Efficient Cybersecurity Model Using Wavelet Deep CNN and Enhanced Rain Optimization Algorithm

Abstract

Cybersecurity has received greater attention in modern times due to the emergence of IoT (Internet-of-Things) and CNs (Computer Networks). Because of the massive increase in Internet access, various malicious malware have emerged and pose significant computer security threats. The numerous computing processes across the network have a high risk of being tampered with or exploited, which necessitates developing effective intrusion detection systems. Therefore, it is essential to build an effective cybersecurity model to detect the different anomalies or cyber-attacks in the network. This work introduces a new method known as Wavelet Deep Convolutional Neural Network (WDCNN) to classify cyber-attacks. The presented network combines WDCNN with Enhanced Rain Optimization Algorithm (EROA) to minimize the loss in the network. This proposed algorithm is designed to detect attacks in large-scale data and reduces the complexities of detection with maximum detection accuracy. The proposed method is implemented in PYTHON. The classification process is completed with the help of the two most famous datasets, KDD cup 1999 and CICMalDroid 2020. The performance of WDCNN_EROA can be assessed using parameters like specificity, accuracy, precision F-measure and recall. The results showed that the proposed method is about 98.72% accurate for the first dataset and 98.64% for the second dataset.