MACHINE LEARNING TECHNIQUE TO DETECT BEHAVIOR BASED MALWARE
Author(s):
Ramya sree.K, P. FIROZE KHAN, Pavitra.P, Pranathi.E, Prasanna.U
Keywords:
Adware, Botnets, Malware Analysis
Abstract
With the increased use of the internet and computer systems, data (personal and professional) security has become a serious challenge. Computers using the internet download massive amounts of data from the internet, which may potentially include viruses. Malware is known by many various names, including malicious code, harmful programmes, and malicious executable files. The increasing sophistication of malware assaults has rendered computer systems increasingly vulnerable to hacking. According to Kaspersky Laboratories, malware is "a form of computer software designed to infect a legitimate user's computer and inflict harm on it in a variety of ways". With the rising diversity of malwares, anti-virus scanners cannot guarantee the identification of every form of malware based on its signature, resulting in millions of hosts being targeted and inflicting significant harm to data and other connected systems. According to Kaspersky Lab (2016), 6,563,145 distinct machines were attacked, with around 4,000,000 new forms of malware discovered. As a result, safeguarding the network and user machines from malware is a critical cyber security duty for a single user or a whole enterprise, because a single assault may result in considerable loss and harm. The goal of this work is to provide a malware detection system that will give an effective approach to identify malware based on the actions it may undertake on the machine on which it is installed. Malware comes in many forms however the following are the most common.
Article Details
Unique Paper ID: 159630

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 475 - 479
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