With the rise of generative artificial intelligence, malware creation has become more accessible, leading to a surge in malware and its variants. Traditional detection methods struggle to keep pace with this evolution. Dynamic analysis, though detailed, is resource intensive and susceptible to variations in computer hardware and simulation environments. Static analysis, on the other hand, faces the challenge of discerning valuable features from an extensive pool, especially for software across diverse architectures. To tackle these issues, we propose a binary sample classification approach based on raw bytes, named CAG-Malconv, which incorporates Convolutional Block Attention Module (CBAM) and Bidirectional Gated Recurrent Unit (BiGRU) to extract byte-level features. We evaluated it on two datasets with 48,000 samples of different file types and families. It outperforms state-of-the-art methods based on advanced features and raw bytes in terms of accuracy (ACC), Area Under the Curve (AUC), F1 score, and recall. Furthermore, it allows for the visualization of raw samples, facilitating the precise identification of malicious components like C&C URLs and encryption loops by analyzing activation patterns in hidden layers, thus streamlining malware investigative procedures.

As the Internet continues to evolve, modern information technology infrastructures are constantly under attack and need to be continuously monitored for timely responses. Different devices and detection platforms generate heterogeneous security events that are sent to security operations centers, where security operators investigate those events and identify potential threats. Unfortunately, it is impossible to manually analyze such a huge number of events, leading to “alert fatigue.” Despite a substantial amount of effort having been made to aggregate redundant related alerts, the effectiveness of previous works was essentially restrained by their limited relation learning and explaining abilities. In this work, we propose the bidirectional contextualized attentive model (BiCAM), a novel contextual analysis model that uses a self-supervised deep learning approach to automatically correlate security events in relation to their bidirectional context. It is developed by designing an encoder–decoder architecture that consists of bidirectional gated recurrent units and an attention mechanism to capture both sequential and nonsequential relations of previous and subsequent alerts and provide explainability information for the security operators. In addition, we introduce a bidirectional encoder representations from transformers (BERT)-based embedding method to deal with the heterogeneity of security events, enhancing our model's accommodation to the changes of detectors. We comprehensively evaluate our model on real-world datasets containing over 11M events generated by detectors from 8 different vendors. We found that our model enables accurate, unsupervised correlation extraction; and outperforms the state-of-the-art (SOTA) work when applying event relevance to semiautomatically classify security events (e.g., the $F1$ -score of classification is improved by 4.3% and the false positive rate dropped to 1.39%).

In this article, barycentric interpolation collocation method (BICM) is presented to solve the fractional linear Fredholm-Volterra integro-differential equation (FVIDE). Firstly, the fractional order term of equation is transformed into the Riemann integral with Caputo definition, and this integral term is approximated by the Gauss quadrature formula. Secondly, the barycentric interpolation basis function is used to approximate the unknown function, and the matrix equation of BICM is obtained. Finally, several numerical examples are given to solve one-dimensional differential equation.

With the rapid advancement of deep learning, differential privacy has become a key technique for protecting sensitive data with a formal guarantee of privacy. By injecting noise and enforcing privacy budgets, differentially private deep learning (DP-DL) systems are able to protect individual data points yet still maintain a model’s utility. However, recent studies reveal that DP-DL systems can be vulnerable to different types of attacks throughout their lifecycle. Naturally, this has attracted the attention of both academia and industry. Critically, these risks are not the same as those associated with traditional deep learning. This is because the differential privacy mechanism itself introduces new attack surfaces that adversaries can exploit. Our work focuses on the distinct vulnerabilities that can arise at the data, algorithm, and architecture levels. By analyzing representative attacks and corresponding defenses, this survey highlights emerging challenges and outlines promising research directions. Overall, our aim is to make differential privacy more robust and deployable in real-world deep learning systems.