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dc.contributor.authorJadidi, Zahra
dc.contributor.authorMuthukkumarasamy, Vallipuram
dc.contributor.authorSithirasenan, Elankayer
dc.contributor.authorSheikhan, Mansour
dc.contributor.editorNFSP
dc.date.accessioned2017-05-03T16:08:41Z
dc.date.available2017-05-03T16:08:41Z
dc.date.issued2013
dc.date.modified2014-06-03T02:54:55Z
dc.identifier.isbn978-0-7695-5023-7
dc.identifier.issn1545-0678
dc.identifier.refurihttp://www.temple.edu/cis/icdcs2013/
dc.identifier.doi10.1109/ICDCSW.2013.40
dc.identifier.urihttp://hdl.handle.net/10072/59794
dc.description.abstractAbstract-Reliable high-speed networks are essential to provide quality services to ever growing Internet applications. A Network Intrusion Detection System (NIDS) is an important tool to protect computer networks from attacks. Traditional packet-based NIDSs are time-intensive as they analyze all network packets. A state-of-the-art NIDS should be able to handle a high volume of traffic in real time. Flow-based intrusion detection is an effective method for high speed networks since it inspects only packet headers. The existence of new attacks in the future is another challenge for intrusion detection. Anomaly-based intrusion detection is a well-known method capable of detecting unknown attacks. In this paper, we propose a flow-based anomaly detection system. Artificial Neural Network (ANN) is an important approach for anomaly detection. We used a Multi-Layer Perceptron (MLP) neural network with one hidden layer. We investigate the use of a Gravitational Search Algorithm (GSA) in optimizing interconnection weights of a MLP network. Our proposed GSA-based flow anomaly detection system (GFADS) is trained with a flow-based data set. The trained system can classify benign and malicious flows with 99.43% accuracy. We compare the performance of GSA with traditional gradient descent training algorithms and a particle swarm optimization (PSO) algorithm. The results show that GFADS is effective in flow-based anomaly detection. Finally, we propose a four-feature subset as the optimal set of features.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttps://ieeexplore.ieee.org/xpl/conhome/6679588/proceeding?pageNumber=2
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename33rd IEEE International Conference on Distributed Computing Systems (ICDCS)
dc.relation.ispartofconferencetitle2013 33RD IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW 2013)
dc.relation.ispartofdatefrom2013-07-08
dc.relation.ispartofdateto2013-07-11
dc.relation.ispartoflocationPhiladelphia, PA
dc.relation.ispartofpagefrom76
dc.relation.ispartofpageto81
dc.rights.retentionY
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode089999
dc.titleFlow-Based Anomaly Detection Using Neural Network Optimized with GSA Algorithm
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorMuthukkumarasamy, Vallipuram
gro.griffith.authorSithirasenan, Elankayer
gro.griffith.authorJadidi, Zahra


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    Contains papers delivered by Griffith authors at national and international conferences.

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