A study compared the vulnerability of C programs generated by nine state-of-the-art Large Language Models (LLMs) using a zero-shot prompt. The researchers introduced FormAI-v2, a dataset of 331,000 C programs generated by these LLMs, and found that at least 62.07% of the generated programs contained vulnerabilities, detected via formal verification. The research highlights the need for risk assessment and validation when deploying LLM-generated code in production environments.
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.
Researchers introduce TII-SSRC-23, a new network intrusion detection dataset designed to improve the diversity and representation of modern network traffic for machine learning models. The dataset includes a range of traffic types and subtypes to address the limitations of existing datasets. Feature importance analysis and baseline experiments for supervised and unsupervised intrusion detection are also provided.
A research talk was given on privacy and security issues in speech processing, highlighting the unique privacy challenges due to the biometric information embedded in speech. The talk covered the legal landscape, proposed solutions like cryptographic and hashing-based methods, and adversarial processing techniques. Dr. Bhiksha Raj from Carnegie Mellon University, an expert in speech and audio processing, delivered the talk. Why it matters: As speech-based interfaces become more prevalent in the Middle East, understanding and addressing the associated privacy risks is crucial for ethical AI development and deployment.
Zeerak Talat, an independent scholar, gave a talk at MBZUAI on ethical concerns in NLP. The talk covered disparities in research on biases in NLP, performance differences based on socio-economic language variations, and risks of malicious reuse of NLP tools. Talat's research considers how machine learning interacts with and impacts societies through content moderation technologies. Why it matters: As NLP technologies become more integrated into society, understanding and addressing their potential harms and ethical implications is crucial for responsible development and deployment in the region and beyond.
A talk will present two projects related to the use of NLP for estimating a client’s depression severity and well-being. The first project examines emotional coherence between the subjective experience of emotions and emotion expression in therapy using transformer-based emotion recognition models. The second project proposes a semantic pipeline to study depression severity in individuals based on their social media posts by exploring different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. Why it matters: This research explores how NLP techniques can be applied to mental health assessment, potentially offering new tools for diagnosis and treatment monitoring.
This survey paper reviews the landscape of Natural Language Processing (NLP) research and applications in the Arab world. It discusses the unique challenges posed by the Arabic language, such as its morphological complexity and dialectal diversity. The paper also presents a historical overview of Arabic NLP and surveys various research areas, including machine translation, sentiment analysis, and speech recognition. Why it matters: The survey provides a comprehensive resource for researchers and practitioners interested in the current state and future directions of Arabic NLP, a field critical for enabling AI technologies to serve Arabic-speaking communities.