Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 4/2019

24-10-2018

Sinkhole Attacks in Wireless Sensor Networks: A Survey

Authors: Aqeel-ur Rehman, Sadiq Ur Rehman, Haris Raheem

Published in: Wireless Personal Communications | Issue 4/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Wireless sensor networks (WSNs) consist of a large number of nodes, communicating sensor readings to the base stations through other nodes. Due to their energy limitations and positioning in hostile environments, WSNs are vulnerable to various routing attacks. From a security point of view in WSN, data authenticity, confidentiality, Integrity, and availability are the important security goals. It is in common practice that a security protocol used to be created by focusing a particular attack in WSN. Most renowned attacks in WSN are Sybil attack, Denial of Service attack, wormhole attack, selective attack, HELLO Flooding attack, Sinkhole attack etc. This survey focuses on one of the most challenging routing attacks, called Sinkhole attack. A Sinkhole attack is one of the sternest routing attacks because it attracts surrounding nodes with misleading routing path information and performs data forging or selective forwarding of data passing through it. It can cause an energy drain on surrounding nodes resulting in energy holes in WSNs and it can cause inappropriate and potentially dangerous responses based on false measurements. Researchers had presented several ways to detect and identify sinkhole attacks. This survey reviews related work on Sinkhole attack detection, prevention strategies, and attack techniques and also highlights open challenges in dealing with such attacks. Among many discussed techniques, fuzzy logic-based systems are considered to be good in performance in intruder detection system (IDS).
previous article PSOBLAP: Particle Swarm Optimization-Based Bandwidth and Link Availability Prediction Algorithm for Multipath Routing in Mobile Ad Hoc Networks
next article Integrated Blind Signal Separation and Neural Network Based Energy Detector Architecture

