Designing a holistic end-to-end intelligent network analysis and security platform
Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo's syst...
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Published in | Journal of physics. Conference series Vol. 978; no. 1; pp. 12100 - 12108 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Bristol
IOP Publishing
01.03.2018
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Subjects | |
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Abstract | Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo's system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user's behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques. |
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AbstractList | Firewall protects a network from outside attacks, however, once an attack entering a network, it is difficult to detect. Recent significance accidents happened. i.e.: millions of Yahoo email account were stolen and crucial data from institutions are held for ransom. Within two year Yahoo’s system administrators were not aware that there are intruder inside the network. This happened due to the lack of intelligent tools to monitor user behaviour in internal network. This paper discusses a design of an intelligent anomaly/malware detection system with proper proactive actions. The aim is to equip the system administrator with a proper tool to battle the insider attackers. The proposed system adopts machine learning to analyse user’s behaviour through the runtime behaviour of each node in the network. The machine learning techniques include: deep learning, evolving machine learning perceptron, hybrid of Neural Network and Fuzzy, as well as predictive memory techniques. The proposed system is expanded to deal with larger network using agent techniques. |
Author | Alzahrani, M |
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Cites_doi | 10.1109/NSS.2009.55 10.1109/72.286889 10.1109/SAINTW.2005.1620003 10.1016/j.is.2009.08.003 10.1109/RoEduNet.2011.5993687 10.1109/COMST.2017.2707140 10.1109/ICHIT.2008.249 10.1016/S0167-4048(03)00710-7 |
ContentType | Journal Article |
Copyright | Published under licence by IOP Publishing Ltd 2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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References | 13 Rahmat R (21) 2015; 12 Wang C.Y. (1) 2009 14 Pasha MF (22) 2010; 6 15 Yin Q. (4) 2004 Liu F. (16) 2006 Barabas M (11) 2011 Gwon Y. L (17) 2014 Hawkins J. (19) 2005 2 Ngia J (18) 2011 Sun Y (3) 2010 Pasha MF (20) 2008 6 7 8 Qiao H. (5) 2007 9 Zhang J. (12) 2009; 09 Pais I. (10) 2009 Hasibuan Z A (23) 2009; 9 |
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SubjectTerms | Deep learning Fuzzy logic Intelligent networks Machine learning Malware Network analysis Neural networks Physics |
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Title | Designing a holistic end-to-end intelligent network analysis and security platform |
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