Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohd Faizal Ab Razak
University Malaysia Pahang

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A malicious URLs detection system using optimization and machine learning classifiers Ong Vienna Lee; Ahmad Heryanto; Mohd Faizal Ab Razak; Anis Farihan Mat Raffei; Danakorn Nincarean Eh Phon; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1210-1214

Abstract

The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy.  The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.
Phishing detection system using machine learning classifiers Nur Sholihah Zaini; Deris Stiawan; Mohd Faizal Ab Razak; Ahmad Firdaus; Wan Isni Sofiah Wan Din; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1165-1171

Abstract

The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.
Co-Authors Ahmad Firdaus Ahmad Heryanto Anis Farihan Mat Raffei Danakorn Nincarean Eh Phon Deris Stiawan Nur Sholihah Zaini Ong Vienna Lee Shahreen Kasim Tole Sutikno Wan Isni Sofiah Wan Din