Malware Detection Using Machine Learning

Malware Detection Using Machine Learning

Registration ID: IJNRD_209603

Published ID: IJNRD2311276

DOI: Click Here to Get

Balasaheb Navanath Tambe , Omkar Ranjeet Dhaibar , Nilesh Ravindra Hiray

Zero-day malware, Machine Learning, Sandbox, Feature Extraction, Heuristic Analysis, Model Training.

Abstract - Zero-day or dull malware are made utilizing code befuddling techniques that pass down similar handiness of parent yet with various engravings. Malevolent programming, inferred as malware, is dependably making security danger, thus enormous areas of examination. The basic stage in unmistakable confirmation is evaluation. This consolidates either static or dynamic appraisal of known malware and performing isolation. Results of evaluation are refined into a "signature". One methodology for malware affirmation is the utilization of static engravings to survey programs after they are stacked and before execution. Authentic structures subject to AI are used to find plans identifying with malignant lead). In particular, it was demonstrated that detecting harmful traffic on computer systems, and thereby improving the security of computer networks, was possible using the findings of malware analysis and detection with machine learning algorithms to compute the difference in correlation symmetry (Naive Byes, SVM, J48, RF, and with the proposed approach) integrals. The results showed that when compared with other classifiers, DT (99%), CNN (98.76%), and SVM (96.41%) performed well in terms of detection accuracy. DT, CNN, and SVM algorithms’ performances detecting malware on a small FPR (DT = 2.01%, CNN = 3.97%, and SVM = 4.63%,) in a given dataset were compared. These results are significant, as malicious software is becoming increasingly common and complex.

Published Paper   E-Certificate

"Malware Detection Using Machine Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c629-c635, November-2023, Available :https://ijnrd.org/papers/IJNRD2311276.pdf

Volume 8 Issue 11, November-2023

Pages : c629-c635

Paper Reg. ID: IJNRD_209603

Published Paper Id: IJNRD2311276

Downloads: 000121143

Research Area: Computer Engineering 

Country: Kavhe, MAHARASHTRA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311276.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311276

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

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

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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