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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: | Balasaheb Navanath Tambe , Omkar Ranjeet Dhaibar , Nilesh Ravindra Hiray |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_209603
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| Published Paper Id: | IJNRD2311276 |
| Published In: | Volume 8 Issue 11, November-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Zero-day malware, Machine Learning, Sandbox, Feature Extraction, Heuristic Analysis, Model Training. |
| Cite Article: | "Malware Detection Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c629-c635, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311276.pdf |
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| 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:IJNRD2311276 Registration ID: 209603 Published In: Volume 8 Issue 11, November-2023 DOI (Digital Object Identifier): Page No: c629-c635 Country: Kavhe, MAHARASHTRA, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2311276 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2311276 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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