A Wormhole Attack Detection and Prevention Technique in Wireless Sensor Networks

Article


Siddiqui, A., Karami, A. and Johnson, M. O. 2017. A Wormhole Attack Detection and Prevention Technique in Wireless Sensor Networks. International Journal of Computer Applications. 174 (Art. 4). https://doi.org/10.5120/ijca2017915376
AuthorsSiddiqui, A., Karami, A. and Johnson, M. O.
Abstract

Security is one of the major and important issues surrounding network sensors because of its inherent liabilities, i.e. physical size. Since network sensors have no routers, all nodes involved in the network must share the same routing protocol to assist each other for the transmission of packets. Also, its unguided nature in dynamic topology makes it vulnerable to all kinds of security attack, thereby posing a degree of security challenges. Wormhole is a prominent example of attacks that poses the greatest threat because of its difficulty in detecting and preventing. In this paper, we proposed a wormhole attach detection and prevention mechanism incorporated AODV routing protocol, using neighbour discovery and path verification mechanism. As compared to some preexisting methods, the proposed approach is effective and promising based on applied performance metrics.

JournalInternational Journal of Computer Applications
Journal citation174 (Art. 4)
ISSN0975-8887
Year2017
PublisherFoundation of Computer Science
Accepted author manuscript
License
Digital Object Identifier (DOI)https://doi.org/10.5120/ijca2017915376
Web address (URL)https://doi.org/10.5120/ijca2017915376
Publication dates
Online05 Sep 2017
Publication process dates
Accepted15 Aug 2017
Deposited04 Jul 2019
Copyright holder© 2017 International Journal of Computer Applications
Copyright informationThis is an accepted manuscript of an article published in International Journal of Computer Applications, Volume 174 - No. 4, 2017. https://doi.org/10.5120/ijca2017915376.
Permalink -

https://repository.uel.ac.uk/item/86v26

Download files


Accepted author manuscript
ijca-915376- Arish.pdf
License: All rights reserved

  • 441
    total views
  • 475
    total downloads
  • 2
    views this month
  • 0
    downloads this month

