Abstract
With the acceleration of the Internet of things (IoT) construction, the security and energy consumption of IoT will become an import factor restricting the overall development of the IoT. In order to reduce the energy consumption of the IoT heterogeneous perceptual network in the attack-defense process, the placement strategy of the intrusion detection system (IDS) described in this paper is to place the IDS on the cluster head nodes selected by the clustering algorithm called ULEACH, which we have proposed in this paper. By optimizing the calculation of the node threshold, the ULEACH clustering algorithm will comprehensively consider the heterogeneity of the perceptual nodes and take the residual energy, energy consumption rate, and overall performance of the nodes into account. As a result, the strategy improves the utilization of the nodes to enhance the performance of heterogeneous perceptual network and extend the lifetime of the system. Furthermore, the paper proposes a intrusion detection system framework and establishes dynamic intrusion detection model for IoT heterogeneous perceptual network based on game theory, by applying modified particle swarm optimization, the optimal defense strategy that could balance the detection efficiency and energy consumption of the system is obtained. Finally, the experiment results show that proposed strategy not only effectively detects multiple network attacks, but also reduces energy consumption.









Similar content being viewed by others
References
Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422
Al-Karaki J, Kamal A (2017) Routing techniques in wsn: a survey. IEEE J Wirel Commun 11:6–28
Anirudha A, Mangesh G, Ritesh V (2017) A survey on data mining approaches for network intrusion detection system. Int J Comput Appl 159(1):20–23
Castiglione A, Palmieri F, Fiore U; (2015) Modeling energy-efficient secure communications in multi-mode wireless mobile devices. Comput Syst Sci 81:1464–1478
Asaad M, Ahmad F, Alam MS, Rafat Y (2018) Iot enabled monitoring of an optimized electric vehicle’s battery system. Mobile Netw Appl 23(4):994–1005
Bhunia S (2018) Internet of things security: Are we paranoid enough. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), IEEE, pp 1–1
Caviglione L, Merlo A (2012) The energy impact of security mechanisms in modern mobile devices. Netw Secur 2012(2): 11–14
Chen Y, Wen X, Lu Z, Shao H (2017) Energy efficient clustering and beamforming for cloud radio access networks. Mobile Netw Appl 22(3):589–601
Wong C-M, Chang C-F, Lee B-H. (2013) A simple time shift scheme for beacon broadcasting based on cluster-tree ieee 802.15.4 low-rate wpans. Wirel Pers Commun 72(4):2837–2848
Curti M, Merlo A, Migliardi M, Schiappacasse S (2013) Towards energy-aware intrusion detection systems on mobile devices. In: 2013 International Conference on High Performance Computing Simulation (HPCS), pp 289–296
Hajisalem V, Babaie S (2018) A hybrid intrusion detection system based on abc-afs algorithm for misuse and anomaly detection. Comput Netw 136:37–50
Han L, Zhou M, Jia W, Dalil Z, Xu X (2018) Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model. Information Sciences
Heinzelman WR, Chandrakasan ABh (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on IEEE, vol 2, p 10
Henningsen S, Dietzel S, Scheuermann B (2018) Misbehavior detection in industrial wireless networks: Challenges and directions. Mobile Netw Appl 23(5):1330–1336
Hong S, Kim Y (2017) Developing usable interface for internet of things security analysis software. In: Tryfonas T (ed) Human Aspects of Information Security, Privacy and Trust. Springer International Publishing, Cham, pp 322–328
Hossein J (2011) Designing an agent-based intrusion detection system for heterogeneous wireless sensor networks: robust, fault tolerant and dynamic reconfigurable. Int J Communications, Network and System Sciences 4:523–543
Hwang K, Cai M, Qin YCM (2007) Hybrid intrusion detection with weighted signature generation over anomalous internet episodes. IEEE Transactions on Dependable and Secure Computing, pp 41–55
Kim D, Shin D, Shin D, Kim YH (2018) Attack detection application with attack tree for mobile system using log analysis. Mobile Networks and Applications
Luo J, Wu D (2015) Optimal energy strategy for node selection and data relay in wsn-based iot. Mobile Netw Appl 20(2): 169–180
Mbanaso UM, Chukwudebe GA, Adebisi B (2017) Holistic security architecture for iot technologies. In: 2017 13th International Conference on Electronics, Computer and Computation (ICECCO), pp. 11–16
Merlo A, Migliardi M, Caviglione L (2015) A survey on energy-aware security mechanisms. Pervasive Mob Comput 24:77–90. special Issue on Secure Ubiquitous Computing
Michael Quick, T.R.D.: The deter project. https://www.isi.deterlab.net/index.php3
Morais A, Cavalli A (2014) A distributed and collaborative intrusion detection architecture for wireless mesh networks. Mobile Netw Appl 19(1):101–120
Murali A, Rao M (2005) A survey on intrusion detection approaches. In: International Conference on Information and Communication Technologies, pp 233–240
Ozger M, Cetinkaya O, Akan OB (2018) Energy harvesting cognitive radio networking for iot-enabled smart grid. Mobile Netw Appl 23(4):956–966
Sadek RA (2018) Hybrid energy aware clustered protocol for iot heterogeneous network. Future Computing and Informatics Journal
Sahoo KC, Pati UC (2017) Iot based intrusion detection system using pir sensor. In: 2017 2Nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), IEEE, pp 1641–1645
Sedjelmaci H, Senouci SM, Taleb T (2017) An accurate security game for low-resource iot devices. IEEE Trans Veh Technol 66(10):9381–9393
Sedjelmaci H, Senouci SM, Feham M (2011) New framework for a hierarchical intrusion detection mechanism in cluster-based wireless sensor networks. Security & Communication Networks
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE Conference on Evolutionary Computation, pp 69–73
Totel E, Hkimi M, Hurfin M, Leslous M, Labiche Y (2016) Inferring a distributed application behavior model for anomaly based intrusion detection. In: 2016 12Th European Dependable Computing Conference (EDCC), IEEE, pp 53–64
Vorakulpipat C, Rattanalerdnusorn E, Thaenkaew P, Hai HD (2018) Recent challenges, trends, and concerns related to iot security: an evolutionary study. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp 1–1
Younis O, Fahmy S (2004) Heed: A hybrid, energy-efficient, distributed clustering approach for ad-hoc sensor networks. IEEE Transactions on Mobile Computing 3 (08
Zhang XF (2003) A survey on the development of intrusion detection system. Computer Science
Acknowledgment
This paper is supported by National Natural Science Fund NSF: 61272033 & 61572222.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, M., Han, L., Lu, H. et al. Intrusion Detection System for IoT Heterogeneous Perceptual Network. Mobile Netw Appl 26, 1461–1474 (2021). https://doi.org/10.1007/s11036-019-01483-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-019-01483-5