IDS Based on Deep Learning Algorithms: Design, Implementation, and Performance Evaluation

IDSs will play a big role in defending network infrastructures from the ever-increasing development and sophistication of cyber threats. As attack vectors continue to increase in complexity, more traditional intrusion-detection-based techniques encounter difficulties with effective identification of attacks at the same time as reducing false positives. This paper explores conceptualization, implementation, and assessment of an Intrusion Detection System (IDS) using a change of advanced deep learning algorithms, which includes Simple RNN, LTM, GRU, CNN, and Hybrid architecture. The respective models are trained and fine-tuned on the NF-UNSW-NB15 dataset so they can have high detection precision as well as strong performance. All the deep learning algorithms were estimated in terms of detection accuracy, precision, recall, F1 score, and training time. The CNN-based IDS recorded the highest detection accuracy at 98.59% coupled with an F1 score of 84.2%, which proves its potential for real-time detection scenarios. The Hybrid model displayed good accuracy (86.31%) and recall (78.44%) values, however, its training took a lot of time due to the complexity needed for combining multiple architectures. Both the LTM and GRU achieved similar performance. For the former, accuracy attained 98.28%, and F1 was above 79%. It seems that the Simple RNN model has lower recall and F1, suggesting that certain kinds of attacks may have been missed. This paper summaries in what way deep learning models can help improve the precision and performance of an IDS.