Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.

Automatic malware detection was aimed at determining whether the application is malicious or not with automated systems. Android malware attacks have gained tremendous pace owing to the widespread use of mobile devices. Although significant progress has been made in antimalware techniques, these methods mainly rely on the program features, ignoring the importance of source code analysis. Furthermore, the dynamic analysis is low code coverage and poor efficiency. Hence, we propose an automatic Android malware detection approach, named HyGNN‐Mal. It analyzes the Android applications at source code level by exploiting the sequence and structure information. Meanwhile, we combine the typical static features, permissions, and APIs. In HyGNN‐Mal, we utilize a deep traversal tree neural network (Deep‐TNN) to process the code structure information. Particularly, we add position information to code sequence information before putting in self‐attention mechanism. The evaluations conducted on multiple public datasets indicate that our method can accurately identify and classify the malicious software, and their best accuracy is 99.62% and 99.2%, respectively.

While Tor provides strong anonymity, it also facilitates the concealment of malicious activities, which poses a significant challenge to cybersecurity surveillance. As an effective anti-anonymity technique, Website Fingerprinting(WF) enables the inference of which websites a user is visiting, thereby uncovering potential attacker activities. State-of-the-art(SOTA) methods have demonstrated remarkable effectiveness. However, a large number of labeled traffic is required to ensure effectiveness, and without timely updates, these models will encounter serious challenges of concept drift due to the dynamic nature of website content and network conditions. The core reasons lie in the independently and identically distributed assumption, while in challenging open-world scenarios, the long-term spatial and temporal dynamics complicates data consistency and effective knowledge transfer. To address these issues, this paper presents WF-TFC, an open-world few-shot anonymous WF model via self-supervised contrastive learning and time-frequency consistency. It aligns time- and frequency-based representations in the latent time-frequency space, enhancing the sustained effectiveness of inherent patterns across various websites. Consequently, it accommodates diverse few-shot target domains with varying dynamics, facilitating data consistency and knowledge transfer in unobserved long-term temporal and spatial environments. For instance, with only 5 traces per website, WF-TFC achieves 92.62% accuracy on traces collected six weeks after pre-training, exceeding the SOTA(i.e., NetCLR) by 2.12%. On similar but mutually exclusive traces, it attains an F1 score of 87.20%, surpassing the SOTA by 6.12%.

With the rapid development of information technology, the scale of software has increased exponentially. Binary code similarity detection technology plays an important role in many fields, such as detecting software plagiarism, vulnerabilities discovery, and copyright solution issues. Nevertheless, what cannot be ignored is that a variety of approaches to binary code semantic representation have been introduced recently, but few can catch up with existing code obfuscation techniques due to their maturing and extensive development. In order to solve this problem, we propose a new neural network model, named SROBR, which is a deep integration of natural language processing model and graph neural network. In SROBR, BERT is applied to capture sequence information of the binary code at the first place, and then GAT is utilized to capture the structural information. It combines natural language processing and graph neural network, which can capture the semantic information of binary programs while resisting obfuscation options in a more efficient way. Through binary code similarity detection task and obfuscated option classification task, the experimental results demonstrate that SROBR outperforms existing binary similarity detection methods in resisting obfuscation techniques.