ENERGY EFFICIENT INTRUSION DETECTION SYSTEM IN WIRELESS SENSOR NETWORKS
Keywords:
EEIDS, STR Protocol, DDoS Attacks, Wormhole AttackAbstract
The extensive use of ZigBee standards in Internet of Things (IoT) networks and wireless sensor networks (WSN) has led to questions about how well-equipped current security measures are to fend off emerging dangers like wormhole attacks and Distributed Denial of Service (DDoS) attacks. While ZigBee was designed with cost-effectiveness, security, and network resilience in mind, modern security techniques frequently fall short of offering all-encompassing protection while placing a substantial burden on energy, memory, and computation. The security of ZigBee-based WSNs against wormhole and DDoS assaults is strengthened in this work by the unique method we provide. The Energy Efficient Intrusion Detection System (EE-IDS) and the Energy Efficient Trust System (EE-TS) are the two essential parts of our system. Together, these elements support effective threat detection and mitigation while using the fewest resources possible. We compare the performance of our suggested method to three other routing protocols in order to determine how effective it is: Ad hoc On-Demand Distance Vector (AODV), Shortcut Tree Routing (STR), and Opportunistic Shortcut Tree Routing (OSTR). We get a deeper grasp of the EE-IDS and EE-TS' capabilities under various network dynamics and traffic patterns by deploying them in these scenarios. The proposed Energy Efficient Trust System for Wormhole detection (EE-TSW) and the Energy Efficient Trust System (EE-TS) for DDoS attack detection are thoroughly evaluated through extensive simulations utilising the NS2 (Network Simulator 2) platform. According to the findings of our simulations, the EE-IDS, in particular the versions EE-IDS-AODV, EE-IDS-STR, and EE-IDS-OSTR, consistently beat the EE-TSW in wormhole attack detection. Moreover, our Energy Efficient Intrusion Detection System with Energy Prediction (EE-IDSEP) demonstrates superior performance in detecting DDoS assaults compared to the current EE-TS, as evidenced by key performance metrics including Packet Delivery Ratio (PDR), Average End-to-End Delay, energy consumption, detection rate, average detection time, and False Positive Rate (FPR).
References
X. Du and H.-H. Chen. “Security in wireless sensor networks”, IEEE Wireless Communications, Vol.15, No. 4, pp. 60-66, 2008.
I. Butun, S. D. Morgera, and R. Sankar, “A Survey of Intrusion Detection Systems in Wireless Sensor Networks”, IEEE Communications Surveys & Tutorials, Vol.16, No. 1, pp.266-282, first quarter 2014.
J. Amudhavel et al, “A Survey on Intrusion Detection System: State of the Art Review”, Indian Journal of Science and Technology, Vol 9, issue 11, 2016.
Y. Cho, G. Qu, Y. Wu, “Insider Threats against Trust Mechanism with Watchdog and Defending Approaches in Wireless Sensor Networks”, IEEE Computer Society on Security and Privacy Workshops, pp.134-141, 2012.
A. Forootaninial and M.B. Ghaznavi- Ghoushchi, “An Improved Watchdog Technique Based On Power-Aware Hierarchical Design For Ids In Wireless Sensor Networks”, International Journal of Network Security & Its Applications, Vol.4, No.4, pp.161-178, 2012.
Y. C. Hu, Perrig A, and Johnson B. “Packet leashes: a defense against wormhole attacks in wireless networks”, In: Proc. of INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, Vol.3, pp.1976 – 1986, 2003.
B. M. David, B. Santana, L. Peotta, M. D. Holtz, and R. T. Sousa Jr, “A Context- Dependent Trust Model for the MAC Layer in LR-WPANs”, International Journal on Computer Science and Engineering, Vol.2, No.9, pp. 3007-3016, 2010.
G. Jegan and P. Samundiswary “Wormhole Attack Detection in Zigbee Wireless Sensor Networks using Intrusion Detection System” Indian Journal of Science and Technology, Vol.9, No. 45, pp. 1-10, 2016.
Bhatia, A., & Hansdah, R. (2016). TRM-MAC: A TDMA-based reliable multicast MAC protocol for WSNs with flexibility to trade-off between latency and reliability. Computer Networks, 104, 79–93.
Thalore, R., Sharma, J., Khurana, M., & Jha, M. (2013). QoS evaluation of energy-efficient ML-MAC protocol for wireless sensor networks. AEU—International Journal of Electronics and Communica- tions, 67(12), 1048–1053.
Mouradian, A., Auge-Blum, I., & Valois, F. (2014). RTXP: A localized real-time MAC-routing proto- col for wireless sensor networks. Computer Networks, 67, 43–59.
Masdari, M., Bazarchi, S., & Bidaki, M. (2013). Analysis of secure LEACH-based clustering protocols in wireless sensor networks. Journal of Network and Computer Applications, 36(4), 1243–1260.
Alkhatib, A. A. A., & G. S. Baicher. (2012). Wireless sensor network architecture. In International conference on computer networks and communication systems (ICNCS 2012) (Vol. 35).
Udhayavani, M., & Chandrasekaran, M. (2018). Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: A trust-aware routing framework for wireless sensor net- works. Cluster Computing, 22, 11919.
Ahmed, A., Bakar, K., Channa, M., & Khan, A. (2016). A secure routing protocol with trust and energy awareness for wireless sensor network. Mobile Networks and Applications, 21(2), 272–285.
Mehetre, D., Roslin, S., & Wagh, S. (2018). Detection and prevention of black hole and selective for- warding attack in clustered WSN with Active Trust. Cluster Computing, 22, 1313.
AlFarraj, O., AlZubi, A., & Tolba, A. (2018) .Trust-based neighbor selection using activation func- tion for secure routing in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing.
Deepa, C. & Latha, B. (2017). HHSRP: A cluster based hybrid hierarchical secure routing protocol for wireless sensor networks. Cluster Computing.
Zahedi, A., & Parma, F. (2018). An energy-aware trust-based routing algorithm using gravitational search approach in wireless sensor networks. Peer-to-
Peer Networking and Applications, 12, 167.
Mohajerani, A., & Gharavian, D. (2015). An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks. Wireless Networks, 22(8), 2637–2647.
Prabha, V. R., & Latha, P. (2017). Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks. Sadhana, 42(2), 143–151.