Due to its exceptional performance and memory security, Java is extensively employed in enterprise applications, thereby rendering any vulnerability in Java applications capable of causing significant repercussions. In recent years, due to the frequent occurrence of network attacks, new attack methods continue to emerge while software systems become increasingly intricate and unstable under the weight of historical burdens. Consequently, static analysis techniques are imperative for identifying security risks in code during the coding phase. The prevailing approach in static analysis involves transforming code into a standardized intermediate representation and conducting vulnerability detection, without code execution. However, this methodology often yields detection results accompanied by a considerable false positive rate. We propose VulnSlicer, a tool based on graph similarity matching and vulnerability subgraph slice technology. This tool not only enables efficient vulnerability retrieval but also effectively reduces the false positive rate by leveraging the slice mechanism to save historical vulnerabilities. Additionally, it facilitates rapid matching of accumulated vulnerabilities in the database and further mitigates false positives through the introduction of a keyframe mechanism. In the experiment, we conducted a comparative analysis of several existing static detection tools to assess their effectiveness in detecting vulnerabilities within the same codebase. Experiments show that VulnSlicer has decent accuracy and lower false positive rate.
this paper proposed a cross-chain multi-dimensional reputation model based on block-chain to deal with the nodes’ risk assessment in block-chain. When the nodes joined the network, the block-chain was difficult to evaluate these nodes’ behavioral changes especially those happened in recent time. This model comprehensively took the individual behavior and transaction history of enterprise nodes, and adjusted the reputation value of the initial node dynamically by means of the recommendation code. Meanwhile, the reputation model has been brought in a liability sharing mechanism to construct a multi-level and dynamic reputation evaluation mechanism. At same time, the quantum random number could be used in this model to distinguish the malicious nodes from the honest ones. Finally, the test of the improved multi-dimensional reputation model scheme was implemented on the Fabric simulation system. The experimental results show that this model could effectively identify the possible deception behavior of nodes after joining the block-chain, improve the overall security and the success rate of transactions, enhance the users’ trust.
Elastic ring (ER) is widely adopted for its compact size and ease of assembly and have become a prevalent type of elastic support within rotor-support systems. However, it may exhibit stiffness nonlinearity under specific operating conditions, potentially compromising the safety of the rotor system. This paper examines the influence of elastic ring stiffness on the critical speed of a specific aero-engine rotor support structure, focusing on related nonlinear phenomena. The Levenberg-Marquardt method was employed to optimize key parameters, including the stiffness and clearance of the elastic ring, with the second-order critical speed as the optimization goal. Finite element analysis was performed to validate the optimization results, which were found to be both effective and practically applicable. The finding provides a significant reference for the design and analysis of aero-engine rotor systems and similar applications.
BGP route leak represents a significant vulnerability in BGP security. The occurrence of BGP route leak can lead to increased internet latency and diminished service quality in less severe cases, and in more severe instances, it can result in large-scale regional outages or substantial traffic redirection, causing considerable financial losses to network operators and users. Thus, the ability to accurately and promptly identify route leak is of paramount importance for maintaining internet security. Current route leak detection methods typically involve a trade-off between accuracy and real-time performance, making it challenging to achieve both simultaneously. To address this issue, this paper proposes the Leak-Detector framework, which synthesizes the strengths of existing advanced methodologies. By integrating AS hegemony indicators with machine learning models, Leak-Detector provides accurate and real-time detection of route leak incidents, and it can precisely report both the suspects and the timing of the leak. Application of Leak-Detector to real-world route leak detection demonstrates that it achieves an accuracy of 85%, recall of 84%, and F1 score of 83%, with minimal detection latency. Furthermore, it accurately identifies most route leak suspects and their occurrence times.
In recent years, learning based methods are introduced to identify the elements in the point cloud, which are easily captured with UAV and LiDAR. However, supervised learning needs great efforts in data labeling, and cannot well adapt to the point clouds in different places. In this paper, we propose an unsupervised point cloud segmentation method that can can distinguish identical elements from the point cloud. Firstly, with the collected point cloud, pre-processing including denoising and downsampling is conducted. After that, cloud elements such as transmission lines, towers, buildings, roads and vegetation are extracted with unsupervised algorithms including DBSCAN, and other point cloud processing algorithms such as cloth filtering and PCA. To be specific, intrinsic features, e.g., the evaluation, the density, the curvature, of the point clouds are used in the unsupervised learning and clustering. Finally, with the safety distance rules, potential danger points that locate close to the transmission lines are detected, and a danger point report is further generated. In addition, based on the algorithm, a point analysis and management system is developed. With a B/S architecture, the system provide the function of point cloud uploading, viewing, analysis, and danger point prediction. The research results is hoped to be applied in grid enterprise to improve the intelligent level of power grid management and maintenance.
