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Anomaly-based Network Intrusion Detection Model using Deep Learning in Airports

Sezari, Behrooz and Möller, Dietmar P. F. and Deutschmann, Andreas (2018) Anomaly-based Network Intrusion Detection Model using Deep Learning in Airports. In: Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. IEEE TrustCom 2018. IEEE Trust Com 2018, 31.Jul.2018, New York City. ISBN 978-153864387-7

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Abstract

The number of cyber-attacks are growing quickly and we are encountering modern and complex network intrusion attacks everyday even in secure computer networks. Last year, many airports in different countries were under attack of multiple network intrusions in various cybersegments especially Information and Communication Technology (ICT) system (e.g. Ransomware attacks). Such cyber-attacks could happen again in much more destructive ways which can cause irreparable losses, and endanger human life by disruption and corruption of the airport ICT system. We are approaching an anomaly-based Network Intrusion Detection System (IDS) using deep learning which provides a normal system behavior model and detects an abnormal behavior. In other words, this model is designed to detect not only known network intrusion attacks, but also unknown and modern attacks. We have trained and tested our model with DARPA dataset used in KDD 1999 Cup. Our model achieved an outstanding result with highly accurate detection rate, also low false alarm rate, which is superior to the previous researches conducted on this dataset.

Item URL in elib:https://elib.dlr.de/121797/
Document Type:Conference or Workshop Item (Poster)
Title:Anomaly-based Network Intrusion Detection Model using Deep Learning in Airports
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Sezari, BehroozUNSPECIFIEDUNSPECIFIED
Möller, Dietmar P. F.dietmar.moeller (at) tu-clausthal.deUNSPECIFIED
Deutschmann, AndreasAndreas.Deutschmann (at) dlr.deUNSPECIFIED
Date:31 July 2018
Journal or Publication Title:Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Publisher:IEEE TrustCom 2018
ISBN:978-153864387-7
Status:Published
Keywords:Intrusion Detection, Cyber-Security, Deep Learning, Feedforwards Neural Network, Network Intrusion Detection
Event Title:IEEE Trust Com 2018
Event Location:New York City
Event Type:international Conference
Event Dates:31.Jul.2018
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Optimode.net (old)
Location: Braunschweig
Institutes and Institutions:Institute of Air Transport and Airport Research > Airport Research
Deposited By: Deutschmann, Andreas
Deposited On:24 Sep 2018 09:29
Last Modified:09 Oct 2018 10:17

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