Open-source ecosystems such as NPM and PyPI are increasingly targeted by supply chain attacks, yet existing detection methods either depend on fragile handcrafted rules or data-driven features that fail to capture evolving attack semantics. We present IntelGuard, a retrieval-augmented generation (RAG) based framework that integrates expert analytical reasoning into automated malicious package detection. IntelGuard constructs a structured knowledge base from over 8,000 threat intelligence reports, linking malicious code snippets with behavioral descriptions and expert reasoning. When analyzing new packages, it retrieves semantically similar malicious examples and applies LLM-guided reasoning to assess whether code behaviors align with intended functionality. Experiments on 4,027 real-world packages show that IntelGuard achieves 99% accuracy and a 0.50% false positive rate, while maintaining 96.5% accuracy on obfuscated code. Deployed on PyPI.org, it discovered 54 previously unreported malicious packages, demonstrating interpretable and robust detection guided by expert knowledge.

Abstract In the contemporary digital landscape, mobile applications have become the predominant conduit for internet connectivity and daily tasks. Simultaneously, the advent of application encryption technology has safeguarded users’ privacy. However, this encryption, while fortifying privacy, introduces challenges to security by hindering the effective management of network applications within encrypted data streams. Conventional detection methods for encrypted application traffic, relying heavily on statistical metrics like payload, packet size, and distribution, are constrained to single traffic flows, often yielding results of limited specificity. To address this limitation, our paper introduces an innovative approach that elucidates the multi-flow nature of application behavior traffic and provides context to encrypted application traffic. This method offers a more nuanced and comprehensive perspective for understanding and representing network traffic, even when encrypted. The efficacy of our approach was evaluated using a substantial volume of real network traffic data. Results indicate that our method achieves an average accuracy of 0.958 in identifying application behavior traffic and 0.955 in classifying application traffic. These outcomes signify a substantial enhancement over single network flow-based detection methods, demonstrating a notable 5.3% improvement.

Current methods for classifying IoT malware predominantly utilize binary and family classifications. However, these outcomes lack the detailed granularity to describe malicious behavior comprehensively. This limitation poses challenges for security analysts, failing to support further analysis and timely preventive actions. To achieve fine-grained malicious behavior identification in the lurking stage of IoT malware, we propose MaGraMal. This approach, leveraging masked graph representation, supplements traditional classification methodology, empowering analysts with critical insights for rapid responses. Through the empirical study, which took three person-months, we identify and summarize four fine-grained malicious behaviors during the lurking stage, constructing an annotated dataset. Our evaluation of 224 algorithm combinations results in an optimized model for IoT malware, achieving an accuracy of 75.83%. The maximum improvement brought by the hybrid features and graph masking achieves 5% and 4.16%, respectively. The runtime overhead analysis showcases MaGraMal’s superiority over the existing dynamic analysis-based detection tool (12x faster). This pioneering work combines machine learning and static features for malicious behavior profiling.

During the last decade, rapid development of mobile devices and applications has produced a large number of mobile data which hide numerous cyber‐attacks. To monitor the mobile data and detect the attacks, NIDS/NIPS plays important role for ISP and enterprise, but now it still faces two challenges, high performance for super large patterns and detection of the latest attacks. High performance is dominated by Deep Packet Inspection (DPI) mechanism, which is the core of security devices. A new TTL attack is just put forward to escape detecting, such that the adversary inserts packet with short TTL to escape from NIDS/NIPS. To address the above‐mentioned problems, in this paper, we design a security system to handle the two aspects. For efficient DPI, a new two‐step partition of pattern set is demonstrated and discussed, which includes first set‐partition and second set‐partition. For resisting TTL attacks, we set reasonable TTL threshold and patch TCP protocol stack to detect the attack. Compared with recent produced algorithm, our experiments show better performance and the throughput increased 27% when the number of patterns is 106. Moreover, the success rate of detection is 100%, and while attack intensity increased, the throughput decreased.