Dependable intrusion detection system using deep convolutional neural network: A Novel framework and performance evaluation approach

Abstract

Intrusion detection systems (IDS) play a critical role in safeguarding computer networks against unauthorized access and malicious activities. However, traditional IDS approaches face challenges in accurately detecting complex and evolving cyber threats. The proposed framework leverages the power of deep learning to automatically extract meaningful features from network traffic data, enabling more accurate and robust intrusion detection. The proposed deep convolutional neural network (DCNN) has been trained on large-scale datasets, incorporating both normal and malicious network traffic, to enable effective discrimination between normal and anomalous behavior. To evaluate the performance of the framework, a comprehensive performance evaluation approach is developed, considering key metrics such as detection accuracy, false positive rate, and computational efficiency. Additionally, GPU has been utilized for boosting the performance of the model, demonstrating the effectiveness and superiority of the deep CNN-based intrusion detection system over traditional methods. The novelty of this study lies in the development of a dependable intrusion detection system that harnesses the potential of DCNN for network traffic analysis. The proposed framework is evaluated with four publicly available IDS datasets, namely ISCX-IDS 2012, DDoS (Kaggle), CICIDS2017, and CICIDS2018. Our results demonstrate the effectiveness of the optimized DCNN model in improving IDS performance and accuracy. With detection accuracy levels ranging from 99.79% to 100%, our results underscore the model’s efficacy, offering a dependable and efficient approach for the detection of cyber threats. The outcomes of this study have significant implications for network security, providing valuable insights for practitioners and researchers working towards building robust and intelligent intrusion detection systems.