System Malware Detection Using Machine Learning for Cybersecurity Risk and Management
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Keywords:
Malware detection, Machine Learning, Cybersecurity, Zero-day vulnerabilities, Feature extractionAbstract
In the context of the relentless increase in the velocities and complexities of cyberattacks, malware remains one of the major cybersecurity threats that organizations, individuals, and governments are facing. Traditional signature-based detection systems can’t keep up with evolving zero-day threats. The focus of malware detection in this study is to enhance it using machine learning algorithms. With machine learning models, automatically analyzing vast volumes of data can pick malicious patterns and allow the evolution of such in real-time by matching the pace with emerging threats. The work contributes to showing that machine learning-based malware detection systems enhance both the accuracy of detection and resistance to new malware variants. These adjuncts reduce cybersecurity risks. The challenges of reducing false positives are also discussed in the work, with suggestions for optimized feature extraction methods that enhance the performance and scalability of the system.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.