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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Munther, Alhamza
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Computer Science & IT Electrical & Electronics Engineering

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A significant features vector for internet traffic classification based on multi-features selection techniques and ranker, voting filters Munther, Alhamza; Abualhaj, Mosleh M.; Alalousi, Alabass; Fadhil, Hilal A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6958-6968

Abstract

The pursuit of effective models with high detection accuracy has sparked great interest in anomaly detection of internet traffic. The issue still lies in creating a trustworthy and effective anomaly detection system that can handle massive data volumes and patterns that change in real-time. The detection techniques used, especially the feature selection methods and machine learning algorithms, are crucial to the design of such a system. The fundamental difficulty in feature selection is selecting a smaller subset of features that are more related to the class but are less numerous. To reduce the dimensionality of the dataset, this research offered a multi-feature selection technique (MFST) using four filter techniques: fast correlation-based filter, significance feature evaluator, chi-square, and gain ratio. Each technique's output vector is put via ranker and Borda voting filters. The feature with the highest number of votes and rank values will be selected from the dataset. The performance of the given MFST framework was the best when compared to the four strategies listed above functioning alone; three different classifiers were employed to test the accuracy. C4.5, nave Bayes, and support vector machine. The experiment outcomes employed ten datasets of different sizes with 10,000-300,000 instances. Only 8 out of 248 characteristics were chosen, with classifiers percentages averaging 65%, 93.8%, and 95.5%.
Enhancing spyware detection by utilizing decision trees with hyperparameter optimization Abualhaj, Mosleh M.; Al-Shamayleh, Ahmad Sami; Munther, Alhamza; Alkhatib, Sumaya Nabil; Hiari, Mohammad O.; Anbar, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7939

Abstract

In the realm of cybersecurity, spyware has emerged as a formidable adversary due to its persistent and stealthy nature. This study delves deeply into the multifaceted impact of spyware, meticulously examining its implications for individuals and organizations. This work introduces a systematic approach to spyware detection, leveraging decision trees (DT), a machine-learning classifier renowned for its analytical prowess. A pivotal aspect of this research involves the meticulous optimization of DT's hyperparameters, a critical operation for enhancing the precision of spyware threat identification. To evaluate the efficacy of the proposed methodology, the study employs the Obfuscated-MalMem2022 dataset, well-regarded for its comprehensive and detailed spyware-related data. The model is implemented using the Python programming language. Significantly, the findings of this study consistently demonstrate the superiority of the DT classifier over other methods. With an accuracy rate of 99.97%, the DT proves its exceptional effectiveness in detecting spyware, particularly in the face of more intricate threats. By advancing our understanding of spyware and providing a potent detection mechanism, this research equips cybersecurity professionals with a valuable tool to combat this persistent online menace.
Co-Authors Abualhaj, Mosleh M. Al-Shamayleh, Ahmad Sami Alalousi, Alabass Alkhatib, Sumaya Nabil Anbar, Mohammed Fadhil, Hilal A. Hiari, Mohammad O.