TY - GEN
T1 - Evolving neural network intrusion detection system for MCPS
AU - Mowla, Nishat
AU - Doh, Inshil
AU - Chae, Ki Joon
N1 - Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011788).
Publisher Copyright:
© 2017 Global IT Research Institute - GiRI.
PY - 2017/3/29
Y1 - 2017/3/29
N2 - Medical Cyber Physical Systems (MCPS) are some of the most promising next generation technologies so far. Like many other systems connected to a wider network such as internet, MCPS are also vulnerable to various forms of network attacks. For detecting such diverse forms of attack, we need smart and efficient mechanisms. Human intelligence is good enough to track such attacks but when it is a huge number of traffic it is no more a feasible process to detect them manually as it is time consuming and computationally intensive. Machine learning techniques embracing artificial intelligence are emerging as powerful tools to detect abnormalities in the network data. Supervised Neural Networks are some of the most efficient techniques to perform such classification. In this paper, we propose neural network technique that evolves based on classification, elimination and prioritization while considering time, space, and accuracy to efficiently classify the four major types of network attack traffic found in an effectively pruned KDD dataset.
AB - Medical Cyber Physical Systems (MCPS) are some of the most promising next generation technologies so far. Like many other systems connected to a wider network such as internet, MCPS are also vulnerable to various forms of network attacks. For detecting such diverse forms of attack, we need smart and efficient mechanisms. Human intelligence is good enough to track such attacks but when it is a huge number of traffic it is no more a feasible process to detect them manually as it is time consuming and computationally intensive. Machine learning techniques embracing artificial intelligence are emerging as powerful tools to detect abnormalities in the network data. Supervised Neural Networks are some of the most efficient techniques to perform such classification. In this paper, we propose neural network technique that evolves based on classification, elimination and prioritization while considering time, space, and accuracy to efficiently classify the four major types of network attack traffic found in an effectively pruned KDD dataset.
KW - Intrusion detection system
KW - MCPS
KW - Machine learning
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85018466404&partnerID=8YFLogxK
U2 - 10.23919/ICACT.2017.7890080
DO - 10.23919/ICACT.2017.7890080
M3 - Conference contribution
AN - SCOPUS:85018466404
T3 - International Conference on Advanced Communication Technology, ICACT
SP - 183
EP - 187
BT - 19th International Conference on Advanced Communications Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Advanced Communications Technology, ICACT 2017
Y2 - 19 February 2017 through 22 February 2017
ER -