MA-IDS: Multi-Agent RAG Framework for IoT Network Intrusion Detection with an Experience Library

Network Intrusion Detection Systems (NIDS) face important limitations. Signature-based methods are effective for known attack patterns, but they struggle to detect zero-day attacks and often miss modified variants of previously known attacks, while many machine learning approaches offer limited interpretability. These challenges become even more severe in IoT environments because of resource constraints and heterogeneous protocols. To address these issues, we propose MA-IDS, a Multi-Agent Intrusion Detection System that combines Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for reasoning-driven intrusion detection. The proposed framework grounds LLM reasoning through a persistent, self-building Experience Library. Two specialized agents collaborate through a FAISS-based vector database: a Traffic Classification Agent that retrieves past error rules before each inference, and an Error Analysis Agent that converts misclassifications into human-readable detection rules stored for future retrieval, enabling continual learning through external knowledge accumulation, without modifying the underlying language model. Evaluated on NF-BoT-IoT and NF-ToN-IoT benchmark datasets, MA-IDS achieves Macro F1-Scores of 89.75% and 85.22%, improving over zero-shot baselines of 17% and 4.96% by more than 72 and 80 percentage points. These results are competitive with SVM while providing rule-level explanations for every classification decision, demonstrating that retrieval-augmented reasoning offers a principled path toward explainable, self-improving intrusion detection for IoT networks.

Multi-View Bangla Sign Language Dataset and Continuous BSL Recognition

Being able to express our thoughts, feelings, and ideas to one another is essential for human survival and development. A considerable portion of the population encounters communication obstacles in environments where hearing is the primary means of communication, leading to unfavorable effects on daily activities. An autonomous sign language recognition system that works effectively can significantly reduce this barrier. To address the issue, we proposed a large scale dataset namely Multi-View Bangla Sign Language dataset (MV- BSL) which consist of 115 glosses and 350 isolated words in 15 different categories. Furthermore, We have built a recurrent neural network (RNN) with attention based bidirectional gated recurrent units (Bi-GRU) architecture that models the temporal dynamics of the pose information of an individual communicating through sign language. Human pose information, which has proven effective in analyzing sign pattern as it ignores people's body appearance and environmental information while capturing the true movement information makes the proposed model simpler and faster with state-of-the-art accuracy.