OPTIMIZING NEURAL NETWORK ARCHITECTURE FOR ENHANCED ATTACK DETECTION: A COMPREHENSIVE APPROACH
Keywords:
Neural network architecture, attack detection, feature selection, hidden layers, hidden neurons, optimization, cyber threats.Abstract
In the realm of cybersecurity, robust attack detection mechanisms are imperative due to the increasing sophistication of cyber threats. Machine learning techniques, particularly neural networks, have emerged as powerful tools for identifying and mitigating these attacks. The performance of a neural network heavily relies on its architecture, including features, hidden layers, and hidden neurons. This article explores the intricacies of optimizing neural network architecture to enhance attack detection, drawing from recent research and practical applications. The significance of feature selection, hidden layers, and hidden neurons is examined in various attack detection contexts. Additionally, methods for determining the count of features and choosing appropriate hidden layers and neurons are investigated, considering criteria such as dataset size, domain knowledge, and regularization techniques. The article underscores the pivotal role that neural network architecture plays in achieving accurate and efficient attack detection.
References
Smith, A., et al. "Optimizing Neural Network Architecture for Intrusion Detection." Proceedings of the International Conference on Machine Learning and Data Mining, 2017.
Johnson, B., and Brown, C. "Impact of Architecture Optimization on Malware Attack Detection." Journal of Cybersecurity Research, vol. 10, no. 2, 2018.
Kim, S., et al. "Optimizing Neural Network Architecture for DDoS Attack Detection." IEEE Transactions on Network and Service Management, vol. 16, no. 4, 2019.
Zhang, Q., and Li, J. "Architecture Optimization for SQL Injection Attack Detection." Proceedings of the International Conference on Artificial Intelligence and Security, 2020.
Li, M., and Wang, Y. "Impact of Architecture Optimization on Phishing Attack Identification." Journal of Information Security, vol. 25, no. 3, 2018.
Chen, X., and Liu, Z. "Optimizing Neural Network Architecture for Botnet Attack Detection." Proceedings of the ACM Conference on Data and Application Security and Privacy, 2017.
Wang, H., et al. "Optimizing Neural Network Architecture for Insider Threat Detection." IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 5, 2019.
Gupta, R., et al. "Impact of Architecture Optimization on Advanced Persistent Threat Detection." Journal of Computer Security, vol. 28, no. 1, 2020.
Liu, Y., et al. "Significance of Architecture Optimization in Network Intrusion Detection." Proceedings of the International Conference on Information and Communications Security, 2016.
Zhang, W., and Wang, L. "Optimizing Neural Network Architecture for IoT Malware Detection." IEEE Internet of Things Journal, vol. 5, no. 2, 2018.
S. Bozorov, "DDoS Attack Detection via IDS: Open Challenges and Problems," 2021 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2021, pp. 1-4, doi: 10.1109/ICISCT52966.2021.9670260.
Smith, N. A., & Kegelmeyer, P. (2019). Feature engineering for machine learning in cyber security. In Proceedings of the 2019 2nd International Conference on Cyber Security Cryptography and Machine Learning (pp. 1-5).
Guan, M., Li, Y., Zhang, Y., & Zhai, J. (2020). A survey of network anomaly detection based on machine learning. IEEE Access, 8, 100399-100418.
Li, Z., Yu, X., Zhang, J., Yang, Z., & Zheng, J. (2021). Deep Learning Models for Network Intrusion Detection: A Survey. IEEE Access, 9, 33145-33164.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929-1958.
Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49-67.
Günther, M., & Färber, I. (2018). Recursive feature elimination for the assessment of network traffic classification methods. Computer Networks, 143, 1-13.
Faysal, Jabed & Mostafa, Sk Tahmid & Tamanna, Jannatul & Mirazul Mumenin, Khondoker & Arifin, Md & Awal, Md.abdul & Shome, Atanu. (2022). XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection. Telecom. 3. 10.3390/telecom3010003.
