Lua is a lightweight scripting language widely adopted across diverse application domains. In practice, Lua applications are often distributed as compiled bytecode to protect intellectual property and improve loading efficiency. Existing Lua decompilers rely heavily on debug symbols embedded in bytecode to generate human-readable code. When debug symbols are stripped, these tools utilize heuristic-based methods to infer variable liveness ranges. However, existing heuristic methods often produce inaccurate predictions, reducing the readability of the decompilation results.

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.

Encrypted malicious traffic detection aims at iden-tifying malicious activities without decrypting network traffic, which is essential for cybersecurity. Existing methods have shown effective performance in encrypted malicious traffic detection, but their heavy reliance on labeled datasets presents a chal-lenge. This paper presents CoMDet, a contrastive multimodal pre-training approach, to detect encrypted malicious traffic. CoMDet leverages three independent Transformer encoders to learn multimodal feature representations from encrypted traffic based on unlabeled data in the pre-training phase. Meanwhile, we introduce a novel inter-modal contrastive learning method to enhance feature representation by maximizing the mutual information among the modalities. Subsequently, we fine-tune the pre-trained model using limited labeled data. Experimental results demonstrate that CoMDet outperforms the existing semi- supervised learning methods and achieves comparable performance with existing supervised learning methods. It obtains an average ACC of 92% and an average macro-Fl of 86% with only 80 labeled samples in each malicious category. We conduct an investigation on the fine-tuning dataset size and discover that as the dataset size increases, the performance of CoMDet is increasingly comparable to existing supervised learning methods.

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.