Outputs (76)

Enhancing Cybersecurity: Machine Learning and Natural Language Processing for Arabic Phishing Email Detection (2024)
Thesis
Salloum, S. (2024). Enhancing Cybersecurity: Machine Learning and Natural Language Processing for Arabic Phishing Email Detection. (Thesis). University of Salford

Phishing is a significant threat to the modern world, causing considerable financial losses. Although electronic mail has shown to be a valuable asset around the world in terms of facilitating communication for all parties involved, whether huge corp... Read More about Enhancing Cybersecurity: Machine Learning and Natural Language Processing for Arabic Phishing Email Detection.

A New English/Arabic Parallel Corpus for Phishing Emails (2023)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (in press). A New English/Arabic Parallel Corpus for Phishing Emails. #Journal not on list, https://doi.org/10.1145/3606031

Phishing involves malicious activity whereby phishers, in the disguise of legitimate entities, obtain illegitimate access to the victims’ personal and private information, usually through emails. Currently, phishing attacks and threats are being hand... Read More about A New English/Arabic Parallel Corpus for Phishing Emails.

Explainable fault prediction using learning fuzzy cognitive maps (2023)
Journal Article
Mansouri, T., & Vadera, S. (2023). Explainable fault prediction using learning fuzzy cognitive maps. Expert Systems, 40(8), https://doi.org/10.1111/exsy.13316

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify v... Read More about Explainable fault prediction using learning fuzzy cognitive maps.

A deep explainable model for fault prediction using IoT sensors (2022)
Journal Article
Mansouri, T., & Vadera, S. (2022). A deep explainable model for fault prediction using IoT sensors. IEEE Access, https://doi.org/10.1109/ACCESS.2022.3184693

IoT sensors and deep learning models can widely be applied for fault prediction. Although deep learning models are considerably more potent than many conventional machine learning models, they are not transparent. This paper first examines differen... Read More about A deep explainable model for fault prediction using IoT sensors.

A systematic literature review on phishing email detection using natural language processing techniques (2022)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2022). A systematic literature review on phishing email detection using natural language processing techniques. IEEE Access, 10, 65703-65727. https://doi.org/10.1109/access.2022.3183083

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks... Read More about A systematic literature review on phishing email detection using natural language processing techniques.

Methods for pruning deep neural networks (2022)
Journal Article
Vadera, S., & Ameen, S. (2022). Methods for pruning deep neural networks. IEEE Access, 63280- 63300. https://doi.org/10.1109/ACCESS.2022.3182659

This paper presents a survey of methods for pruning deep neural networks. It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that... Read More about Methods for pruning deep neural networks.

Development of an evolutionary cost sensitive decision tree induction algorithm (2022)
Presentation / Conference
Kassim, M., & Vadera, S. (2022, May). Development of an evolutionary cost sensitive decision tree induction algorithm. Presented at 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya

This paper develops an Evolutionary Elliptical Cost-Sensitive Decision Tree Algorithm (EECSDT) which learns cost-sensitive non-linear decision trees for multiclass problems. EECSDT is developed by formulating the problem as an optimization task in wh... Read More about Development of an evolutionary cost sensitive decision tree induction algorithm.

Phishing website detection from URLs using classical machine learning ANN model (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing website detection from URLs using classical machine learning ANN model. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Internet), 2, 509-523. https://doi.org/10.1007/978-3-030-90022-9_28

Phishing is a serious form of online fraud made up of spoofed websites that attempt to gain users’ sensitive information by tricking them into believing that they are visiting a legitimate site. Phishing attacks can be detected many ways, including a... Read More about Phishing website detection from URLs using classical machine learning ANN model.

Cost-sensitive meta-learning framework (2021)
Journal Article
Shilbayeh, S., & Vadera, S. (2021). Cost-sensitive meta-learning framework. Journal of Modelling in Management, https://doi.org/10.1108/JM2-03-2021-0065

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classif... Read More about Cost-sensitive meta-learning framework.

Phishing email detection using Natural Language Processing techniques : a literature survey (2021)
Journal Article
Salloum, S., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing email detection using Natural Language Processing techniques : a literature survey. Procedia Computer Science, 189, 19-28. https://doi.org/10.1016/j.procs.2021.05.077

Phishing is the most prevalent method of cybercrime that convinces people to provide sensitive information; for instance, account IDs, passwords, and bank details. Emails, instant messages, and phone calls are widely used to launch such cyber-attacks... Read More about Phishing email detection using Natural Language Processing techniques : a literature survey.