Export as

Related outputs

Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data
Amirhosseini, M. H., Ayodele, A. L. and Karami, A. 2024. Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data. IS'24: 12th IEEE International Conference on Intelligent Systems. Varna, Bulgaria 29 - 31 Aug 2024 IEEE.
Application of Blockchain Based e-Procurement Solution for Mitigating Corruption in Smart Cities Using Digital Identities
Siddiqui, A., Tansen, K. and Abdalla, H. 2023. Application of Blockchain Based e-Procurement Solution for Mitigating Corruption in Smart Cities Using Digital Identities. IFIPIoT 2023: 6th IFIP International Internet of Things Conference. Dallas-Fort Worth Metroplex, Texas (USA) 02 - 03 Nov 2023 Springer. https://doi.org/10.1007/978-3-031-45882-8_7
Blockchain Based Framework for Enhancing Cybersecurity and Privacy in Procurement
Siddiqui, A., Tansen, K. and Abdalla, H. 2023. Blockchain Based Framework for Enhancing Cybersecurity and Privacy in Procurement. IFIPIoT 2023: 6th IFIP International Internet of Things Conference. Dallas-Fort Worth Metroplex, Texas (USA) 02 - 03 Nov 2023 Springer. https://doi.org/10.1007/978-3-031-45882-8_8
Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark
Chaudhury, M., Karami, A. and Ghazanfar, M. A. 2022. Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark. Electronics. 11 (16), p. 2567. https://doi.org/10.3390/electronics11162567
Designing a Cost-Efficient Network for a Small Enterprise
Jafari, F., Karami, A. and Osemwengie, L. 2021. Designing a Cost-Efficient Network for a Small Enterprise. SAI Computing Conference 2021. Online 15 - 16 Jul 2021 Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_14
Stock market prediction using machine learning classifiers and social media, news
Khan, W., Ghazanfar, M., Azam, M. A., Karami, A., Alyoubi, K. H. and Alfakeeh, A. S. 2020. Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing. 13, pp. 3433-3456. https://doi.org/10.1007/s12652-020-01839-w
A novel centroids initialisation for K-means clustering in the presence of benign outliers
Karami, A., Ur Rehman, S. and Ghazanfar, M. 2020. A novel centroids initialisation for K-means clustering in the presence of benign outliers. International Journal of Data Analysis Techniques and Strategies. 12 (4), pp. 287-298. https://doi.org/10.1504/IJDATS.2020.111498
Improving Student Engagement and Performance in Computing Final Year Projects
Naeem, U., Islam, S. and Siddiqui, A. 2019. Improving Student Engagement and Performance in Computing Final Year Projects. IEEE TALE 2019. Yogyakarta - Indonesia 10 - 13 Oct 2019 IEEE. https://doi.org/10.1109/TALE48000.2019.9225860
An Effective Framework for Enhancing Student Engagement and Performance in Final Year Projects
Siddiqui, A., Naeem, U. and Islam, S. 2019. An Effective Framework for Enhancing Student Engagement and Performance in Final Year Projects. 2019 IEEE Global Engineering Education Conference (EDUCON). Dubai, United Arab Emirates, United Arab Emirates 08 - 11 Apr 2019 IEEE. pp. 401-410 https://doi.org/10.1109/EDUCON.2019.8725253
An Anomaly-based Intrusion Detection System in Presence of Benign Outliers with Visualization Capabilities
Karami, A. 2018. An Anomaly-based Intrusion Detection System in Presence of Benign Outliers with Visualization Capabilities. Expert Systems with Applications. 108, pp. 36-60. https://doi.org/10.1016/j.eswa.2018.04.038
Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network
Sharif, M., Naeem, U., Islam, S. and Karami, A. 2018. Functional Connectivity Evaluation for Infant EEG Signals based on Artificial Neural Network. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_34
The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain
Okoye, Kingsley, Islam, S., Naeem, U., Sharif, M., Azam, Muhammad Awais and Karami, A. 2018. The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain. Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Intelligent Systems Conference (IntelliSys) 2018. London, UK 06 - 07 Sep 2018 Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_96
A Framework for Uncertainty-Aware Visual Analytics in Big Data
Karami, A. 2015. A Framework for Uncertainty-Aware Visual Analytics in Big Data. CEUR Workshop Proceedings. 1510, pp. 146-155.
Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options
Karami, A. and Johansson, Ronnie 2013. Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options. Journal of Information Science and Engineering. 30 (2), pp. 519-534.
Choosing DBSCAN parameters automatically using differential evolution
Karami, A. and Johansson, Ronnie 2014. Choosing DBSCAN parameters automatically using differential evolution. International Journal of Computer Applications. 91 (7), pp. 1-11. https://doi.org/10.5120/15890-5059
A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks
Karami, A. and Guerrero-Zapata, Manel 2014. A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. Neurocomputing. 149 (Part C), pp. 1253-1269. https://doi.org/10.1016/j.neucom.2014.08.070
A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking
Karami, A. and Guerrero-Zapata, Manel 2014. A hybrid multiobjective RBF-PSO method for mitigating DoS attacks in Named Data Networking. Neurocomputing. 151 (3), pp. 1262-1282. https://doi.org/10.1016/j.neucom.2014.11.003
An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking
Karami, A. and Guerrero-Zapata, Manel 2015. An ANFIS-based cache replacement method for mitigating cache pollution attacks in Named Data Networking. Computer Networks. 80 (April), pp. 51-65. https://doi.org/10.1016/j.comnet.2015.01.020
ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network
Karami, A. 2015. ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network. Journal of Network and Computer Applications. 56 (Oct.), pp. 1-18. https://doi.org/10.1016/j.jnca.2015.05.017