DocumentCode :
3659753
Title :
An efficient classification model for detecting advanced persistent threat
Author :
Saranya Chandran; Hrudya P;Prabaharan Poornachandran
Author_Institution :
Amrita Center for Cybersecurity, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, India
fYear :
2015
Firstpage :
2001
Lastpage :
2009
Abstract :
Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.
Keywords :
"Mathematical model","Feature extraction","Malware","Vegetation","Training","Organizations"
Publisher :
ieee
Conference_Titel :
Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on
Print_ISBN :
978-1-4799-8790-0
Type :
conf
DOI :
10.1109/ICACCI.2015.7275911
Filename :
7275911
Link To Document :
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