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Explainable AI for Cybersecurity

Siva Raja Sindiramutty, Chong Eng Tan, Sei Ping Lau, Rajan Thangaveloo et al. • Advances in computational intelligence and robotics book series (2024)

In recent years, the utilization of AI in the field of cybersecurity has become more widespread. Black-box AI models pose a significant challenge in terms of interpretability and transparency, which is one of the major drawbacks of AI-based...

OPENALEX DOI: https://doi.org/10.4018/978-1-6684-6361-1.ch002

Explainable artificial intelligence for cybersecurity: a literature survey

Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications (2022)

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...

OPENALEX cc-by DOI: https://doi.org/10.1007/s12243-022-00926-7

Empowering text classification with NLP and explainable AI for enhanced interpretability

Sumaya Mustafa, Mariwan Hama Saeed • Journal of Electrical Systems and Information Technology (2025)

Abstract Artificial intelligence (AI) models have demonstrated significant success in classifying various types of text. However, the complex nature of these models often complicates the interpretability of their classifications. To address...

DOAJ Open Access (DOAJ) DOI: 10.1186/s43067-025-00273-2

Multidimensional Correlates of Childhood Stunting in India: A Spatial Machine Learning and Explainable AI Approach

Bhagyajyothi Rao, Md Gulzarull Hasan, Bandhavya Putturaya, Asha Kamath et al. • Stats (2026)

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Agai...

DOAJ Open Access (DOAJ) DOI: 10.3390/stats9020034

Prediction of bank transaction fraud using TabNet—an adaptive deep learning architecture

B.S. Prashanth, Manoj Kumar, Ariful Hoque, Nasser Al Muraqab et al. • International Review of Economics & Finance (2026)

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems b...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.iref.2026.104916

Enhancing Explainable Artificial Intelligence: Using Adaptive Feature Weight Genetic Explanation (AFWGE) with Pearson Correlation to Identify Crucial Feature Groups

Ebtisam AlJalaud, Manar Hosny • Mathematics (2024)

The ‘black box’ nature of machine learning (ML) approaches makes it challenging to understand how most artificial intelligence (AI) models make decisions. Explainable AI (XAI) aims to provide analytical techniques to understand the behavior...

DOAJ Open Access (DOAJ) DOI: 10.3390/math12233727