Adhikary, S., Anwar, M.M., Chowdhury, M.J.M., Sarker, I.H. (2022). Genetic Algorithm-Based Optimal Deep Neural Network for Detecting Network Intrusions. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_12
Adhikary, S., Anwar, M.M., Chowdhury, M.J.M., Sarker, I.H. (2022). Genetic Algorithm-Based Optimal Deep Neural Network for Detecting Network Intrusions. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_12
Adhikary, S., Anwar, M.M., Chowdhury, M.J.M., Sarker, I.H. (2022). Genetic Algorithm-Based Optimal Deep Neural Network for Detecting Network Intrusions. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_12
Adhikary, S., Anwar, M.M., Chowdhury, M.J.M., Sarker, I.H. (2022). Genetic Algorithm-Based Optimal Deep Neural Network for Detecting Network Intrusions. In: Skala, V., Singh, T.P., Choudhury, T., Tomar, R., Abul Bashar, M. (eds) Machine Intelligence and Data Science Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-19-2347-0_12
Abstract
Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology,most implementationsstilldependonsignature-basedintrusiondetection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detectionbinary classificationproblem.ExperimentalresultsdemonstratethattheproposedDNN architecture shows better performance than classical machine learning algorithms at a lower computationalcost.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
