Transforming Data Representation: A Comparative Analysis of Tabular and Image-Based Approaches with XAI

Malik AL-Essa; Mohammad Alsharo; Yazan Alnsour; Wasim A. Ali; Omar Almomani

doi:10.47852/bonviewJCCE52026581

Authors

Malik AL-Essa Department of Computer Science, The University of Jordan, Jordan https://orcid.org/0000-0002-0892-975X
Mohammad Alsharo Department of Information Systems, Al al-Bayt University, Jordan https://orcid.org/0000-0003-1978-3220
Yazan Alnsour Department of Management Information Systems, Prince Mohammad Bin Fahd University, Saudi Arabia
Wasim A. Ali Department of Electrical and Information Engineering, Politecnico di Bari, Italy
Omar Almomani Department of Networks and Cybersecurity, Al-Ahliyya Amman University, Jordan https://orcid.org/0000-0003-3160-6542

DOI:

https://doi.org/10.47852/bonviewJCCE52026581

Keywords:

DeepInsight, Deep Learning, malware detection, intrusion detection, XAI

Abstract

Cyber-attacks are increasingly becoming a major concern for individuals and organizations alike. Meanwhile, attackers are employing advanced techniques taking advantage of the growing power of Artificial Intelligence (AI) to develop highly sophisticated attacks at an accelerated pace. Consequently, developing effective tools to detect cyber-attacks and protect digital assets has become of utmost importance to practice and research. Numerous AI-based techniques, mainly Machine Learning (ML) and Deep Learning (DL), have been investigated in the literature to protect digital assets from cyber threats. Particularly, DL has received significant attention in recent cybersecurity research due to its powerful capabilities in managing huge amounts of data and detecting malicious cyber threats. The majority of the proposed threat-detecting techniques in the literature focus on using Deep Neural Networks (DNNs). However, some research in the literature utilized the capabilities of Convolutional Neural Networks (CNNs) in detecting cyber threats by converting the cybersecurity data from tabular data into images. This paper aims to investigate the efficiency of both approaches in detecting cyber threats with the help of eXplainable AI (XAI). Our findings indicate that on NSL-KDD, the DL model trained on tabular features achieved WeightedF1 = 0.74, MacroF1 = 0.56, and Overall Accuracy (OA) = 0.78, compared with 0.73, 0.54, and 0.76 for the image-based model. On CICMaldoid20, the tabular model achieved 0.79 (WeightedF1), 0.80 (MacroF1), and 0.80 (OA), compared with 0.76, 0.75, and 0.77 for the image-based counterpart. These results suggest that while image transformations can be beneficial in specific classes, tabular models consistently deliver stronger overall performance.

Received: 25 June 2025 | Revised: 12 November 2025 | Accepted: 21 November 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data are available from the corresponding author upon reasonable request.

Author Contribution Statement

Malik AL-Essa: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing. Mohammad Alsharo: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing. Yazan Alnsour: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Writing – review & editing. Wasim A. Ali: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data curation, Writing – review & editing. Omar Almomani: Writing – review & editing, Project administration.