Machine Learning for Malware Detection

Registration ID: IJNRD_221121

Published ID: IJNRD2405285

DOI: Click Here to Get

James K.Davids

Machine learning, malware detection, Data collection, unsupervised learning, supervised learning, semi- learning supervised learning and deep learning

In the dynamic landscape of cybersecurity, the emergence and spread of malware present significant threats to individuals, organizations, and society at large. Malware, a term encompassing various harmful programs crafted to infiltrate systems, pilfer data, disrupt operations, or compromise security, continuously evolves, posing challenges to traditional signature-based detection methods. These conventional techniques struggle to keep pace with the rapid evolution of malware variants, necessitating the adoption of more advanced approaches. Machine learning, with its capacity to scrutinize extensive datasets and discern intricate patterns, emerges as a potent ally in combating malware. This introduction offers an overview of employing machine learning algorithms for malware detection, accentuating the hurdles posed by contemporary cyber threats and the potential of machine learning to effectively counter them. It explores the foundational principles of malware detection, examines the strengths and limitations of conventional methodologies, and elucidates how machine learning techniques present innovative solutions to augment detection precision and efficacy. The proliferation of malware presents a daunting challenge to cybersecurity experts, as cybercriminals continuously devise sophisticated techniques to evade detection and breach systems. Traditional signature-based detection mechanisms rely on predefined patterns or signatures to identify known malware strains. While effective against recognized threats, these methods stumble in detecting previously unseen or zero-day attacks, which exploit vulnerabilities before patches or signatures become available. Moreover, the sheer volume and diversity of malware variants render manual signature creation and maintenance impractical. Machine learning algorithms herald a paradigm shift in malware detection by harnessing data-driven analysis to pinpoint malicious behavioral patterns. By training on extensive datasets containing both benign and malicious samples, machine learning models can discern normal from anomalous behavior, thereby detecting previously unseen malware variants. These models exhibit a capacity to generalize across diverse samples, rendering them particularly adept at identifying zero-day attacks and emerging threats across various platforms and environments, spanning from endpoint security solutions to network intrusion detection systems.

Published Paper   E-Certificate

James K.Davids (May-2024). Machine Learning for Malware Detection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c810-c820. https://ijnrd.org/papers/IJNRD2405285.pdf

Volume 9 Issue 5, May-2024

Pages : c810-c820

Paper Reg. ID: IJNRD_221121

Published Paper Id: IJNRD2405285

Downloads: 000121979

Research Area: Computer Science & Technology 

Country: Raipur , Chhattisgarh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405285.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405285

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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