To maintain secure web services and IT infrastructure, this has proposed the prototype of a vulnerability reporting system. Among these reporting systems developed to deal with problems of maintenance and analysis report management system which not yet been implemented in the cyber security scanning tool, Nessus. The system, which was designed to manage and analyze cyber security maintenance by focusing on vulnerability reports, was based on web-based technology. To ensure that the prototyping system could be used quickly, the development process employed the prototyping-rapid application development technique. The administrator of this system may easily manage and keep track of the report following the activity of scanning the cyber security maintenance for vulnerabilities.
In today's digital landscape, the identification of malicious software has become a crucial undertaking. The evergrowing volume of malware threats renders conventional signature-based methods insufficient in shielding against novel and intricate attacks. Consequently, machine learning strategies have surfaced as a viable means of detecting malware. The following research report focuses on the implementation of classification machine learning methods for detecting malware. The study assesses the effectiveness of several algorithms, including Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Logistic Regression, through an examination of a publicly accessible dataset featuring both benign files and malware. Additionally, the influence of diverse feature sets and preprocessing techniques on the classifiers' performance is explored. The outcomes of the investigation exhibit that machine learning methods can capably identify malware, attaining elevated precision levels and decreasing false positive rates. Decision Tree and Random Forest display superior performance compared to other algorithms with 100.00% accuracy. Furthermore, it is observed that feature selection and dimensionality reduction techniques can notably enhance classifier effectiveness while mitigating computational complexity. Overall, this research underscores the potential of machine learning approaches for detecting malware and offers valuable guidance for the development of successful malware detection systems.
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