| Autor: |
Devaraju, Sellappan, Soni, Dheresh, Jawahar, Sundaram, Maurya, Jay Prakash, Tiwari, Vipin |
| Předmět: |
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| Zdroj: |
International Journal of Safety & Security Engineering; Oct2024, Vol. 14 Issue 5, p1431-1437, 7p |
| Abstrakt: |
Network Intrusion Detection Systems (NIDS) are a difficult task for determining in any managerial information system or IT sectors, if a user is a normal user or an attacker. The main objectives of the proposed system are to enhance operational efficiency, decreasing the occurrence of false positives, to minimize the time complexity of the process. It is an excellent way for dealing with various types of network problems. Research focusses the various classifiers are applied to detect various types of network assaults. Performance of network intrusion detection by two classifiers are used to compare the results. Probabilistic Neural Network (PNN) and Feed Forward Neural Network (FFNN) classifiers are employed this suggested study. The performance results comparison between full featured and reduced features are presented. MATLAB software application is applied to test the performance of both test and train dataset. Detecting network intrusions is a critical challenge within managerial information systems and the IT sector, as it involves the complex task of distinguishing between legitimate users and potential attackers. Maintaining a secure network environment is paramount to safeguarding sensitive information and operations. In the arena of network intrusion detection, the research predominantly revolves around the deployment of diverse classifiers to identify various types of network attacks. This paper, proposes the evaluation of two specific classifiers, the PNN and the FFNN, with the objective of comparing their performance in the context of network intrusion detection. We systematically assess their effectiveness in both full-featured and reduced-feature scenarios, utilizing MATLAB software to rigorously analyze their capabilities across test and training datasets. In essence, this research delves into the intricate realm of Network Intrusion Detection Systems (NIDS), investigating how the PNN and FFNN classifiers function in the critical role of safeguarding networks against a multitude of potential threats. Through comprehensive analysis, we aim to illuminate the most efficient approach to enhancing network security in the constantly evolving landscape of cybersecurity. As a result, it is recommended that FFNN approaches be adopted as a means of improving detection efficiency and reducing the False Positive Rate (FPR) in network intrusion detection systems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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