Global Navigation Satellite System (GNSS) interference poses a serious threat to Unmanned Aerial Vehicles (UAVs), potentially leading to navigation failures, airspace violations, or even loss of flight control. Although deep learning methods have demonstrated strong performance in interference detection and characterization, most models remain too computationally expensive for onboard deployment due to high computational cost. To address this challenge, we propose LiteJam, a lightweight architecture that utilizes pre-correlation in-phase and quadrature (I/Q) data to construct pseudo-image representations without requiring additional hardware. Specifically, LiteJam adopts a multi-scale convolutional architecture to capture interference patterns, employs a dynamic sparse attention mechanism to adaptively emphasize spatio-spectral cues, and leverages a hierarchical multi-head module for interference detection and characterization. Experimental results show that LiteJam outperforms all baselines. The F1-score of interference classification is 95.74%, outperforming lightweight baselines by 4.37%–24.51%, and generalizes well across diverse scenarios, while maintaining high computational efficiency for real-time UAV applications. Our codes are available at https://github.com/CynthiaCYX/LiteJam.

Source code vulnerabilities are one of the significant threats to software security. Existing deep learning‐based detection methods have proven their effectiveness. However, most of them extract code information on a single intermediate representation of code (IRC), which often fails to extract multiple information hidden in the code fully, significantly limiting their performance. To address this problem, we propose VulMPFF, a vulnerability detection method that fuses code features under multiple perspectives. It extracts IRC from three perspectives: code sequence, lexical and syntactic relations, and graph structure to capture the vulnerability information in the code, which effectively realizes the complementary information of multiple IRCs and improves vulnerability detection performance. Specifically, VulMPFF extracts serialized abstract syntax tree as IRC from code sequence, lexical and syntactic relation perspective, and code property graph as IRC from graph structure perspective, and uses Bi‐LSTM model with attention mechanism and graph neural network with attention mechanism to learn the code features from multiple perspectives and fuse them to detect the vulnerabilities in the code, respectively. We design a dual‐attention mechanism to highlight critical code information for vulnerability triggering and better accomplish the vulnerability detection task. We evaluate our approach on three datasets. Experiments show that VulMPFF outperforms existing state‐of‐the‐art vulnerability detection methods (i.e., Rats, FlawFinder, VulDeePecker, SySeVR, Devign, and Reveal) in Acc and F1 score, with improvements ranging from 14.71% to 145.78% and 152.08% to 344.77%, respectively. Meanwhile, experiments in the open‐source project demonstrate that VulMPFF has the potential to detect vulnerabilities in real‐world environments.

Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRCLSTM in short-term forecasting for multivariate time series of cryptocurrencies.

With the large-scale development of open source software, software plagiarism has become a serious threat to software industry and intellectual property. As the latest technique of plagiarism detection, dynamic software birthmark has attracted much attention in recent years. Most of the existing dynamic birthmarks focus on how to resist obfuscation techniques such as compiler optimizations and strong obfuscations implemented in tools. However, they pay little attention to packers, especially encryption packer which is commonly used in software protection as well as plagiarism. When used to encrypt software, the decryption code is added to the binary. It is hard to distinguish the original parts of software from the decryption parts using traditional dynamic birthmarks. In this paper, we propose a new dynamic software birthmark called weight sequences birthmark (WSB) which is based on weight sequences of dynamic control flow graph (DCFG). The weight sequences are used as characteristics, which make full use of the different patterns of dynamic basic block replications between the original code and the decryption code. Compared with the-state-of-art dynamic key instruction sequence birthmark (DKISB), the new birthmark can resist encryption packer effectively. Furthermore, WSB shows better credibility than DKISB when distinguishing independent programs. The comprehensive experiments illustrate that the value of extended F-measure can reach 96.8%, indicating that it is a high-quality birthmark which satisfies both the credibility and the resiliency.