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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | MALWARE DETECTION USING MACHINE LEARNING |
| Authors Name: | Chalamalla Nikhil kumar reddy |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_212001
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| Published Paper Id: | IJNRD2401065 |
| Published In: | Volume 9 Issue 1, January-2024 |
| DOI: | |
| Abstract: | Malicious software, commonly referred to as malware, continues to be a persistent and evolving threat in the realm of cybersecurity. As traditional signature-based detection methods struggle to keep pace with the dynamic nature of malware, the integration of machine learning techniques has emerged as a promising avenue for enhancing detection accuracy. This paper offers a comprehensive review and analysis of the current state of malware detection using machine learning. The review begins by outlining the challenges posed by the ever-changing landscape of malware, emphasizing the limitations of conventional detection methods. The subsequent exploration of machine learning algorithms, including supervised learning (e.g., Decision Trees, Support Vector Machines), unsupervised learning (e.g., Clustering algorithms), and deep learning (e.g., Neural Networks), highlights their potential in improving detection capabilities. A critical component of effective malware detection is feature extraction, and this paper delves into various static, dynamic, and hybrid analysis features. It underscores the importance of feature selection in refining the accuracy of machine learning models. The discussion extends to datasets used for training and evaluation, examining publicly available datasets such as the Malware Genome Project and the Microsoft Malware Classification Challenge. The evaluation metrics, including Precision, Recall, and F1 Score, are elucidated, along with the challenges inherent in assessing machine learning models for malware detection. Furthermore, the paper identifies and discusses challenges and limitations in the application of machine learning, such as adversarial attacks, imbalanced datasets, and the need for models to generalize across rapidly evolving malware variants. These challenges underscore the importance of ongoing research to address these issues. Looking ahead, the paper outlines future directions in the field, including the incorporation of explainability in machine learning models, the utilization of ensemble learning for improved accuracy, and the exploration of real-time and proactive detection approaches. The integration of machine learning with threat intelligence feeds is also proposed as a promising avenue for enhancing overall cybersecurity. In conclusion, this paper provides a thorough examination of the use of machine learning in malware detection, offering insights into the current state of the field, its challenges, and future directions. It aims to serve as a valuable resource for researchers, practitioners, and cybersecurity professionals engaged in fortifying defenses against the persistent and adaptive threat of malware. |
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| Cite Article: | "MALWARE DETECTION USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 1, page no.a568-a575, January-2024, Available :http://www.ijnrd.org/papers/IJNRD2401065.pdf |
| Downloads: | 000118765 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2401065 Registration ID: 212001 Published In: Volume 9 Issue 1, January-2024 DOI (Digital Object Identifier): Page No: a568-a575 Country: hyderabad, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2401065 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2401065 |
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ISSN: 2456-4184
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