IoT devices have become deeply integrated into our daily lives, making comprehensive security research on critical infrastructure devices increasingly important. Static analysis techniques, particularly those leveraging taint propagation, have demonstrated promise in identifying security vulnerabilities within these devices, effectively detecting critical vulnerabilities. However, current solutions often struggle with limitations in both detection efficiency and accuracy. To address these challenges, this paper introduces Liva, a novel static taint analysis tool designed for detecting web vulnerabilities in IoT devices. Liva employs a large language model (LLM) multi-agent approach for static binary taint analysis, primarily leveraging fine-tuned open-source models and commercial LLMs to improve source/sink identification and taint data analysis—areas where traditional methods often fall short—thereby enhancing overall analysis efficiency. LIVA's core analysis engine leverages a Qwen3-32B open-source model that has been fine-tuned using a dataset of 3,000 real-world device samples. This fine-tuned model achieves a 3 percentage point improvement in accuracy for identifying taint data propagation relationships compared to commercial LLMs, while also increasing average analysis efficiency by 5.5%. A comprehensive evaluation of Liva, conducted on a dataset of 64 devices from 11 vendors, revealed that it detected 309 and 349 more known vulnerabilities than the state-of-the-art solutions SaTC and Karonte, respectively, while simultaneously reducing false positive rates by 59.4% and 67.6%. Liva achieves a recall of 98.1% and a precision of 74.6%, with a 6.7× reduction in analysis time compared to the best-performing baseline. Furthermore, in the realm of zero-day vulnerability detection, Liva discovered 64 previously unknown vulnerabilities, 39 of which have since been assigned official CVE/CNVD identifiers.

In recent years, with the rapid development of tourism, the abnormal events in the traffic fortress of tourist attractions not only endanger personal safety, but also cause a lot of negative effects on the society. In the face of the low accuracy of abnormal event detection in complex scenes and the low efficiency of manual observation, combining the advantages and characteristics of YOLOv5 and 3D convolutional neural network(C3D), an automatic abnormal event detection algorithm based on YOLOv5+C3D was proposed. Experimental data show that compared with other methods, the abnormal event detection method based on YOLOv5+C3D has a higher accuracy for various abnormal events detection, indicating that the trained model has a strong generalization ability for abnormal event detection.

Recently, side-channel attacks based on deep learning (DLSCAs) have attracted much attention. Many works have improved the performance of DLSCAs by designing advanced neural network architectures and training strategies. There are few studies on leakage models for DLSCAs. Existing researches usually utilize the intermediate value Hamming weight (HW) and the intermediate value itself (ID) as leakage models. Training a classifier with good performance is challenging due to the many label classes in the ID leakage model. The HW leakage model can significantly reduce the number of labels, but it will cause samples imbalance. In this paper, we propose a new DLSCA leakage model, named Balanced Labels Compression (BLC). We consider dividing sensitive intermediate values with same lowest bits into same class to obtain balanced labels. Then, we train a classifier using the compressed BLC labels and profiling energy traces. At the attack phase, the probability distribution of BLC labels is extended to the probability distribution of sensitive intermediate values. We conduct extensive comparison experiments with HW, ID, and BLC leakage models under the two scenarios of sufficient and insufficient profiling energy traces. Further, we exploit VAE to improve attack performance when energy traces are insufficient. Experimental results show that VAE-based data augmentation can significantly reduce required energy traces.

Intrusion detection system (IDS) is used to find malicious connections and protect networks from external or internal attacks. Various fuzzy or fuzzy intelligence approaches have been proposed in the development of IDS. In particular, the fuzzy interpolation technique guarantees the performance of IDS where only a sparse rule base is available. Furthermore, backward fuzzy interpolation allows interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. In this paper, a novel fuzzy association rules based classification intrusion detection system framework enhanced by a hierarchical bidirectional fuzzy rule interpolation technique is proposed for designing an IDS. Hierarchical bidirectional fuzzy rule interpolation is also employed to refine fuzzy rule base while which exists some consistency. This framework uses fuzzy association rules for building classifiers, and allows the generation of security alerts from situations which are not directly covered, missing values, or existing inconsistency by knowledge base. The proposed method is herein applied through integration with the Snort software to demonstrate the efficacy of this proposed approach.