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.
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.
A new dataset called the Saudi Privacy Policy Dataset is introduced, which contains Arabic privacy policies from various sectors in Saudi Arabia. The dataset is annotated based on the 10 principles of the Personal Data Protection Law (PDPL) and includes 1,000 websites, 4,638 lines of text, and 775,370 tokens. The dataset aims to facilitate research and development in privacy policy analysis, NLP, and machine learning applications related to data protection.
MBZUAI researchers introduce M4, a multi-generator, multi-domain, and multi-lingual benchmark dataset for detecting machine-generated text. The study reveals challenges in generalizing detection across unseen domains or LLMs, with detectors often misclassifying machine-generated text as human-written. The dataset aims to foster research into more robust detection methods and is available on GitHub.
The paper introduces TimeHUT, a new method for learning time-series representations using hierarchical uniformity-tolerance balancing of contrastive representations. TimeHUT employs a hierarchical setup to learn both instance-wise and temporal information, along with a temperature scheduler to balance uniformity and tolerance. The method was evaluated on UCR, UAE, Yahoo, and KPI datasets, demonstrating superior performance in classification tasks and competitive results in anomaly detection.