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01.01.2025На каком языке издана
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The paper provides an overview of the anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities. Main objective is to provide a comprehensive understanding of the various methodologies, algorithms, and frameworks that have been developed to enhance the security and reliability of software systems. In paper determines early research in anomaly detection techniques from 2019 and followed methods, approaches and frameworks until recent times. According to research are defined challenges such as high computational costs, false positives, and the need for large, labeled datasets persist. The work specifies ML methods and calculates classic classification metrics such as accuracy, F1 score, precision, recall, geometric mean, etc. This review underscores the importance of continuous advancements in anomaly detection techniques to address the ever-evolving landscape of code vulnerabilities.DOI
10.1109/ICECET63943.2025.11472453Тип публикаций
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Scopus
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Anomaly Detection on Code Vulnerabilities.pdf