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Hidoussi, F., Toral-Cruz, H., Boubiche, D. E., Lakhtaria, K., Mihovska, A., & Voznak, M. (2015). Centralized IDS based on misuse detection for cluster-based wireless sensors networks. Wireless Personal Communications, 85(1), 207–224.CrossRef
2.
Bhushan, B., & Sahoo, G. (2018). Recent advances in attacks, technical challenges, vulnerabilities and their countermeasures in wireless sensor networks. Wireless Personal Communications, 98(2), 2037–2077.CrossRef
3.
4.
Dimitriou, T., & Krontiris, I. (2006). Secure in-network processing in sensor networks. In Y. Xiao (Ed.), Security in sensor networks (pp. 275–290, Chap. 11). Taylor and Francis Group, CRC press.
5.
Camtepe, S. A., & Yener, B. (2005). Key distribution mechanisms for wireless sensor networks: A survey. Rensselaer Polytechnic Institute, Troy, New York, Technical Report, 05-07.
6.
Lazos, L., & Poovendran, R. (2005). SeRLoc: Robust localization for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 1(1), 73–100.CrossRef
7.
Ngai, E. C. H., Liu, J., & Lyu M. R. (2006). On the intruder detection for sinkhole attack in wireless sensor networks. In IEEE international conference on communications, 2006. ICC’06 (Vol. 8, pp. 3383–3389). IEEE.
8.
Hu, Y. C., Perrig, A., & Johnson, D. B. (2003). Packet leashes: a defense against wormhole attacks in wireless networks. In INFOCOM 2003, twenty-second annual joint conference of the IEEE computer and communications. IEEE Societies (Vol. 3, pp. 1976–1986). IEEE.
9.
Yu, B., & Xiao, B. (2006). Detecting selective forwarding attacks in wireless sensor networks. In 20th international parallel and distributed processing symposium, 2006. IPDPS 2006 (pp. 8). IEEE.
10.
Othman, S. B., Bahattab, A. A., Trad, A., & Youssef, H. (2015). Confidentiality and integrity for data aggregation in WSN using homomorphic encryption. Wireless Personal Communications, 80(2), 867–889.CrossRef
11.
Gupta, D., Kaur, H., & Kumar, R. (2016). Detection of sink hole attack in wireless sensor network using advanced secure AODV routing protocol. International Journal of Computer Applications, 156(11), 1–5.CrossRef
12.
Sundararajan, R. K., & Arumugam, U. (2015). Intrusion detection algorithm for mitigating sinkhole attack on LEACH protocol in wireless sensor networks. Journal of Sensors, 2015, 203814.CrossRef
13.
Shafiei, H., Khonsari, A., Derakhshi, H., & Mousavi, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Journal of Computer and System Sciences, 80(3), 644–653.CrossRefMATH
14.
Sharmila, S., & Umamaheswari, G. (2011). Detection of sinkhole attack in wireless sensor networks using message digest algorithms. In 2011 international conference on process automation, control and computing (PACC) (pp. 1–6). IEEE.
15.
Sheela, D., Kumar, C. N., & Mahadevan, G. (2011). A non-cryptographic method of sinkhole attack detection in wireless sensor networks. In 2011 international conference on recent trends in information technology (ICRTIT) (pp. 527–532). IEEE.
16.
Chen, C., Song, M., & Hsieh, G. (2010). Intrusion detection of sinkhole attacks in large-scale wireless sensor networks. In 2010 IEEE international conference on wireless communications, networking and information security (WCNIS) (pp. 711–716). IEEE.
17.
Papadimitriou, A., Le Fessant, F., Viana, A. C., & Sengul, C. (2009). Cryptographic protocols to fight sinkhole attacks on tree-based routing in wireless sensor networks. In 5th IEEE workshop on secure network protocols, 2009. NPSec 2009 (pp. 43–48). IEEE.
18.
Choi, B. G., Cho, E. J., Kim, J. H., Hong, C. S., & Kim, J. H. (2009). A sinkhole attack detection mechanism for LQI based mesh routing in WSN. In International conference on information networking, 2009. ICOIN 2009 (pp. 1–5). IEEE.
19.
Krontiris, I., Giannetsos, T., & Dimitriou, T. (2008, October). Launching a sinkhole attack in wireless sensor networks; the intruder side. In IEEE international conference on wireless and mobile computing networking and communications, 2008. WIMOB’08 (pp. 526–531). IEEE.
20.
Coppolino, L., D’Antonio, S., Romano, L., & Spagnuolo, G. (2010). An intrusion detection system for critical information infrastructures using wireless sensor network technologies. In 2010 5th international conference on critical infrastructure (CRIS) (pp. 1–8). IEEE.
21.
Rassam, M. A., Zainal, A., Maarof, M. A., & Al-Shaboti, M. (2012). A sinkhole attack detection scheme in mint route wireless sensor networks. In 2012 international symposium on telecommunication technologies (ISTT) (pp. 71–75). IEEE.
22.
Hamedheidari, S., & Rafeh, R. (2013). A novel agent-based approach to detect sinkhole attacks in wireless sensor networks. Computers & Security, 37, 1–14.CrossRef
23.
Jahandoust, G., & Ghassemi, F. (2017). An adaptive sinkhole aware algorithm in wireless sensor networks. Ad Hoc Networks, 59, 24–34.CrossRef
24.
Kibirige, G. W., & Sanga, C. (2015). A survey on detection of sinkhole attack in wireless sensor network. arXiv preprint arXiv:​1505.​01941.
25.
Devibala, K., BalaMurali, D. S., Ayyasamy, D. A., & Archana, D. M. (2017). Flow-based mitigation model for sinkhole attack in wireless sensor networks using time-variant snapshot. International Journal of Advances in Computer and Electronics Engineering, 2(05), 14–21.
26.
Ahmed, N., Kanhere, S. S., & Jha, S. (2005). The holes problem in wireless sensor networks: A survey. ACM SIGMOBILE Mobile Computing and Communications Review, 9(2), 4–18.CrossRef
27.
Huang, C. F., & Tseng, Y. C. (2005). The coverage problem in a wireless sensor network. Mobile Networks and Applications, 10(4), 519–528.CrossRef
28.
Ma, W., Yan, F., Zuo, X., Ren, L., Xia, W., & Shen, L. (2017). Coverage hole detection algorithm without location information in wireless sensor networks. In 2017 3rd IEEE international conference on computer and communications (ICCC) (pp. 357–361). IEEE.
29.
Wood, A. D., Stankovic, J. A., & Son, S. H. (2003). JAM: A jammed-area mapping service for sensor networks. In 24th IEEE real-time systems symposium, 2003. RTSS 2003 (pp. 286–297). IEEE.
30.
Karmakar, S., & Roy, A. (2014). Holes detection in wireless sensor networks: A survey. International Journal of Modern Education and Computer Science, 6(4), 24.CrossRef
31.
