2024

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Browsing 2024 by Author "Ahmad Bin Sajid, Sami Ullah, Muhammad Hadeed Ullah and Raja Anas Basheer Khan"

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    Zulfiqar: Web Based Botnet Detection in IoT Network Using Machine Learning
    (UMT, Lahore, 2024) Ahmad Bin Sajid, Sami Ullah, Muhammad Hadeed Ullah and Raja Anas Basheer Khan
    As the era of interconnected devices through the Internet of Things (IoT) continues to expand, the proliferation of botnet attacks poses a significant threat to the security and privacy of IoT networks. This research endeavors to address the challenge of botnet attacks by employing advanced machine learning algorithms for detection. The proposed solution, named Zulfiqar, aims to enhance the safety of IoT networks by developing a system capable of real-time detection and alerting against various types of botnet attacks. The research explores key questions surrounding the detection of botnets in IoT networks, including training computers to identify these threats, recognizing the creation of botnets within a system, detecting ongoing botnet attacks through live network traffic analysis, and understanding the challenges faced by existing security systems. The methodology involves the use of diverse datasets, including the focused examination of CICIoT2023, and the application of smart machine learning algorithms such as Logistic Regression, Deep Neural Network (DNN), and Random Forest. The significance of this study lies in its contribution to safeguarding IoT networks from the escalating danger of botnets. Zulfiqar is designed not as a one-size-fits-all solution but as an adaptive system capable of addressing the specific challenges each IoT device faces when connected to the internet. The proposed methodology integrates proven machine learning techniques with innovative algorithms, aiming to redefine the landscape of IoT botnet detection. The literature review highlights the current state of knowledge in the field, emphasizing the application of machine learning and artificial neural network algorithms for detecting botnets in IoT ecosystems. Despite the progress, the review identifies gaps and emphasizes the need for further research in this direction.