DocumentCode :
3106837
Title :
Threat prediction using honeypot and machine learning
Author :
Mehta, Vishal ; Bahadur, Pushpendra ; Kapoor, Manik ; Singh, Preeti ; Rajpoot, Subhadra
Author_Institution :
Mphasis-An Hp Co., Pune, India
fYear :
2015
fDate :
25-27 Feb. 2015
Firstpage :
278
Lastpage :
282
Abstract :
Data is an abstraction which encapsulates information. In today´s era businesses are data driven which gives insight to predict the destiny of the business by making predictions but another side of the coin is data also helps in placing the present health of the business under our radar and looking back in our past and answer some important questions: what exactly went wrong in the past?. In this paper we try to look into the architecture of frameworks which can predict threat using Honeypot as the source of data and various machine learning algorithms to make precise prediction using OSSEC as Host Intrusion Detection System [HIDS], SNORT for Network Intrusion Detection System [NIDS] and Honeyd an open source Honeypot.
Keywords :
business data processing; computer network security; data encapsulation; learning (artificial intelligence); public domain software; HIDS; Honeyd; NIDS; OSSEC; SNORT; business prediction; data source; frameworks architecture; host intrusion detection system; information encapsulation; machine learning; network intrusion detection system; open source honeypot; threat prediction; Computer hacking; Conferences; IP networks; Intrusion detection; Market research; Operating systems; Ports (Computers); High Interaction Honeypots (HIH); Host Intrusion Detection System (HIDS); Low Interaction Honeypots (LIH); Network Intrusion Detection System (NIDS);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-8432-9
Type :
conf
DOI :
10.1109/ABLAZE.2015.7155011
Filename :
7155011
Link To Document :
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