Recovering function signatures is a cornerstone of binary program analysis, yet it remains a challenging task. Existing methods either rely on disassembly-based constraints, which struggle with cross-architecture compatibility and scalability, or adopt learning-based approaches that are resource-intensive and often inaccurate.

State machines are essential for enhancing protocol analysis to identify vulnerabilities. However, inferring state machines from network protocol implementations is challenging due to complex code syntax and semantics. Traditional dynamic analysis methods often miss critical state transitions due to limited coverage, while static analysis faces path explosion issues. To overcome these challenges, we introduce a novel state machine inference approach utilizing Large Language Models (LLMs), named ProtocolGPT. This method employs retrieval augmented generation technology to enhance a pre-trained model with specific knowledge from protocol implementations. Through effective prompt engineering, we accurately identify and infer state machines. To the best of our knowledge, our approach represents the first state machine inference that leverages the source code of protocol implementations. Our evaluation of six protocol implementations shows that our method achieves a precision of over 90 %, outperforming the baselines by more than 30 %. Furthermore, integrating our approach with protocol fuzzing improves coverage by more than 20 % and uncovers two 0-day vulnerabilities compared to baseline methods.

With more attention paid to communication security and user privacy, there has been a dramatic rise in private protocols, which brings great challenges to traditional protocol identification. The increasingly complex design of private pro-tocols has led to the loss of effectiveness of features used for traditional identification. Besides, some features are redundant and also lose sight of the particularity of private protocols, which could not be guaranteed to be the optimal identification solution. In this paper, we propose a method to obtain the Sequence Sta-tistical Fingerprint(Seq-SF) which is proprietary for each private protocol and can be used for identifying private protocols from promiscuous traffic. We extend statistical features associated with protocol transmission to make the sequence feature be integrated into it. Then, we adopt a feature selection algorithm based on mutual information ranking, which can evaluate the pertinence between features and protocols to guarantee the effectiveness and optimality of Seq-SF. The results of experiments on the real-world dataset covering 13 private protocols indicate that Seq-SF achieves an ideal performance(99.41% accuracy, 97.56% precision, 95.18% recall, and 0.20% FPR on average) and has good robustness.

Most existing image captioning methods use only the visual information of the image to guide the generation of captions, lack the guidance of effective scene semantic information, and the current visual attention mechanism cannot adjust the focus intensity on the image. In this article, we first propose an improved visual attention model. At each timestep, we calculated the focus intensity coefficient of the attention mechanism through the context information of the model, then automatically adjusted the focus intensity of the attention mechanism through the coefficient to extract more accurate visual information. In addition, we represented the scene semantic knowledge of the image through topic words related to the image scene, then added them to the language model. We used the attention mechanism to determine the visual information and scene semantic information that the model pays attention to at each timestep and combined them to enable the model to generate more accurate and scene-specific captions. Finally, we evaluated our model on Microsoft COCO (MSCOCO) and Flickr30k standard datasets. The experimental results show that our approach generates more accurate captions and outperforms many recent advanced models in various evaluation metrics.

Existing attention models of image captioning typically extract only word-level attention information. i.e., the attention mechanism extracts local attention information from the image to generate the current word. We propose an image captioning approach based on self-attention to utilize image features more effectively. The self-attention mechanism can extract sentence-level attention information with richer visual representation from images. Furthermore, we propose a double attention model. The model combines sentence-level and word-level attention information to better simulate human perception system. We implement supervision and optimization in the intermediate stage of the model to solve over-fitting and information interference problems, and we apply reinforcement learning to two-stage training to optimize the evaluation metrics of the model. Finally, we evaluate our model on MSCOCO dataset. The experimental results show that our approach can generate more accurate and richer captions, and outperforms many state-of-the-art image captioning approaches on various evaluation metrics.

Rainfall data is useful in many fields such as urban management, agriculture, and so on. Spatial interpolation is widely used to interpolation continue rainfall data from discrete rainfall gauges. The uncertainty in spatial interpolation is change in different region. Paper focus on urban small area of Beijing city, Xicheng District and analyses uncertainty of spatial interpolation from four aspects: rainfall gauge number, density, position, spatial interpolation methods. RMSE and cross-validation is adopted to evaluate the accuracy of interpolation and the lowest RMSE is taken as optimal. The results suggest that more gauges can get a good performance with low error compared to little stations; and dense gauges network gets high accuracy than sparse station. Ordinary kriging is simple than other method and has a good estimation (except co-kriging) in small area spatial interpolation. Co-kriging has a high accuracy in interpolation but complex in computation and must be considering in the other variables.