A Detection of Intrusions Based on Deep Learning

Abstract

The use of network intrusion detection systems is expanding as cloud computing becomes more widespread. Network intrusion detection systems (NIDS) are crucial to network security since network traffic is increasing and cyberattacks are being launched more frequently. Algorithms for detecting anomalies in intruder detection use either machine learning systems or pattern matching systems. Pattern-matching methods frequently produce false positive results, while AI/ML-based systems predict possible assaults by identifying connections between metrics, features, or collections of metrics, features. KNN, SVM, and other models are the most widely used, but they only apply to a few features, are not very accurate, and have a higher false positive rate. This proposal developed a deep learning model that combines the benefits of two-dimensional LSTMs and convolutional neural networks to learn the characteristics of spatial and temporal data. The study’s model was developed and evaluated using the freely available NSL-KDD dataset. The suggested model is very effective, having a low rate of false positives and a high rate of detection. Some sophisticated network intrusion detection systems use machine learning and deep learning models, and their performance is superior to that of the proposed model.