (IoT) digital era, IoT devices not only hack our world, but also start to hack our personal life. The widespread IoT has created a rich platform for potential IoT cyberattacks. Data mining and machine learning techniques have significant roles in the field of IoT botnet detection. The aim of this chapter is to develop detection model based on multi-objective particle swarm optimization (MOPSO) for identifying the malicious behaviors in IoT network traffic. The performance of MOPSO is verified against multi-objective non-dominating sorting genetic algorithm (NSGA-II), common traditional machine learning algorithms, and some conventional filter-based feature selection methods. As per the obtained results, MOPSO is competitive and outperforms NSGA-II, traditional machine learning methods, and filter-based methods in most of the studied datasets.

Keywords

Internet of things Classification Multi-objective particle swarm optimization Non-dominating sorting genetic algorithm Multi-objective feature selection Botnets 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Maria Habib
    • 1
  • Ibrahim Aljarah
    • 1
  • Hossam Faris
    • 1
  • Seyedali Mirjalili
    • 2
    • 3
    Email author
  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Torrens University AustraliaBrisbaneAustralia
  3. 3.Griffith UniversityBrisbaneAustralia

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