Real-time attack analysis and defense technology for IoT

The research addresses the increase in cyber attacks on IoT networks and explores the use of multi-class classification techniques to improve current iterations of intrusion detection systems. The research used an innovative dataset known as TON_IoT, to perform feature extraction in identifying net...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Sie, Jovan
مؤلفون آخرون: Liu Yang
التنسيق: Final Year Project
اللغة:English
منشور في: Nanyang Technological University 2024
الموضوعات:
IoT
الوصول للمادة أونلاين:https://hdl.handle.net/10356/181159
الوسوم: إضافة وسم
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الوصف
الملخص:The research addresses the increase in cyber attacks on IoT networks and explores the use of multi-class classification techniques to improve current iterations of intrusion detection systems. The research used an innovative dataset known as TON_IoT, to perform feature extraction in identifying network attacks, and for training and testing of 6 different classification models, which were then filtered to be implemented into an intrusion detection system for real-world testing. Features for each attack type in the dataset were analysed and rated in importance on distinguishing the individual attacks from benign traffic. The 6 classification models yielded varying results, with one attaining a value of 0.98 in accuracy. The models were tested against real-world data attaining an accuracy of 0.68. The study proposes the use of multi-class classification in performing anomaly-based intrusion detection systems, to create accurate and tailored response for different attack types.