Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3029 2019 2019 Assistant Professor (Selection Grade) /ECE, B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India Anusooya Anusooya Assistant Professor, Department of ECE, Nehru Institute of Engineering and Technology, Coimbatore, India N. Revathi Assistant professor, Department of ECE, United Institute of Technology, Coimbatore, India Sivakamasundari .P Assistant Professor, Department of ECE, Kings Engineering College, Irungattukottai, India A. N. Duraivel Professor, Department of ECE, Mahendra Institute of Technology, Namakkal, India S. Prabu This study addresses the growing threat of network attacks by exploring their types and analyzing the challenges associated with their precise detection. To mitigate these threats, we propose a novel cyber security approach that integrates Genetic Algorithm (GA) and neural network architecture. The GA is employed for the selection and optimization of attributes that represent DDoS and malware attack features. These optimized features are then fed into a neural network for training and classification. The effectiveness of the proposed approach was evaluated through precision, recall, and F-measure analyses, demonstrating superior detection capabilities for DDoS and malware attacks compared to existing methods. Furthermore, we introduce a hybrid approach that combines Swarm Intelligence (SI) and nature-inspired techniques. The GA is utilized to select features and reduce the dataset size, followed by the application of Discrete Wavelet Transform (DWT) with Artificial Bee Colony (ABC) to further filter irrelevant features. The results show that this hybrid approach significantly enhances the accuracy and efficiency of network attack detection in wide area networks. 2024 2024 253 262 10.54216/JCIM.140217 https://www.americaspg.com/articleinfo/2/show/3029