Karlof, C., & Wagner, D. (2003). Secure routing in wireless sensor networks: Attacks and countermeasures. Ad Hoc Networks, 1(2–3), 293–315.CrossRef
32.
Chaturvedi, P., & Daniel, A. K. (2014). Recovery of holes problem in wireless sensor networks. In 2014 international conference on information communication and embedded systems (ICICES) (pp. 1–6). IEEE.
33.
Mohemed, R. E., Saleh, A. I., Abdelrazzak, M., & Samra, A. S. (2017). Energy-efficient routing protocols for solving energy hole problem in wireless sensor networks. Computer Networks, 114, 51–66.CrossRef
34.
Shahabi, S., Ghazvini, M., & Bakhtiarian, M. (2016). A modified algorithm to improve the security and performance of AODV protocol against black hole attack. Wireless Networks, 22(5), 1505–1511.CrossRef
35.
Oriyano, S. P. (2016). Denial of service. CEH™ v9: Certified ethical hacker version 9 study guide (pp. 305–329). New York: Wiley.CrossRef
36.
Zhu, S., Setia, S., & Jajodia, S. (2006). LEAP + : Efficient security mechanisms for large-scale distributed sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(4), 500–528.CrossRef
37.
Krontiris, I., Dimitriou, T., Giannetsos, T., & Mpasoukos, M. (2007). Intrusion detection of sinkhole attacks in wireless sensor networks. In International symposium on algorithms and experiments for sensor systems, wireless networks and distributed robotics (pp. 150–161). Springer, Berlin, Heidelberg.
38.
Sterne, D., Balasubramanyam, P., Carman, D., Wilson, B., Talpade, R., Ko, C., & Bowen, T. (2005). A general cooperative intrusion detection architecture for MANETs. In Proceedings of the third IEEE international workshop on information assurance, 2005 (pp. 57–70). IEEE.
39.
Vuppala, S., Banerjee, A., Pal, A., & Choudhury, P. (2011). An attack identification scheme using hierarchical data clustering in MANET. In International conference on computer engineering and technology, 3rd (ICCET 2011). ASME Press.
40.
Abraham, A., Grosan, C., & Martin-Vide, C. (2007). Evolutionary design of intrusion detection programs. IJ Network Security, 4(3), 328–339.
41.
Sun, B., Wu, K., & Pooch, U. W. (2003). Zone-based intrusion detection for mobile ad hoc networks. International Journal of Ad Hoc and Sensor Wireless Networks, 2(3), 2003–2009.
42.
Banerjee, S., Grosan, C., & Abraham, A. (2005). IDEAS: intrusion detection based on emotional ants for sensors. In Proceedings of the 5th international conference on intelligent systems design and applications, 2005. ISDA’05 (pp. 344–349). IEEE.
43.
Patcha, A., & Park, J. M. (2006). A game theoretic formulation for intrusion detection in mobile Ad Hoc networks. IJ Network Security, 2(2), 131–137.
44.
Marti, S., Giuli, T. J., Lai, K., & Baker, M. (2000). Mitigating routing misbehavior in mobile ad hoc networks. In Proceedings of the 6th annual international conference on mobile computing and networking (pp. 255–265). ACM.
45.
Mukkamala, S., Sung, A. H., & Abraham, A. (2005). Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications, 28(2), 167–182.CrossRef
46.
Li, Y., & Parker, L. E. (2008). Intruder detection using a wireless sensor network with an intelligent mobile robot response. In Southeastcon, 2008. IEEE (pp. 37–42). IEEE.
47.
Soni, V., Modi, P., & Chaudhri, V. (2013). Detecting sinkhole attack in a wireless sensor network. International Journal of Application or Innovation in Engineering & Management, 2(2), 29–32.
48.
Diallo, C., Sawaré, A., & Sow, M. T. (2017). Security issues and solutions in wireless sensor networks. International Journal of Computer Science and Information Security, 15(3), 6.
49.
Kaur, M., & Singh, A. (2016, September). Detection and Mitigation of Sinkhole Attack in Wireless Sensor Network. In 2016 International conference on micro-electronics and telecommunication engineering (ICMETE) (pp. 217–221). IEEE.
50.
Sharma, P., & Singh, A. (2017). Probing and removal of denial of service attack in wireless sensor networks. Imperial Journal of Interdisciplinary Research, 3(2), 975–978.
51.
Dalal, S., & Devi, S. (2016). Survey on attacks pertaining to wireless mesh networks and approach towards counter measures. International Journal of Computer Applications, 149(12), 20–26.CrossRef
52.
Singh, A., & Singh, T. (2016). Review on detection and prevention of sink hole attack in network. Global Journal of Computers & Technology, 5(2), 289–292.
53.
Deng, J., Han, R., & Mishra, S. (2006). INSENS: Intrusion-tolerant routing for wireless sensor networks. Computer Communications, 29(2), 216–230.CrossRef
54.
Misra, S., Bhattarai, K., & Xue, G. (2011, June). BAMBi: Blackhole attacks mitigation with multiple base stations in wireless sensor networks. In 2011 IEEE international conference on communications (ICC) (pp. 1–5). IEEE.
55.
56.
Roy, S. D., Singh, S. A., Choudhury, S., & Debnath, N. C. (2008). Countering sinkhole and black hole attacks on sensor networks using dynamic trust management. In IEEE symposium on computers and communications, 2008. ISCC 2008 (pp. 537–542). IEEE.
57.
Kalnoor, G., Agarkhed, J., & Patil, S. R. (2017). Agent-based QoS routing for intrusion detection of sinkhole attack in clustered wireless sensor networks. In Proceedings of the first international conference on computational intelligence and informatics (pp. 571–583). Springer, Singapore.
Metadata
Title
Sinkhole Attacks in Wireless Sensor Networks: A Survey
Authors
Aqeel-ur Rehman
Sadiq Ur Rehman
Haris Raheem
Publication date
24-10-2018
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 4/2019
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-6040-7

Other articles of this Issue 4/2019

Wireless Personal Communications 4/2019 Go to the issue

OriginalPaper

Concatenated LDPC-TCM Codes for Better Performance of OFDM-FSO System Using Gamma–Gamma Fading Model

OriginalPaper

Fast-Fading Channel Environment Estimation Using Linear Minimum Mean Squares Error-Support Vector Regression

OriginalPaper

Hybrid Anomaly Detection by Using Clustering for Wireless Sensor Network

OriginalPaper

UAV Based Data Gathering in Wireless Sensor Networks

BriefCommunication

Performance Analysis of MultiACK-SFAMA for Underwater Acoustic Networks

OriginalPaper

Monitoring, Control and Energy Management of Smart Grid System via WSN Technology Through SCADA Applications