Detecting Malware Based on Portable Executable Analysis

Pandharpote, Shubham Prashant (2021) Detecting Malware Based on Portable Executable Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

The antivirus software work on the principle of detecting the virus based on the signatures. However, the malware developers have developed more powerful these days for which sometimes malware detection based on signature becomes difficult. To tackle this problem, the system designed extracts the features from Portable executables, which are analyzed with the help of machine learning techniques. The portable executable is a file format for executables, object code, Data Link Library (DLLs), and other portable executives used in 32 bits and 64 bits versions of Windows Operating systems. The paper is regarding the detection of Malware by analyzing portable executable files with the help of Machine Learning Techniques. The dataset is used which consists of malicious portable executable files. Machine Learning Techniques, Support Vector Machine (SVM), and Gradient Boosting are being used to train the structure by extracting features in such a way that a particular file is being detected after feature extraction. The features extracted from the dataset are Optional Header and Section Header. After the implementation process was carried out, based on it, accuracy was calculated.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Malware; Portable Executables; Windows Operating System; Support Vector Machine (SVM); Gradient Boosting
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	29 Dec 2022 10:37
Last Modified:	07 Mar 2023 12:42
URI:	https://norma.ncirl.ie/id/eprint/6034

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