Author

Souham Meshoul

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

AraCTI-NER: A Dataset and Benchmark for Arabic Cyber Threat Intelligence Named Entity Recognition

Automated extraction of structured threat information from unstructured cyber threat intelligence (CTI) underpins modern security operations, yet the supporting machine learning resources are almost exclusively English: no annotated Arabic CTI named entity recognition (NER) corpus has been published. We introduce AraCTI-NER, a dataset of 10,312 token-level annotated samples (275,530 tokens; 42,360 entity spans) over eight STIX-inspired entity types, built by an LLM-assisted pipeline seeded with authentic Arabic cybersecurity articles, structurally validated and rebalanced through targeted generation. We benchmark seven encoders from three families (Arabic-specialized, English cybersecurity-adapted, and multilingual) over three seeds under strict entity-level metrics, and release a 408-sentence expert-audited test subset (ATS-gold) whose reliability is quantified by a second independent expert validation (inter-annotator agreement 0.878 entity-level F1). XLM-RoBERTa Large attains the best mean F1 (0.7603; 0.7674 on ATS-gold), with AraBERTv2 close behind (0.7491), while both English-only cybersecurity encoders fall to ≈0.63, a separation that holds across every seed and survives expert correction, with the ≈3-point F1 decrease from ATS-silver to ATS-gold concentrated in Vulnerability and TTP. On 350 doubly annotated sentences from authentic Arabic cyber-incident news, a shift in both provenance and register, the strongest model reaches F1 = 0.5429 against an inter-annotator F1 of 0.616. AraCTI-NER establishes the first reproducible baseline for Arabic CTI NER and identifies domain-adaptive Arabic cybersecurity pre-training as the highest-value next step.

Joud Alghamdi, Souham Meshoul · Electronics · 0 citations

Open access Jul 2026

Leveraging Code Language Models for Code Smells Detection

Improving software quality remains a major challenge in modern software engineering. Among the different techniques used to address this issue, code smell detection plays an important role, as code smells may negatively affect software maintainability, readability, and evolution. Recent advances in machine learning, particularly pre-trained code representation models, have opened new perspectives for automating this task. In this study, code smell detection is formulated as a multi-label classification problem in order to reflect realistic scenarios where multiple smells may co-occur within the same code fragment. To enable the evaluation of pre-trained code representation models in a multi-label setting, six multi-label datasets containing source code were derived from two existing datasets, namely MLCQ and Fontana, by merging instances sharing common code smells. The study investigates multiple experimental configurations based on CodeBERT, GraphCodeBERT, and CodeT5, including direct transformer-based classification, sliding window processing for long code sequences, and hybrid approaches combining transformer embeddings with classical machine learning classifiers. Among the evaluated configurations, the hybrid approach based on CodeBERT embeddings combined with a Random Forest classifier achieved the best overall performance, reaching an accuracy of 0.91 and a micro-F1 score of 0.90 on the Fontana datasets.

Soumia Zellagui, Abderrezak Kenniche, Samiha Fadloun et al. · Applied Sciences · 0 citations