Cloud-assisted Internet of Things (IoT) has become an increasingly popular paradigm to greatly improve the performance of IoT applications by delegating the cloud to manage the massive IoT data. How to achieve secure and real-time traceable data sharing (STDS) is crucial in this paradigm, especially, a large amount of sensitive data produced by IoT devices needs to be stored or accessed to/from the clouds. This article proposes an STDS scheme, which leverages the acrlong DIFC model to allow data owners to not only securely and efficiently share their data produced by IoT devices with data users but also have the capability of tracking the data users’ identity with nonrepudiation based on the hash chain technique. Subsequently, the acrlong HLPN, acrlong SMT-Lib, and Z3 solver are used to formally analyze and verify STDS based on acrlong BMC technique to prove the correctness and security STDS. The formal analysis results show that STDS fulfills its intended security goals. Finally, the performance evaluation results have demonstrated the efficiency of STDS.

Domain Name System is widely abused by various types of malicious campaigns. Recently, many graph learning models have been proposed to detect malicious domains based on the domain name resolution process and related data. These models focus on associations among domain names, which are defined as heuristic relations in this paper, and typically report an F1-score exceeding 0.95, indicating high detection accuracy achieved in real-world DNS applications. In order to explore how far we are from excellent graph-based malicious domain name detection methods, this paper conducts an in-depth analysis of six representative graph-based models on three experimental datasets and one real-world dataset. Our experiments focus on several aspects of model evaluation, including heuristic relation distributions, heuristic relation selection, feature extraction, graph reduction operation, and imbalance distribution of malicious domain names in the real world. The experimental results demonstrate that all these aspects have a significant impact on the detection performance, existing models are still relatively shallow in utilizing heuristic relations, and that all the studied models do not always work well as claimed. We further propose several possible future works that may contribute to achieving excellent performance in malicious domain name detection.

Malicious traffic detection in the Industrial Internet of Things (IIoT) faces significant challenges, primarily due to the scarcity of labeled data, high inference latency on resource-constrained edge devices, and the lack of comprehensibility in deep learning models. To overcome these limitations, this paper proposes MeeDet, a novel detection framework that integrates Mamba-based state-space modeling, a dynamic early-exit mechanism, and Large Language Model (LLM)-driven comprehensibility. The proposed MeeDet operates through a four-stage pipeline. First, raw packet captures are preprocessed into header-only, standardized stride-based sequences. Second, a 12-layer unidirectional Mamba backbone is pretrained on unlabeled data using two complementary tasks: Masked Byte Modeling for byte-level semantics and Next-Flow Prediction for long-range flow-level temporal coherence. Third, the model is fine-tuned by attaching lightweight binary heads to each Mamba layer, allowing for the early termination of high-confidence benign samples and adaptive routing of ambiguous flows to deeper layers. Finally, for detected malicious samples, structured prompts containing key network traffic features are processed by an LLM to generate human-readable diagnostic reports, without affecting real-time detection latency. Extensive experiments on five public IIoT datasets demonstrate the superiority of MeeDet over existing baselines. MeeDet achieves F1-scores exceeding 0.98 on key benchmarks while significantly reducing computational overhead. Specifically, at a 1% malicious traffic ratio, MeeDet requires only 1.7 MFLOPs and 1.58 ms of average inference latency, representing a reduction of over 70% in computational cost compared to strong pretrained baselines.