Jul 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 69 references
Computer Science
TL;DR
A deep learning based framework that can be effectively integrate code embeddings including Word2Vec, TF-IDF representations, and handcrafted contextual features to detect vulnerabilities over multiple Common Weakness Enumeration (CWE) classes is proposed.
This work presents a deep learning approach for multilingual vulnerability detection that emphasizes semantic transfer rather than architectural complexity and suggests that stabilizing semantic representations during transfer is key to improving generalization while maintaining practical efficiency under moderate comp...
Tuan Nguyen Kim, Nin Ho Le Viet, Chieu Ta Quang· Engineering, Technology &...· 0 citations
The results indicate that the hybrid RNN–BiLSTM architecture can be used to efficiently perform proactive vulnerability detection and is also applicable to enable real-time cybersecurity monitoring and the use of automatically possible counter efforts in contemporary software systems.
Akshay Jain, Atul Agrawal· International journal of com...· 1 citation
The results show that the ensemble techniques are a practical approach to boost the precision of LLMs in the detection of vulnerabilities and suggest that ensemble methods offer great potential in the advancement of software security analysis.
H. Al-Ofeishat, Azhar Hussain, M. Faheem et al.· Engineering, Technology &...· 0 citations
A dual-stream CodeBERT architecture is presented that addresses keyword bias —by combining pre-trained Transformer representations with a 50-dimensional hand-engineered security feature vector, supported by targeted data augmentation and two-stage adversarial fine-tuning.
Arjun Khurana, Talaya Farasat, Joachim Posegga et al.· 0 citations
This thesis rebuilds Real-Vul through a controlled Code Property Graph pipeline, measure and remove a 37% content leak intrinsic to whole-codebase sampling, and train a relational graph neural network with a disciplined class-imbalance recipe: focal loss, class-aware undersampling, and fine-tuning of a GraphCodeBERT no...
A metadata-driven detection framework that fine-tunes DistilBERT on repository metadata text, specifically the description, README, and topics, and formulates detection as a binary classification task between malware and benign repositories, suggesting that the framework is promising for threshold-based screening scena...
Rabeaa Mouty, M. Abdullah-Al-Wadud· IEEE Access· 0 citations
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