Optimization Analysis of CNN-based Deep Learning System for Autonomous Detection of IoT Botnet Attacks
Keywords:
CNN, Botnet Attacks, Precision, F_measureAbstract
The rapid growth of Internet of Things (IoT) devices has significantly increased the attack surface of modern networks, making them highly vulnerable to large-scale botnet attacks such as Distributed Denial of Service (DDoS), data exfiltration, and remote device takeover. Conventional intrusion detection systems based on signatures and handcrafted features are inadequate for identifying sophisticated and evolving IoT botnet behaviors in real time. To address these challenges, this paper presents an optimized Convolutional Neural Network (CNN)-based deep learning system for autonomous detection of IoT botnet attacks. This paper proposed for efficient botnet detection in IoT networks using deep learning algorithms such as LSTM and CNN. The effectiveness of this method was validated by performing extensive experiments with the most relevant publicly available dataset (Bot-IoT) in binary and multi-class classification scenarios. Simulation is performed using python spyder 3.7 software. It is clear from the simulation results the precision of the proposed work is 97 % while in the previous work it is 100 %. Similarly, the other parameter F_Measure is 99 % by the proposed work and 96 % by the previous work.
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