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Malware Analysis Detection
Subject area: Science,Engineering and Technology · Area of research: Computer Engineering
Abstract
The term malware stands for malicious software. It is a program installed on a system without the knowledge of owner of the system. It is basically installed by the third party with the intention to steal some private data from the system.One of the major and serious threats on the Internet today is malicious software, often referred to as a malware. The malwares being designed by attackers are polymorphic and metamorphic which have the ability to change their code as they propagate. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executable. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malwares into their known families using machine learning techniques. This survey paper provides an overview of techniques for analyzing and classifying the malwares.
Keywords
Malware, Malware Analysis, Static Malware Analysis, Dynamic Malware Analysis, exploited etc.
How to cite this paper
@article{1700619,
author = {Rahul, Ravindra Soni},
title = {Malware Analysis Detection},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {1},
number = {10},
pages = {132-135},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1700619.pdf},
abstract = {The term malware stands for malicious software. It is a program installed on a system without the knowledge of owner of the system. It is basically installed by the third party with the intention to steal some private data from the system.One of the major and serious threats on the Internet today is malicious software, often referred to as a malware. The malwares being designed by attackers are polymorphic and metamorphic which have the ability to change their code as they propagate. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executable. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malwares into their known families using machine learning techniques. This survey paper provides an overview of techniques for analyzing and classifying the malwares.},
keywords = {Malware, Malware Analysis, Static Malware Analysis, Dynamic Malware Analysis, exploited etc.},
month = {April},
}