Volume 14 , Issue 2 , PP: 253-262, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Anusooya .S 1 * , N. Revathi 2 , Sivakamasundari .P 3 , A. N. Duraivel 4 , S. Prabu 5
Doi: https://doi.org/10.54216/JCIM.140217
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.
Cyber Security , Network Attacks , Genetic Algorithm , Neural Network , DDoS Detection , Malware Detection , Swarm Intelligence , Nature-Inspired Techniques , Discrete Wavelet Transform , Artificial Bee Colony
[1] Mohammadi, S., & Babagoli, M. (2021). A hybrid modified grasshopper optimization algorithm and genetic algorithm to detect and prevent DDoS attacks. International Journal of Engineering, 34(4), 811-824.
[2] Gadzama, E. H. (2021). IMPROVED GENETICALLY OPTIMIZED NEURAL NETWORK ALGORITHM FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS (Doctoral dissertation).
[3] Reddy, K. P., Kodati, S., Swetha, M., Parimala, M., & Velliangiri, S. (2021, October). A hybrid neural network architecture for early detection of DDOS attacks using deep learning models. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (pp. 323-327). IEEE.
[4] Kajal, A., & Nandal, S. K. (2020). A hybrid approach for cyber security: improved intrusion detection system using Ann-Svm. Indian Journal of Computer Science and Engineering, 11(4), 325-412.
[5] Hosseini, S., & Zade, B. M. H. (2020). New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN. Computer Networks, 173, 107168.
[6] Saber, A., Abbas, M., & Fergani, B. (2020, December). A DDoS attack detection system: applying a hybrid genetic algorithm to optimal feature subset selection. In 2020 4th International Symposium on Informatics and its Applications (ISIA) (pp. 1-6). IEEE.
[7] Oreški, D., & Andročec, D. (2020, September). Genetic algorithm and artificial neural network for network forensic analytics. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1200-1205). IEEE.
[8] Qazi, E. U. H., Faheem, M. H., & Zia, T. (2023). HDLNIDS: hybrid deep-learning-based network intrusion detection system. Applied Sciences, 13(8), 4921.
[9] Ramya, P., Chandra, Himagiri. Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP. Journal of Journal of Cybersecurity and Information Management, vol. 14, no. 2, 2024, pp. 161-172. DOI: https://doi.org/10.54216/JCIM.140211.
[10] Sagu, A., Gill, N. S., & Gulia, P. (2022). Hybrid deep neural network model for detection of security attacks in IoT enabled environment. International Journal of Advanced Computer Science and Applications, 13(1).
[11] Halim, Z., Yousaf, M. N., Waqas, M., Sulaiman, M., Abbas, G., Hussain, M., ... & Hanif, M. (2021). An effective genetic algorithm-based feature selection method for intrusion detection systems. Computers & Security, 110, 102448.
[12] Barati, M., Abdullah, A., Udzir, N. I., Mahmod, R., & Mustapha, N. (2014, August). Distributed Denial of Service detection using hybrid machine learning technique. In 2014 International Symposium on Biometrics and Security Technologies (ISBAST) (pp. 268-273). IEEE.
[13] Muthusamy, P., A.Rajan. ,, R.Praveena. , Rajulu, Sundara. , Babu, T.R.Ganesh. , Murugan, K.Sakthi. Optimized Group-Centric Data Routing in Heterogeneous Wireless Sensor Networks for Enhanced Energy Efficiency. Journal of Journal of Cybersecurity and Information Management, vol. 14, no. 2, 2024, pp. 173-185. DOI: https://doi.org/10.54216/JCIM.140212.
[14] Subashini, P., Krishnaveni, M., Dhivyaprabha, T. T., & Shanmugavalli, R. (2020). Review on intelligent algorithms for cyber security. In Handbook of Research on Machine and Deep Learning Applications for Cyber Security (pp. 1-22). IGI Global.
[15] Sureshkumar, S., Prasanna, G. K. D., & Santhosh, R. (2023). Adaptive Butterfly Optimization Algorithm (ABOA) Based Feature Selection and Deep Neural Network (DNN) for Detection of Distributed Denial-of-Service (DDoS) Attacks in Cloud. Computer Systems Science & Engineering, 47(1).
[16] Ramesh, S., Yaashuwanth, C., Prathibanandhi, K., Basha, A. R., & Jayasankar, T. (2021). An optimized deep neural network based DoS attack detection in wireless video sensor network. Journal of Ambient Intelligence and Humanized Computing, 1-14.
[17] Hussan, M. T., Reddy, G. V., Anitha, P. T., Kanagaraj, A., & Naresh, P. (2023). DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization. Cluster Computing, 1-22.
