The SPECTRE family of speculative execution attacks has required a rethinking of formal methods for security. Approaches based on operational speculative semantics have made initial inroads towards finding vulnerable code and validating defenses. However, with each new attack grows the amount of microarchitectural detail that has to be integrated into the underlying semantics. We propose an alternative, lightweight and axiomatic approach to specifying speculative semantics that relies on insights from memory models for concurrency. We use the CAT modeling language for memory consistency to specify execution models that capture speculative control flow, store-to-load forwarding, predictive store forwarding, and memory ordering machine clears. We present a bounded model checking framework parameterized by our speculative CAT models and evaluate its implementation against the state of the art. Due to the axiomatic approach, our models can be rapidly extended to allow our framework to detect new types of attacks and validate defenses against them.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

Video summarization aims to improve the efficiency of large-scale video browsing through producting concise summaries. It has been popular among many scenarios such as video surveillance, video review and data annotation. Traditional video summarization techniques focus on filtration in image features dimension or image semantics dimension. However, such techniques can make a large amount of possible useful information lost, especially for many videos with rich text semantics like interviews, teaching videos, in that only the information relevant to the image dimension will be retained. In order to solve the above problem, this paper considers video summarization as a continuous multi-dimensional decision-making process. Specifically, the summarization model predicts a probability for each frame and its corresponding text, and then we designs reward methods for each of them. Finally, comprehensive summaries in two dimensions, i.e. images and semantics, is generated. This approach is not only unsupervised and does not rely on labels and user interaction, but also decouples the semantic and image summarization models to provide more usable interfaces for subsequent engineering use.