ISSN : 2319-7323

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE ENGINEERING

ABSTRACT

Title	:	PAYLOAD BASED INTERNET WORM DETECTION USING EURAL NETWORK CLASSIFIER
Authors	:	A.Tharani, B.Leelavathi
Keywords	:	Internet Worm Detection, Flow features, neural network, Novel detection.
Issue Date	:	Apr 2017
Abstract	:	With the capability of infecting hundreds of thousands of hosts, worms represent a major threat to the Internet. The detection against Internet worms is largely an open problem. Internet worms pose a serious threat to computer security. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of this research is shifting from using signature patterns to identifying the malicious behavior displayed by the Internet worms. This paper presents a novel idea of extracting flow level features that can identify worms from clean programs using data mining technique such as neural network classifier. Our approach showed 97.90% detection rate on Internet worms whose data was not used in the model building process.
Page(s)	:	103-110
ISSN	:	2319-7323
Source	:	Vol. 6, No.4