Smart and mobile devices are essential components of the Internet of Things (IoT) ecosystems, facilitating connectivity and automation across various domains. Due to its flexibility, the Android operating system is widely adopted in these devices. However, their increasing integration into IoT networks has introduced significant security risks, particularly from Android malware. To address these challenges, effective detection methods are needed to enhance IoT security. Given the success of contrastive learning in computer vision, researchers have increasingly explored its potential for Android malware detection. This article presents a static Android malware detection method that integrates deep learning with supervised contrastive learning. Based on the characteristic that contrastive learning enhances the model’s ability to effectively represent input samples, we design a novel contrastive loss based on structural similarity metrics and integrate it with contractive loss and binary cross-entropy loss to construct a hierarchical loss function for guiding model optimization. Furthermore, the method directly analyzes the classes.dex file from Android application package, eliminating the need for feature engineering or domain expertise, thus enhancing its applicability. Experimental results demonstrate that the proposed method achieves an 87.13% F1-score on the AndroZoo dataset, outperforming baseline models while maintaining computational efficiency and practical usability. Ablation studies validate the effectiveness of the hierarchical loss function in improving model performance and ensuring consistent malware representation within the same family.

Internet of Things (IoT) devices have been widely used in many fields, bringing many conveniences to people’s life. With the massive deployment and application of IoT devices, how to maintain the IoT from cyber-attacks has become one of the major concerns of researchers. Due to IoT devices’ limited computational capabilities and storage resources, IoT usually does not have sufficient security defense mechanisms, making it vulnerable to malware or device attacks. However, existing IoT-oriented intrusion detection systems usually only support the detection of specific malicious attacks or require complex models and massive computational resources to obtain high detection accuracy. We propose a lightweight and efficient intrusion detection method based on feature grouping to address the above challenges. We first design a fast protocol parsing method on the raw packet capture files to generate semantic-level parsing features. Then, we propose session merging and feature grouping methods. Finally, we verify the proposed features’ effectiveness and analyze the malicious attacks’ working process. The proposed method achieves more than 99.5% classification accuracy on three public IoT data sets. The proposed method requires significantly fewer computational resources than baseline methods in the protocol parsing and model training process. Experimental results show that the proposed method is lightweight, efficient, and extensible. Therefore, the proposed method is suitable for IoT intrusion detection.

Vulnerability detection in source code has been a focal point of research in recent years. Traditional rule-based methods fail to identify complex and unknown vulnerabilities, leading to poor performance. While deep learning (DL)-based methods have improved these shortcomings, there is still room for enhancement. For C/C++ source code, effective vulnerability detection requires considering both the information in code statements and the structural information of the code. Graph-based code representation methods can address this need, but existing approaches often use homogeneous graphs that do not differentiate between various types of code statements or dependencies. Few methods use heterogeneous graphs for C/C++ code representation. This study explores this potential and proposes a new C/C++ vulnerability detection method named HeVulD. HeVulD introduces two node definition approaches and a key-node-based program slicing method, generating heterogeneous graph representations for source code. These representations consist of both heterogeneous nodes and edges, providing a more precise representation of source code. HeVulD achieves an F1-score of 96.4% on the SARD dataset, outperforming nine baseline C/C++ vulnerability detection methods. HeVulD has been tested under adversarial attack scenarios to assess its robustness. Additionally, HeVulD has been tested on ten open-source software projects and the latest CVEs, demonstrating its detection and generalization capabilities in real-world scenarios and its ability to identify unknown vulnerabilities.

An accurate inertia identification is particularly important for the controller self-tuning to achieve high-performance permanent magnet synchronous motor (PMSM) drives. In this paper, a variable-period inertia identification strategy based on Landau adaptive method is proposed to improve the identification accuracy under low-acceleration conditions. According to the speed measurement rule of low-resolution encoder, the speed updating period of the Landau adaptive inertia identification method is redesigned and auto-adjusted under low-acceleration conditions. Finally, the proposed method is verified on a 2.2-kW PMSM drive platform.