AINIS: An Intelligent Network Intrusion System

doi:10.23940/ijpe.24.01.p4.2431

Abstract

Abstract: Machine learning algorithms have substantially increased the ability of intrusion detection (IDS) systems to identify and categorize cyber-attacks on the network and host levels in real time. But the fact that dangerous attacks happen frequently and are always evolving causes a number of issues that call for scalable solutions. This article discusses how to develop adaptive and powerful intrusion detection systems that can identify and classify unwanted and unplanned cyber-attacks using deep neural networks (DNNs), a form of deep learning model. A number of benchmarks damaging datasets that are accessible to the general public are provided with a full examination of DNN and other conventional machine learning classifier trials. The KDDCup99 and NSDL-KDD datasets are used in the following hyper parameter selection technique to determine the best DNN network parameters and network design. System administrators can use Network Intrusion Detection Systems (NIDS) to investigate network security vulnerabilities in their enterprises. To counter unconventional and unplanned assaults, there have been numerous attempts to develop reliable and effective NIDS. Each DNN experiment has 1,000 epochs and a learning rate between [0.01-0.5]. DNNs outperform traditional machine learning classifiers, according to the results of rigorous experimental testing.

Key words: cyber-attacks, network intrusion detection, deep learning, machine learning, deep neural networks, industrial IoT

Rahul Bhandari, Sanjay Singla, Purushottam Sharma, and Sandeep Singh Kang. AINIS: An Intelligent Network Intrusion System [J]. Int J Performability Eng, 2024, 20(1): 24-31.

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