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

    Author(s)
    Habib, Maria
    Aljarah, Ibrahim
    Faris, Hossam
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
    Metadata
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    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 ...
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    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.
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    Book Title
    Evolutionary Machine Learning Techniques
    DOI
    https://doi.org/10.1007/978-981-32-9990-0_10
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/398830
    Collection
    • Book chapters

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