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Prevention and Propagation of Malware by Using Hybrid Adaptive Neuro-Fuzzy Interface System

Mohammed, Kaleemuddin (2019) Prevention and Propagation of Malware by Using Hybrid Adaptive Neuro-Fuzzy Interface System. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the wake of cybersecurity issues and the ever-growing incidents of cyber-attacks, it is essential to have a mechanism in a pace that can protect information system from malicious attacks. Malware detection is thus given importance. Adaptive Neuro-Fuzzy Interface System (ANFIS) is the algorithm with the machine learning approach which employs both fuzzy logic and also neural network. By making a hybrid of them, it provides a fuzzy inference system that can detect malware. In this project, the ANFIS algorithm is implemented using the Java programming language. It takes malware dataset and provides malware detection mechanism. It makes use of membership functions and the combination of fuzzy logic principles and neural network to have potential benefits of both the approaches in a single framework. A prototype application is built to show the effectiveness of the hybrid ANFIS algorithm for malware detection besides comparing its performance with state of the art.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 02 Apr 2020 14:00
Last Modified: 02 Apr 2020 14:00
URI: http://trap.ncirl.ie/id/eprint/4164

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