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All Journal Journal of Applied Data Sciences
Hiari, Mohammad O
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Performance Comparison of Whale and Harris Hawks Optimizers with Network Intrusion Prevention Systems Abualhaj, Mosleh M.; Al-Khatib, Sumaya N; Alsharaiah, Mohammad A; Hiari, Mohammad O
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.323

Abstract

Digital technology has permeated every aspect of our daily lives. Processing and evaluating information are highly demanding in all fields, including cybersecurity. Cybersecurity engineers widely use the Network Intrusion Prevention System (NIPS) to safeguard against cyberattacks. To avoid cyberattacks, the NIPS must deal with a large amount of data, which degrades its performance. This paper uses the whale optimization algorithm (WOA) and the Harris Hawks optimization method (HHO) to diminish the large amount of data that the NIPS needs to deal with. Subsequently, the Gradient Boosting Machine (GBM) is employed to determine the accuracy achieved when employing WOA and HHO. The GBM classifier is widely regarded as a sophisticated and straightforward classifier in data mining. Regardless of the premise of feature independence, it outperforms all other classification algorithms by delivering excellent performance. When using GBM, the findings indicate that the accuracy achieved with HHO is 89.81%, but the accuracy attained with WOA is 94.3%.
Spam Feature Selection Using Firefly Metaheuristic Algorithm Abualhaj, Mosleh M; Hiari, Mohammad O; Alsaaidah, Adeeb; Al-Zyoud, Mahran; Al-Khatib, Sumaya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.336

Abstract

This paper presents a novel method for improving spam detection by utilizing the Firefly Algorithm (FA) for feature selection. The FA, a bio-inspired metaheuristic optimization algorithm, is applied to identify the most relevant features from the ISCX-URL2016 dataset, which contains 72 features. By balancing exploration (searching for new solutions) and exploitation (focusing on the best solutions), FA is able to effectively reduce the feature space from 72 to 31 features. This reduction improves model efficiency without sacrificing performance, as only the most impactful features are retained for the classification task. The selected features were then used to train three machine learning classifiers: Decision Tree (DT), Gradient Boost Tree (GBT), and Naive Bayes (NB). Each classifier's performance was evaluated based on accuracy, with DT achieving the highest accuracy of 99.81%, GBT achieving 99.70%, and NB scoring 90.33%. The superior performance of the DT algorithm is attributed to its ability to handle non-linear relationships and high-dimensional data, making it particularly well-suited for the FA-selected features. This combination of FA for feature selection and DT for classification demonstrates significant improvements in spam detection performance, highlighting the importance of selecting the most relevant features. The results show that by reducing the dimensionality of the dataset, the FA algorithm not only accelerates the classification process but also enhances detection accuracy.
Co-Authors Abualhaj, Mosleh M Abualhaj, Mosleh M. Al-Khatib, Sumaya Al-Khatib, Sumaya N Al-Zyoud, Mahran Alsaaidah, Adeeb Alsharaiah, Mohammad A