2019
DOI: 10.22214/ijraset.2019.6010
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Enhancing Malware Detection with Static Analysis using Machine Learning

Abstract: As malware increases nowadays, it is necessary to safeguard your system from the malware. Malware is being protected by traditional methods but it only protects system from the malware whose signature is known. So we aim to prepare a software which detects the malware (signature is unknown) by the malware(signature is known) and after detecting the malware, steps must be taken to retrain the model and disallow the malware to compromise the system. For detecting the malware already known machine learning method… Show more

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Cited by 5 publications
(3 citation statements)
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“…These tools help cover security cautions and help malwareattacks.However, we must exclude it before it transmits its infection any farther, If malware is dangerous. Malware analysis is getting decreasingly popular as it helps businesses lessen the goods of the growing number of malware pitfalls and the adding complexity of the ways malware can be used to attack [10].…”
Section: Literature Reviewmentioning
confidence: 99%
“…These tools help cover security cautions and help malwareattacks.However, we must exclude it before it transmits its infection any farther, If malware is dangerous. Malware analysis is getting decreasingly popular as it helps businesses lessen the goods of the growing number of malware pitfalls and the adding complexity of the ways malware can be used to attack [10].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The objective was to optimize the model's performance by leveraging the power of feature selection. (23)…”
Section: Features Selectionmentioning
confidence: 99%
“…(1) Malware refers to a collection of programs or instructions intentionally created to cause harm to computer systems, users, businesses, or individual computers. (23) This research project focuses on demonstrating the practicality of detecting malicious network traffic within computer systems, effectively bolstering the security of computer networks. Achieving this objective involves utilizing machine learning algorithms and leveraging the results of malware analysis to calculate differential correlation symmetry integrals.…”
Section: Introductionmentioning
confidence: 99%