Efficient Intrusion Detection in Ad-Hoc Networks

We study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks via the prism of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following mesh networks. Current state-of-the-art (IDS) have been developed taking into consideration regular computer networks, and as such they do not efficiently addresses the paradigm of ad-hoc networks. In this work we firstly identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine tune this trade-off, we model such networks as Random Geometric Graphs; a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach by having only a subset of the nodes acting as IDS agents. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols such as RPL. Our detailed experimental evaluation demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes.

Content

Author and article information

Contributors

Mohammed Al Qurashi

Constantinos Marios Angelopoulos

Vasilios Katos

Conference

Publication date: September 2019

Publication date (Print): September 2019

Pages: 117-125

Affiliations

[0001]Bournemouth University

Faculty of Science & Technology

Dorset,Poole,UK

Article

DOI: 10.14236/ewic/icscsr19.15

SO-VID: be96ee19-1838-456f-b595-04e9373aec73

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 6th International Symposium for ICS & SCADA Cyber Security Research 2019

Conference acronym: ICS-CSR

Conference number: 6

Conference location: Athens, Greece

Conference date: 10th-12th September 2019

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Cyber Security Research

History

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/icscsr19.15

Self URI (journal page): https://ewic.bcs.org/

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C. KoliasG. KambourakisA. StavrouJ. Voas 2017 Ddos in the iot: Mirai and other botnets Computer 50 7 80 84
H. KumarageI. KhalilZ. TariA. Zomaya 2013 Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling Journal of Parallel and Distributed Computing 73 6 790 806
S. LiL. Xu DaS. Zhao 2015 The internet of things: a survey Information Systems Frontiers 17 2 243 259
Y. MalehA. EzzatiY. QasmaouiM. Mbida 2015 A global hybrid intrusion detection system for wireless sensor networks Procedia Computer Science 52 1047 1052
D. MidiA. RulloA. MudgerikarE. Bertino 2017 Kalisa system for knowledge-driven adaptable intrusion detection for the internet of things Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on IEEE 656 666
S. Y. MoonJ. W. KimT. H. Cho 2014 An energy-efficient routing method with intrusion detection and prevention for wireless sensor networks Advanced Communication Technology (ICACT), 2014 16th International Conference on IEEE 467 470
F. OsterlindA. DunkelsJ. ErikssonN. FinneT. Voigt 2006 Cross-level sensor network simulation with cooja Local computer networks, proceedings 2006 31st IEEE conference on IEEE 641 648
M. Penrose 2003 Random Geometric Graphs Oxford University Press
Y. PonomarchukD.-W. Seo 2010 Intrusion detection based on traffic analysis in wireless sensor networks Wireless and Optical Communications Conference (WOCC), 2010 19th Annual IEEE 1 7
S. RazaL. WallgrenT. Voigt 2013 SVELTE: Real-time intrusion detection in the Internet of Things Ad hoc networks 11 8 2661 2674
T. Ring 2015 Connected cars–the next targe tfor hackers Network Security 2015 11 11 16
Z. ShelbyC. Bormann 2011 6LoWPAN: The wireless embedded Internet 43 John Wiley & Sons
Z. ShelbyK. HartkeC. Bormann 2014 The constrained application protocol (coap), Technical report
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Z. ZhengA. LiuL. X. CaiZ. ChenX. S. Shen 2016 Energy and memory efficient clone detection in wireless sensor networks IEEE Transactions on Mobile Computing 15 5 1130 1143

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[1] L. CoppolinoS. D’AntonioA. GarofaloL. Romano 2013 Applying data mining techniques to intrusion detection in wireless sensor networks P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2013 Eighth International Conference on IEEE 247 254

[2] D. DujovneT. WatteyneX. VilajosanaP. Thubert 2014 6tisch: deterministic ip-enabled industrial internet (of things) IEEE Communications Magazine 52 12 36 41

[3] A. DunkelsB. GronvallT. Voigt 2004 Contiki-a lightweight and flexible operating system for tiny networked sensors Local Computer Networks 2004 29th Annual IEEE International Conference on IEEE 455 462

[4] P. GuptaP. Kumar 1998 Critical power for asymptotic connectivity in wireless networks Stochastic Analysis, Control, Optimization and Applications Boston

[5] C. KoliasG. KambourakisA. StavrouJ. Voas 2017 Ddos in the iot: Mirai and other botnets Computer 50 7 80 84

[6] H. KumarageI. KhalilZ. TariA. Zomaya 2013 Distributed anomaly detection for industrial wireless sensor networks based on fuzzy data modelling Journal of Parallel and Distributed Computing 73 6 790 806

[7] S. LiL. Xu DaS. Zhao 2015 The internet of things: a survey Information Systems Frontiers 17 2 243 259

[8] Y. MalehA. EzzatiY. QasmaouiM. Mbida 2015 A global hybrid intrusion detection system for wireless sensor networks Procedia Computer Science 52 1047 1052

[9] D. MidiA. RulloA. MudgerikarE. Bertino 2017 Kalisa system for knowledge-driven adaptable intrusion detection for the internet of things Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on IEEE 656 666

[10] S. Y. MoonJ. W. KimT. H. Cho 2014 An energy-efficient routing method with intrusion detection and prevention for wireless sensor networks Advanced Communication Technology (ICACT), 2014 16th International Conference on IEEE 467 470

[11] F. OsterlindA. DunkelsJ. ErikssonN. FinneT. Voigt 2006 Cross-level sensor network simulation with cooja Local computer networks, proceedings 2006 31st IEEE conference on IEEE 641 648

[12] M. Penrose 2003 Random Geometric Graphs Oxford University Press

[13] Y. PonomarchukD.-W. Seo 2010 Intrusion detection based on traffic analysis in wireless sensor networks Wireless and Optical Communications Conference (WOCC), 2010 19th Annual IEEE 1 7

[14] S. RazaL. WallgrenT. Voigt 2013 SVELTE: Real-time intrusion detection in the Internet of Things Ad hoc networks 11 8 2661 2674

[15] T. Ring 2015 Connected cars–the next targe tfor hackers Network Security 2015 11 11 16

[16] Z. ShelbyC. Bormann 2011 6LoWPAN: The wireless embedded Internet 43 John Wiley & Sons

[17] Z. ShelbyK. HartkeC. Bormann 2014 The constrained application protocol (coap), Technical report

[18] Statista 2018 Size of the internet of things market worldwide in 2014 and 2020, by industry (in billion u.s. dollars) URL: https://www.statista.com/statistics/512673/worldwide-internet-of-things-market/

[19] Z. ZhengA. LiuL. X. CaiZ. ChenX. S. Shen 2016 Energy and memory efficient clone detection in wireless sensor networks IEEE Transactions on Mobile Computing 15 5 1130 1143

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