Enhancing Intelligent Alarm Reduction for Distributed Intrusion Detection Systems via Edge Computing

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

4 Scopus Citations
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Author(s)

  • Weizhi Meng
  • Yu Wang
  • Zhe Liu
  • Jin Li
  • Christian W. Probst

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationInformation Security and Privacy
Subtitle of host publication23rd Australasian Conference, ACISP 2018, Proceedings
EditorsWilly Susilo, Guomin Yang
PublisherSpringer, Cham
Pages759-767
ISBN (Electronic)9783319936383
ISBN (Print)9783319936376
Publication statusPublished - Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Security and Cryptology)
PublisherSpringer, Cham
VolumeLNCS 10946
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title23rd Australasian Conference on Information Security and Privacy, ACISP 2018
PlaceAustralia
CityWollongong
Period11 - 13 July 2018

Abstract

To construct an intelligent alarm filter is a promising solution to help reduce false alarms for an intrusion detection system (IDS), in which an appropriate algorithm can be selected in an adaptive way. Taking the advantage of cloud computing, the process of algorithm selection can be offloaded to the cloud, but it may cause communication delay and additional burden on the cloud side. This issue may become worse when it comes to distributed intrusion detection systems (DIDSs), i.e., some IoT applications might require very short response time and most of the end nodes in IoT are energy constrained things. In this paper, with the advent of edge computing, we propose a framework for improving the intelligent false alarm reduction for DIDSs based on edge computing devices (i.e., the data can be processed at the edge for shorter response time and could be more energy efficient). The evaluation shows that the proposed framework can help reduce the workload for the central server and shorten the delay as compared to the similar studies.

Research Area(s)

  • Cloud computing, Distributed environment, Edge computing, Intelligent false alarm filtration, Intrusion detection

Citation Format(s)

Enhancing Intelligent Alarm Reduction for Distributed Intrusion Detection Systems via Edge Computing. / Meng, Weizhi; Wang, Yu; Li, Wenjuan; Liu, Zhe; Li, Jin; Probst, Christian W.

Information Security and Privacy: 23rd Australasian Conference, ACISP 2018, Proceedings. ed. / Willy Susilo; Guomin Yang. Springer, Cham, 2018. p. 759-767 (Lecture Notes in Computer Science (including subseries Security and Cryptology); Vol. LNCS 10946).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)