Enhancing Multiclass Network Intrusion Detection Systems Using Continuous Wavelet Transform on Network Traffic

Network systems are susceptible to cyberattacks, which motivates attackers to exploit their vulnerabilities. Scanning network traffic to identify malicious activity is becoming a trend in the cybersecurity domain to mitigate the negative effects of intruders. Network intrusion detection systems (NIDS) are widely recognized as essential tools against cyberattacks. However, there is a need to go beyond designing traditional NIDS, which are preferred to be used with binary classification, towards designing multiclass network intrusion detection systems (MNIDS) to predict the cyberattack category. This, indeed, assists in understanding cyberattack behavior, which mitigates their effects quickly. Machine learning models, including conventional and deep learning, have been widely employed in the design of MNIDS. However, MNIDS based on machine learning can face challenges in predicting the category of cyberattack, especially with complex data that has a large number of categories. Thus, this paper proposes an enhanced MNIDS by exploiting the power of integrating continuous wavelet transform (CWT) with machine learning models to increase the accuracy of predicting cyberattacks in network traffic. This is due to the fact that CWT is considered as an effective method for feature extraction. The experimental results emphasize that using CWT with machine learning models improves the classification performance of MNIDS by up to 3.36% in overall accuracy. Additionally, it enhances the F1-score value in up to 40% of the total classes using the proposed model.