This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated Data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences.

One of the primary concerns of governments, corporations, and even individual users is their level of online protection. This is because a large number of attacks target their primary assets. A firewall is a critical tool that almost every organization uses to protect its assets. However, firewalls become less reliable when they deal with large amounts of data. One method for reducing the amount of data and enhancing firewall performance is feature selection. The main aim of this study is to enhance the firewall's performance by proposing a new feature selection method. The proposed feature selection method combines the strengths of Harris Hawks optimization (HHO) and whale optimization algorithm (WOA). Experiments were performed utilizing the NSL-KDD dataset to measure the effectiveness of the proposed method. The experiments employed the decision trees (DTs) as a machine classifier. The experimental results show that the achieved accuracy is 98.46% when using HHO/WOA for feature selection and DT for classification, outperforming the HHO and WOA when used separately for feature selection. The study's findings offer insightful information for researchers and practitioners looking to improve firewall effectiveness and efficiency in defending internet connections against changing threats.

Uniform resource locator (URL) defacement attack can be defined as any cyberattack in which the attacker replaces the appearance or content of the targeted webpage with their own that is intended to disgrace, mislead, or malign the website. Detecting URL defacement attacks is significant to avoid breaching the security of the website content or its configuration files, modifying the file locations, templates, or attacks on the website environment and applications. A machine learning (ML) technique can be used to detect the defacement attack on any website with complex content and structure, as opposed to the classical techniques for detection, such as Diff comparison, Document Object model tree analysis, and checksum, which can only be applied to static websites. This article proposes a feature selection model based on particle swarm optimization with support vector machine, decision tree, random forest, Naive Bayes, and k-nearest neighbor ML classification algorithms. The proposed model aims to improve the URL defacement attack detection by selecting the best features from the ISCX-URL-2016 dataset. Then, the reduced set of features produced by the proposed model step is used as input to evaluate and compare the results of the used ML classifiers. The results showed that the proposed model has significantly reduced the features, regarding the classification's feature reduction, the random forest classifier outperformed other classifiers in terms of true positive rates, accuracy, precision, sensitivity, and F-measure, whereas the proposed model with random forest classifier has 99.21% True positive rates, 99.29% accuracy rate, 99.38% precision rate, 99.21% sensitivity rate, and 99.29% F-measure rate. In the future directions of this article, more research should be done on a variety of things, including varying and sophisticated techniques of altering the URL defacement since it would better calibrate the model for application in real-life situations. Received: 28 October 2024 | Revised: 16 December 2024 | Accepted: 1 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in URL dataset (ISCX-URL2016) at: https://www.unb.ca/cic/datasets/url-2016.html. Author Contribution Statement Omar Almomani: Conceptualization, Methodology, Software, Investigation, Writing – original draft, Supervision, Project administration. Adeeb Alsaaidah: Conceptualization, Formal analysis, Writing – original draft. Ahmad Adel Abu-Shareha: Methodology, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization. Abdullah Alzaqebah: Software, Resources, Data curation, Writing–review & editing, Visualization. Mohammed Amin Almaiah: Validation, Resources, Writing – original draft. Qusai Shambour: Resources, Writing – original draft

In this paper, we propose a hybrid intrusion detection system (IDS) that leverages Harris Hawks optimization (HHO) and whale optimization algorithm (WOA) for feature selection to enhance the detection of cyberattacks. The hybrid approach reduces the dimensionality of the NSL KDD dataset, allowing the IDS to operate more efficiently. The reduced feature set is then classified using logistic regression (LR) and gradient boosting machine (GBM) classifiers. Performance evaluation demonstrates that the GBM-HHO/WOA combination outperforms the LR-HHO/WOA approach, achieving an accuracy of 97.68%. These results indicate that integrating HHO and WOA significantly improves the IDS's ability to identify intrusions while maintaining high computational efficiency. This research highlights the potential of advanced optimization techniques to strengthen network security against evolving threats.