Rap-Densenet Framework for Network Attack Detection and Classification
Abstract
Cybersecurity is becoming increasingly important with the rise in Internet usage. The two most frequent cyberattacks that can seriously harm a website or a server and render them inaccessible to other customers are denial of service (DoS) and distributed denial of service (DDoS) attacks. These attacks are so common and take many different forms, it is difficult to identify and respond to them with previous methods. Furthermore, computational complexity, inconsistency, and irrelevant data are problems for traditional intrusion detection methods. As a result, a powerful deep learning-based technique is applied in this study for the identification and categorisation of DoS and DDoS attacks. Refined Attention Pyramid Network (RAPNet)-based feature extraction is used in this proposed framework to extract features from the input data. Then, Binary Pigeon Optimisation Algorithm (BPOA) is used to determine the best features. After choosing optimal characteristics, Densenet201-based deep learning is deployed to categorise the assaults in Bot-IoT, CICIDS2017, and CICIDS2019 datasets. Furthermore, the Conditional Generative Adversarial Network (CGAN) is used to provide extra data samples for minority classes to address the issue of imbalanced data. The findings show that the proposed model can precisely identify and categorise DoS and DDoS assaults in comparison to the existing intrusion detection approaches with 99.43%, 99.26%, and 99.38% accuracy for CICIDS2019, CICIDS2017, and BoT-IoT correspondingly.
