International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Improving Sinkhole Attack Detection Rate through Knowledge-Based Specification Rule for a Sinkhole Attack Intrusion Detection Technique of IoT


Affiliations
1 Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea, Republic of
2 Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Korea, Republic of
 

The Internet of Things (IoT) is a technology that enables various IoT devices to collect data through sensors or sensor networks and to allow devices to share the collected data in an internet environment. Therefore, most communication is made wirelessly, and it is very vulnerable to a blackhole, selective forwarding, and sinkhole attacks that can occur in the network. One of the destructive attacks is the sinkhole attack, which compromises the integrity and reliability of data in a network. In general, the sinkhole attack detection method used by ad hoc networks and WSNs is less effective than the method used for IoT because of environmental differences. Therefore, the Intrusion detection of SiNkhole attack on 6LoWPAN for InterneT of Things (INTI) method can detect sinkhole attacks occurring in IoT. In this study, rules are defined using a specification-based approach of intrusion detection technology based on the number of input/output transmissions collected in the monitoring phase of INTI. Knowledge base rules were defined to thresholds of normal operation, and different rules were defined according to the role each node plays in improving sinkhole attack detection rates.

Keywords

Wireless Sensor Network, Internet of Things, Sinkhole, Intrusion Detection, Artificial Intelligence, Rule Based System, Forward Chaining.
User
Notifications
Font Size

  • Atzori, Luigi, Antonio Iera, and Giacomo Morabito. "The internet of things: A survey." Computer networks 54.15 (2010): 2787-2805

  • Hassan, Basma Mostafa. Monitoring the Internet of Things (IoT) Networks. Diss. Université Montpellier; Ǧāmiʿat ͏̈ al-Qāhirat ͏̈, 2019.

  • Alaba, Fadele Ayotunde, et al. "Internet of Things security: A survey." Journal of Network and Computer Applications 88 (2017): 10-28.

  • Mendez, Diego M., Ioannis Papapanagiotou, and Baijian Yang. "Internet of things: Survey on security and privacy." arXiv preprint arXiv:1707.01879 (2017).

  • Deogirikar, Jyoti, and Amarsinh Vidhate. "Security attacks in IoT: A survey." 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, 2017.

  • Choudhary, Sarika and Nishtha Kesswani. "A Survey: Intrusion Detection Techniques for Internet of Things." IJISP vol.13, no.1 2019: pp.86-105. http://doi.org/10.4018/IJISP.2019010107

  • Ahmed, Hassan I., et al. "A survey of IoT security threats and defenses." International Journal of Advanced Computer Research 9.45 (2019): 325-350.

  • Dvir, Amit, and Levente Buttyan. "VeRA-version number and rank authentication in RPL." 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems. IEEE, 2011.

  • Raza, Shahid, Linus Wallgren, and Thiemo Voigt. "SVELTE: Real-time intrusion detection in the Internet of Things." Ad hoc networks 11.8 (2013): 2661-2674.

  • Zaminkar, Mina, and Reza Fotohi. "SoS-RPL: securing internet of things against sinkhole attack using RPL protocol-based node rating and ranking mechanism." arXiv preprint arXiv:2005.09140 (2020).

  • Cervantes, Christian, et al. "Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things." 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). IEEE, 2015.

  • Karlof, Chris, and David Wagner. "Secure routing in wireless sensor networks: Attacks and countermeasures." Ad hoc networks 1.2-3 (2003): 293-315.

  • Wallgren, Linus, Shahid Raza, and Thiemo Voigt. "Routing attacks and countermeasures in the RPL-based internet of things." International Journal of Distributed Sensor Networks 9.8 (2013): 794326.

  • Chawla, Shiven. Deep learning based intrusion detection system for the Internet of Things. University of Washington, 2017.

  • S., Gayathri K. and Tony Thomas. "Intrusion Detection Systems for Internet of Things." Handbook of Research on Intrusion Detection Systems, edited by Brij B. Gupta and Srivathsan Srinivasagopalan, IGI Global, 2020, pp. 148-171. http://doi:10.4018/978-1-7998-2242-4.ch008

  • Bace, Rebecca Gurley, and Peter Mell. "Intrusion detection systems." (2001): 201.

  • Smys, S., Abul Basar, and Haoxiang Wang. "Hybrid intrusion detection system for internet of things (IoT)." Journal of ISMAC 2.04 (2020): 190-199.

  • Zarpelão, Bruno Bogaz, et al. "A survey of intrusion detection in Internet of Things." Journal of Network and Computer Applications 84 (2017): 25-37.

  • Faraj, Omair, et al. "Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things." Proceedings of the 15th International Conference on Availability, Reliability, and Security. 2020.

  • Al-Ajlan, Ajlan. "The comparison between forward and backward chaining." International Journal of Machine Learning and Computing 5.2 (2015): 106.

  • Mzori, Bareen Haval Sadiq. Forward and Backward Chaining Techniques of Reasoning in Rule-Based Systems. MS thesis. Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ), 2015.

  • Lodder, Arno R., and John Zeleznikow. "Artificial intelligence and online dispute resolution." Online Dispute Resolution: Theory and Practice A Treatise on Technology and Dispute Resolution (2012): 73-94.

  • Winston, Patrick Henry, and Richard Henry Brown. "Artificial intelligence: an MIT perspective." Cambridge, Mass (1979): 1.

  • B. George and S. S. Mathai, "Improving quality of interference in multilevel secure knowledge-based systems," 2004 International Conference on Machine Learning and Applications, 2004. Proceedings., 2004, pp. 477-484, doi: 10.1109/ICMLA.2004.1383553


Abstract Views: 332

PDF Views: 2




  • Improving Sinkhole Attack Detection Rate through Knowledge-Based Specification Rule for a Sinkhole Attack Intrusion Detection Technique of IoT

Abstract Views: 332  |  PDF Views: 2

Authors

Ga Hyeon An
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea, Republic of
Tae Ho Cho
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

Abstract


The Internet of Things (IoT) is a technology that enables various IoT devices to collect data through sensors or sensor networks and to allow devices to share the collected data in an internet environment. Therefore, most communication is made wirelessly, and it is very vulnerable to a blackhole, selective forwarding, and sinkhole attacks that can occur in the network. One of the destructive attacks is the sinkhole attack, which compromises the integrity and reliability of data in a network. In general, the sinkhole attack detection method used by ad hoc networks and WSNs is less effective than the method used for IoT because of environmental differences. Therefore, the Intrusion detection of SiNkhole attack on 6LoWPAN for InterneT of Things (INTI) method can detect sinkhole attacks occurring in IoT. In this study, rules are defined using a specification-based approach of intrusion detection technology based on the number of input/output transmissions collected in the monitoring phase of INTI. Knowledge base rules were defined to thresholds of normal operation, and different rules were defined according to the role each node plays in improving sinkhole attack detection rates.

Keywords


Wireless Sensor Network, Internet of Things, Sinkhole, Intrusion Detection, Artificial Intelligence, Rule Based System, Forward Chaining.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F212333