In the field of code plagiarism detection, assessing the presence of shared code fragments or similar structures has always been a challenge. Traditional text-based code plagiarism detection methods cannot provide accurate results for code with these characteristics. Therefore, this paper introduces a source code plagiarism detection technique based on multiple feature values. It outlines a method for extracting features from source code comments, structures, code text statements, and structures, and provides a measurement model for source code plagiarism detection. Through comparative experiments with the authoritative code plagiarism detection system Moss, the results indicate that the source code plagiarism detection technique based on multiple feature values achieves a more accurate code detection performance.
The data center has complete equipment, professional management, and a comprehensive application service platform. A good data center security protection system can help prevent dangers and violations that have a serious impact on the business. However, the level of security protection in data centers varies greatly, and the effectiveness of protection lacks means of testing and verification. This article studies the health evaluation method of data center security protection system, proposes a security protection measure evaluation mechanism. The evaluation method provides strong technical support for the effectiveness verification and promotion of existing data center security protection measures.
Aiming at the problems of single monitoring and management mode, poor real-time performance, low transparency, and difficulty in operation and maintenance of the current data room, a digital twin machine room dynamic environment monitoring system based on the Drools inference engine was constructed. In the virtual scene, the Drools rule engine is used to build an expert system for fault analysis and prediction in the data room, which improves the interactivity of the dynamic loop system in the data room, greatly improves the accuracy and timeliness of fault diagnosis, and has great application value.
In recent years, with the rapid development of technology fields such as big data, cloud computing, Internet of Things, and mobile Internet, security incidents such as network attacks and data information leakage have occurred frequently, which shows that the current information system falls in the serious security situation, and methods relying on the traditional security protection mechanism to ensure information security has gradually become inadequate. Compared with other software languages, Java language is widely used in the development of large-scale business systems due to its high access, concurrency, and clustering. Source code is the basic element of building a business application system, and logic vulnerabilities or nonstandard programming in code are the roots of application security events. This paper proposes a source code security defect assessment method based on the entropy weight method by deeply analyzing the Java source code security defect detection and repair methods.
With the acceleration of the digital construction of the power grid, many important data is collected in data centers. If a fault is not found in time, it may cause serious information security incidents. In terms of the above problems, a digital twin modeling of data center computer room (DC computer room) based on long short-term memory (LSTM) network is proposed in this paper to monitor and early warn the failures of important equipment in computer rooms. The model adopts a five-layer architecture of the equipment layer, data interaction layer, model construction layer, simulation analysis layer, and application layer. Meanwhile, the evaluation characteristics of the equipment in the data center room are constructed, and the time sequence parameters of the equipment are predicted in real time based on the long-term and short-term memory network, and the equipment that may fail is warned in advance to assist the maintenance personnel in equipment maintenance.
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