Abstract

Network anomaly detection is critical for preserving cybersecurity and safeguarding sensitive data. Traditional approaches sometimes struggle with the complexity and amount of current network traffic. This research provides an upgraded network anomaly detection method utilizing convolutional neural networks (CNNs). Leveraging the BoT-IoT dataset, this paper utilize feature selection strategies based on entropy and correlation to develop a robust CNN feature matrix. The model showed considerable gains in identifying abnormalities, with a high accuracy rate of 96%. The application of the system in both offline and online modes illustrates its relevance in real-world cybersecurity operations. Detailed assessments, including training and testing timeframes, indicate the system's efficiency and efficacy. Future work will concentrate on increasing the dataset, incorporating additional deep learning models, and boosting real-time detection capabilities.

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• https://research.unsw.edu.au/projects/bot-iot-dataset