A Meta-analysis of Role of Network Intrusion Detection Systems in Confronting Network Attacks

Traffic on the network is increasing immensely. Unprecedented development and close integration of devices for the Internet of Things (IoT) have contributed to an enormous amount of data in recent years. A persistent concern is the detection and prevention of network intrusions. In this paper, we have analyzed the Network Intrusion Detection System (NIDS) deployment, methodology, and the taxonomy of attacks detected by a NIDS. The purpose of the study is to emphasize the role of NIDS in confronting attacks. Deep Learning (DL), has significantly aided Machine Learning (ML) inefficiencies and produced time and cost-effective security solutions. By analyzing the work done by various researchers, it has been observed that deep learning algorithms give better accuracy and performance in confronting attacks with KDD Cup- 99 Dataset.