Sentinel Based Malicious Relay Detection in Wireless IoT Networks

다국어 초록 (Multilingual Abstract)

Increased device connectivity and information sharingin wireless IoT networks increases the risk of cyber attack by maliciousnodes. In this paper, we present an effective and practicalscheme for detecting data integrity and selective forwarding attackslaunched by malicious relays in wireless IoT networks. Theproposed scheme exploits the broadcast nature of wireless transmissionand provides a sentinel based approach to intrusion detection.
Our detection scheme assumes a general noise model for thenetwork where different wireless links may have different packeterror probability (PEP). Further, our detection scheme is effectiveeven in scenarios where different wireless links in the networkemploy distinct modulation and coding schemes at the physicallayer. This detection scheme has application in practical wirelessIoT networks, such as those based on the recently introduced IEEE802.11ah standard.

참고문헌 (Reference)

1 B. Malik, "Why IoT security should be top of your list"

2 K. Roby, "Why IoT devices pose a bigger cybersecurity risk than most realize"

3 Y. Mao, "Tracing malicious relays in cooperative wireless communications" 2 (2): 198-212, 2007

4 R. Langner, "Stuxnet : Dissecting a cyberwarfare weapon" 9 (9): 49-51, 2011

5 A. Tandon, "Sentinel based malicious relay detection scheme for wireless IoT networks" 2018

6 R. G. Gallager, "Principles of Digital Communication" Cambridge University Press 2008

7 S. W. Kim, "Physical integrity check in cooperative relay communications" 14 (14): 6401-6413, 2015

8 B. Krebs, "P2P weakness exposes millions of IoT devices"

9 S. Marti, "Mitigating routing misbehavior in mobile ad hoc networks" 255-265, 2000

10 Y. Mehmood, "Internet-of-things-based smart cities : Recent advances and challenges" 55 (55): 16-24, 2017

11 X. Liu, "Identifying malicious nodes in multihop IoT networks using diversity and unsupervised learning" 1-6, 2018

12 "IEEE standard for information technology–telecommunications and information exchange between systems - local and metropolitan area networks–specific requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 2:Sub 1 GHz License Exempt Operation,” IEEE Std 802.11ah-2016 (Amendment to IEEE Std 802.11-2016, as amended by IEEE Std 802.11ai-2016)" 1-594, 2017

13 W. Wesley Peterson, "Error-Correcting Codes" The M.I.T. Press 1972

14 S. Y. Nam, "Enhanced ARP : Preventing ARP poisoning-based man-in-the-middle attacks" 14 (14): 187-189, 2010

15 D. Agarwal, "Detection of nodemisbehavior using overhearing and autonomous agents in wireless ad-hoc networks" 152-157, 2015

16 C. Tumrongwittayapak, "Detecting sinkhole attack and selective forwarding attack in wireless sensor networks" 1-5, 2009

17 T. H. Hai, "Detecting selective forwarding attacks in wireless sensor networks using two-hops neighbor knowledge" 325-331, 2008

18 C. Jia, "Detecting cluster head attacks in heterogeneous wireless sensor networks" 1-6, 2017

19 L. F. D. Carpio, "Comparison of 802.11ah and BLE for a home automation use case" 1-6, 2016

20 X. Liu, "Bayesian test for detecting false data injection in wireless relay networks" 22 (22): 380-383, 2018

21 J. A. Hartigan, "Algorithm AS 136 : A K-Means clustering algorithm" 28 (28): 100-108, 1979

22 J. Ren, "Adaptive and channel-aware detection of selective forwarding attacks in wireless sensor networks" 15 (15): 3718-3731, 2016

23 H. Xu, "A survey on industrial internet of things : A cyber-physical systems perspective" 6 : 78238-78259, 2018

24 C. Pu, "A light-weight countermeasure to forwarding misbehavior in wireless sensor networks : Design, analysis, and evaluation" 12 (12): 834-842, 2018

25 N. Ahmed, H. Rahman, "A comparison of 802.11ah and 802.15.4 for IoT" 한국통신학회 2 (2): 100-102, 2016