The rapid spread of multimodal fake news (FN) on Online Social Networks (OSNs) threatens digital information ecosystems, particularly in low-resource languages. Existing multimodal fake news detection (FND) methods are largely limited to high-resource settings, restricting their global applicability. We propose an M&M-RAG, a Multilingual & Multimodal Retrieval-Augmented Generation framework, that leverages Large Vision-Language Models (LVLMs) and Large Language Models (LLMs) to verify news claims across English, Chinese and Urdu. M&M-RAG integrates real-time multilingual evidence retrieval, language-aware prompting, and cross-modal reasoning for fact verification. We further propose Multi-Ax-to-Grind Urdu, the first large-scale, multi-domain multimodal benchmark for FND in Urdu. Experiments on typologically diverse monolingual multimodal datasets demonstrate that M&M-RAG achieves state-of-the-art (SOTA) performance, with 94.6% accuracy and 94.2% F1 score, surpassing models such as SpotFake, MPFN, MMCFND, and Semi-FND. The proposed framework remains robust in zero-shot and cross-lingual scenarios under frozen-model inference without task-specific fine-tuning. The results underscore the scalability and interpretability of LVLM-based approaches for combating multimodal misinformation, particularly in under-represented and typologically diverse languages.

Domain Generation Algorithms (DGAs) allow malware to evade detection by generating millions of random domains daily for Command-and-Control (C&C) communication, challenging traditional detection methods. This work presents NIOM-DGA, a novel machine learning model that applies nature-inspired algorithms (NIAs) to select an optimal subset of 78 features from a dataset of over 16 million domain names, including several features not traditionally used in DGA detection. This approach enhances accuracy, robustness, and generalisability, achieving up to 98.3% accuracy—outperforming most existing approaches. Further testing on 10 external datasets with over 37 million domains confirms an average classification accuracy of 95.7%. Designed for seamless integration into SIEM, EDR, XDR, and cloud security platforms, NIOM-DGA significantly improves DGA detection compared to existing methods, advancing practical threat detection capabilities.

Abstract Privacy and data protection by design are relevant parts of the General Data Protection Regulation (GDPR), in which businesses and organisations are encouraged to implement measures at an early stage of the system design phase to fulfil data protection requirements. This paper addresses the policy and system architecture design and propose two variants of privacy policy language and architecture description language, respectively, for specifying and verifying data protection and privacy requirements. In addition, we develop a fully automated algorithm based on logic, for verifying three types of conformance relations (privacy, data protection, and functional conformance) between a policy and an architecture specified in our languages’ variants. Compared to related works, this approach supports a more systematic and fine-grained analysis of the privacy, data protection, and functional properties of a system. Our theoretical methods are then implemented as a software tool called DataProVe and its feasibility is demonstrated based on the centralised and decentralised approaches of COVID-19 contact tracing applications.

In this paper, we address the security properties of automated road congestion detection systems. SCATS, SCOOT and InSync are three examples of Adaptive Traffic Control Systems (ATCSs) widely deployed today. ATCSs minimize the unused green time and reduce traffic congestion in urban areas using different methods such as induction loops and camcorders installed at intersections. The main drawback of these system is that they cannot capture incidents outside the range of these camcorders or induction loops. To overcome this hurdle, theoretical concepts for automated road congestion alarm systems including the system architecture, communication protocol, and algorithms are proposed. These concepts incorporate secure wireless vehicle-to-infrastructure (V2I) communications. The security properties of this new system are presented and then analyzed using the ProVerif protocol verification tool.

Transport protocols for Wireless Sensor Networks (WSNs) are designed to fulfill both reliability and energy efficiency requirements. Distributed Transport for Sensor Networks (DTSN) [1] is one of the most promising transport protocols designed for WSNs because of its effectiveness; however, it does not address any security issues, hence it is vulnerable to many attacks. The first secure transport protocol for WSN was the secure distributed transport protocol (SDTP) [2], which is a security extension of DTSN. Unfortunately, it turns out that the security methods provided by SDTP are not sufficient; some tricky attacks get around the protection mechanism. In this paper, we describe the security gaps in the SDTP protocol, and we introduce SDTP+for patching the weaknesses. We show that SDTP+resists attacks on reliability and energy efficiency of the protocol, and also present an overhead analysis for showing its effectiveness.