AI NETWORK INTRUSION DETECTION SYSTEMUSING MULTI LAYER PERCEPTRON ALGORITHM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.4437Keywords:
Intrusion Detection, Machine Learning, Deep Learning, Multi-Layer Perceptron Algorithm, Network DatasetsAbstract [English]
An intrusion detection system, or IDS, is designed to be a software program that keeps an eye on system or network activity and alerts users when anything suspicious is happening. Concerns regarding how to safely transmit and preserve digital information are raised by the internet's explosive expansion and use. In order to obtain important information, hackers today employ a variety of attack techniques. New things like viruses and worms being imported as the internet becomes more prevalent in society. In order to create system vulnerabilities, malicious individuals employ a variety of methods, such as password cracking and the detection of unencrypted information. As a result, users require security to protect their system from hackers. One of the most often used security methods is the firewall mechanism, which is intended to keep private networks isolated from public networks. IDS are utilized in credit card fraud, medical applications, insurance agencies, and network-related operations. These assaults are detectable with the aid of numerous intrusion detection techniques, methods, and algorithms. This paper's primary goal is to present a comparative analysis of intrusion detection methods utilizing different deep learning and machine learning approaches. In this paper we can implement the multilayer perceptron algorithm to improve the accuracy in intrusion detection.
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Copyright (c) 2024 N.Karthigavani, K.Pradeep, V.Praveenraj, S.Poovarasan
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