Performance Evaluation of Shallow learning techniques and Deep Neural Network for Cyber Security
D Preethi, Neelu Khare · 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) · 2020
The usage of Internet technology is drastically growing day by day. With this enormous raise, a considerable volume of data generated. Therefore, network security is also receiving its attention to secure from attacks. The intrusion detection system (IDS) performs an essential part in the particular domain of network security. This research work aims at evaluating the performance of shallow learning and deep learning methods, which are the subsets of machine learning algorithms. The implementation appropriately employed utilizing standard benchmark NSL-KDD dataset [10]. The experimentation involves 4 shallow learning algorithms and deep neural networks utilizing Tensor flow and python. The experimental results evaluated employing classification metrics, such as Accuracy, Precision, Recall, and F1-score.