Machine Learning Techniques for Polymorphic Malware Analysis and Identification


Authors : Rajashekar Kandakatla; K. Rajakumari

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/4kbxcjk5

Scribd : https://tinyurl.com/2jztukt6

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV1395

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Abstract : Internet users are now experiencing one of the biggest problem is malware. Polymorphic malware refers to harmful software and versatile compare to the other traditional viruses. Malware that is polymorphic continuously alters its signature characteristics to evade detection by conventional malware detection techniques that rely on signatures. In order to detect malware or harmful threats, we employed many machine learning approaches. Based on a high detection rate, best accuracy was selected by the algorithm in this method. One of the benefits by the confusion matrix is that to measure the number of false positives and also number of false negatives, providing deeper insights into the system's effectiveness. Specifically, it was demonstrated that the outcomes of analysis of malware and discovery using ML techniques to quantify the variation in correlation equilibrium integrals could enhance the security of computer networks by identifying malicious traffic on computer systems. According to the findings in percentage, support vector machine is 96.41, Convolutional neural networks is 98.76, Decision tree is 99 do better than the other classifiers to finding the accuracy. Malware detection capabilities of these algorithms were evaluated on a tiny False Positive Rate (Support Vector Machine is 4.63, Convolutional Neural Networks is 3.97 and Decision Tree is 2.01) in a particular dataset. Given the rise in sophistication and prevalence of malicious software, these findings are noteworthy.

Keywords : Machine Learning, Malicious Threats, Cyber Security, Cyber Attacks, Convolutional Neural Networks.

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Internet users are now experiencing one of the biggest problem is malware. Polymorphic malware refers to harmful software and versatile compare to the other traditional viruses. Malware that is polymorphic continuously alters its signature characteristics to evade detection by conventional malware detection techniques that rely on signatures. In order to detect malware or harmful threats, we employed many machine learning approaches. Based on a high detection rate, best accuracy was selected by the algorithm in this method. One of the benefits by the confusion matrix is that to measure the number of false positives and also number of false negatives, providing deeper insights into the system's effectiveness. Specifically, it was demonstrated that the outcomes of analysis of malware and discovery using ML techniques to quantify the variation in correlation equilibrium integrals could enhance the security of computer networks by identifying malicious traffic on computer systems. According to the findings in percentage, support vector machine is 96.41, Convolutional neural networks is 98.76, Decision tree is 99 do better than the other classifiers to finding the accuracy. Malware detection capabilities of these algorithms were evaluated on a tiny False Positive Rate (Support Vector Machine is 4.63, Convolutional Neural Networks is 3.97 and Decision Tree is 2.01) in a particular dataset. Given the rise in sophistication and prevalence of malicious software, these findings are noteworthy.

Keywords : Machine Learning, Malicious Threats, Cyber Security, Cyber Attacks, Convolutional Neural Networks.

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