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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

A. Albarrak

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Review Open access 2026

Culturally Aware Malay–English Code-Mixed Hate Speech Detection: A Systematic Review and Research Taxonomy

Social media platforms such as X, formerly Twitter, have become major channels for communication, information sharing and public discussion. However, the rapid growth of user-generated content has also increased the spread of hate speech and offensive language. Automated hate speech detection remains challenging in multilingual and code-mixed environments, where users frequently combine languages, informal spelling, slang, abbreviations and culturally specific expressions. In Malaysia, online discourse often involves Malay-English code-mixing, commonly referred to as Manglish, which creates additional challenges for natural language processing systems. This study presents an evidence-informed systematic review and research-readiness taxonomy for culturally aware Malay-English hate speech detection. Unlike conventional reviews that mainly summarize model architecture and performance scores, this review critically evaluates existing studies based on dataset availability, code-mix authenticity, annotation practice, cultural sensitivity, model architecture, evaluation strategy, explainability, robustness and deployment readiness. To strengthen this review, this article incorporates a completed empirical case study on Manglish hate-speech detection, using posts collected from X (formerly Twitter). The case study used keyword-based data collection, Malaya NLP-based language filtering, bilingual manual annotation, manual class balancing and transformer-based evaluation using mBERT, XLNet and XLM-RoBERTa. The case evidence is used only as an empirical lens to illustrate practical challenges in dataset curation, class imbalance, lexical overlap and model robustness. It is not positioned as a new benchmark dataset or an independent experimental contribution. The review finds that existing Malay and Malay-English resources remain fragmented. Some datasets are monolingual Malay hate speech datasets, some are bilingual but language-separated, while others are code-mixed but developed for sentiment analysis rather than hate speech detection. Transformer-based models such as BERT, mBERT and XLM-RoBERTa show strong potential, but their results are difficult to compare due to inconsistent datasets, label definitions, class distributions, evaluation metrics and limited robustness testing. The main novelty of this review is the proposed taxon- omy that evaluates the field through six readiness dimensions, which are data, linguistic, cultural, modelling, evaluation and deployment readiness. The findings from this study provide a structured foundation for developing culturally aware, robust, explainable and deployable hate speech detection systems for Malay-English code-mixed social media.

F. Azmi, Normaisharah Mamat, Rawad Abdulghafor et al. · 0 citations
Open access 2026

Hybrid Feature Image Mapping (HFIM): A Multi-Channel Image Approach for Malware Detection Using Static and Dynamic Features

Image-based malware detection has emerged as an alternative to high-dimensional handcrafted feature representations; however, most existing approaches rely primarily on static features or encode multiple features from a single analysis domain, which may limit robustness under code obfuscation and incomplete feature extraction. This paper proposes Hybrid Feature Image Mapping (HFIM), a multi-channel malware visualization technique that integrates structural (binary content), semantic (opcode transitions), and behavioral (API activity) information into a unified RGB representation. HFIM is designed as a representation-level approach that encodes complementary static and dynamic characteristics within a single image, allowing convolutional neural networks (CNNs) to exploit complementary cross-domain patterns while maintaining classification performance when certain feature streams are partially unavailable. HFIM is evaluated against three representative malware imaging methods, HIT4Mal, MC-ISA, and MTV, using seven CNN architectures under identical training conditions to ensure fair comparison and isolate the impact of feature representation. Experimental results based on 10-fold cross-validation demonstrate consistent improvements across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additional analyses, including ablation and family-aware evaluation, further indicate that multi-domain integration provides a more discriminative and stable representation than single-domain approaches, particularly under variations in feature availability. These results indicate that integrating static and dynamic features at the representation level can improve robustness and classification performance in image-based malware detection.

Anis Elgarduh, A. Zainal, Fuad A. Ghaleb et al. · 0 citations
Open access Aug 2026

A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks

A hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier that addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability.

A. Albarrak, Fuad A. Ghaleb, Sultan Noman Qasem et al. · 0 citations