CoChat
Sign Up
Cited by 202Security in Wireless Sensor NetworksNetwork Security and Intrusion DetectionEnergy Efficient Wireless Sensor Networks

Intrusion Detection Protocols in Wireless Sensor Networks Integrated to Internet of Things Deployment: Survey and Future Challenges

Open access
Pundir, Sumit, Wazid, Mohammad, Singh, Devesh Pratap, Das, Ashok Kumar, Rodrigues, Joel J. P. C., Park, Youngho·2020·IEEE Access
DOI 10.1109/access.2019.2962829
Open this paper in CoChat — free accountOpen PDF

Abstract

As we all know that the technology is projected to be next to humans very soon because of its holistic growth. Now-a-days, we see a lot of applications that are making our lives comfortable such as smart cars, smart homes, smart traffic management, smart offices, smart medical consultation, smart cities, etc. All such facilities are in the reach of a common man because of the advancement in Information and Communications Technology (ICT). Because of this advancement, new computing and communication environment such as Internet of Things (IoT) came into picture. Lot of research work is in progress in IoT domain which helps for the overall development of the society and makes the lives easy and comfortable. But in the resource constrained environment of Wireless Sensor Network (WSN) and IoT, it is almost inconceivable to establish a fully secure system. As we are moving forward very fast, technology is becoming more and more vulnerable to the security threats. In future, the number of Internet connected people will be less than the smart objects so we need to prepare a robust system for keeping the above mentioned environments safe and standardized it for the smooth conduction of communication among IoT objects. In this survey paper, we provide the details of threat model applicable for the security of WSN and IoT based communications. We also discuss the security requirements and various attacks possible in WSN and IoT based communication environments. The emerging projects of WSNs integrated to IoT are also briefed. We then provide the details of different architectures of WSN and IoT based communication environments. Next, we discuss the current issues and challenges related to WSN and IoT. We also provide a critical literature survey of recent intrusion detection protocols for IoT and WSN environments along with their comparative analysis. A taxonomy of security and privacy-preservation protocols in WSN and IoT is also highlighted. Finally, we discuss some research challenges which need to be addressed in the coming future.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/access.2019.2962829

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The complexity of the BLE specification is high, which increases the difficulty of cracking the temporary key (TK).

    “The complexity of the specification is high [58], which increases the difficulty of cracking the temporary key (TK).”

    From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [58]

    The Key Management section discusses cryptographic keys in WSN and IoT systems, while the Classification of Intrusion Detection Systems section discusses manually developed specifications. Neither addresses the BLE specification, a temporary key (TK), or whether specification complexity makes that key harder to crack (searched: BLE pairing, temporary key, cracking, and specification complexity). No page numbers were available for these passages. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 108 · Cited by 202

Research works printed but not linked: 103

  1. M. Ammar, G. Russello, and B. Crispo, ''Internet of Things: A survey on the security of IoT frameworks,'' J. Inf. Secur. Appl., vol. 38, pp. 8-27, Feb. 2018.
  2. A. Raoof, A. Matrawy, and C.-H. Lung, ''Routing attacks and mitigation methods for RPL-based Internet of Things,'' IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1582-1606, 2019.
  3. M. Wazid, A. K. Das, V. Odelu, N. Kumar, and W. Susilo, ''Secure remote user authenticated key establishment protocol for smart home environment,'' IEEE Trans. Depend. Sec. Comput., to be published.
  4. S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, ''The Internet of Things for health care: A comprehensive survey,'' IEEE Access, vol. 3, pp. 678-708, 2015.
  5. M. Wazid, A. K. Das, N. Kumar, M. Conti, and A. V. Vasilakos, ''A novel authentication and key agreement scheme for implantable med- ical devices deployment,'' IEEE J. Biomed. Health Inform., vol. 22, no. 4, pp. 1299-1309, Jul. 2018.
  6. M. Wazid, A. K. Das, and A. V. Vasilakos, ''Authenticated key manage- ment protocol for cloud-assisted body area sensor networks,'' J. Netw. Comput. Appl., vol. 123, pp. 112-126, Dec. 2018.
  7. S. Challa, M. Wazid, A. K. Das, and M. K. Khan, ''Authentication protocols for implantable medical devices: taxonomy, analysis and future directions,'' IEEE Consum. Electron. Mag., vol. 7, no. 1, pp. 57-65, Jan. 2018.
  8. M. Wazid, P. Bagga, A. K. Das, S. Shetty, J. J. P. C. Rodrigues, and Y. Park, ''AKM-IoV: Authenticated key management protocol in fog computing- based internet of vehicles deployment,'' IEEE Internet Things J., vol. 6, no. 5, pp. 8804-8817, Oct. 2019, doi: 10.1109/jiot.2019.2923611. · DOI
  9. M. Wazid, A. K. Das, N. Kumar, V. Odelu, A. G. Reddy, K. Park, and Y. Park, ''Design of lightweight authentication and key agree- ment protocol for vehicular ad hoc networks,'' IEEE Access, vol. 5, pp. 14966-14980, 2017.
  10. H. Larthani, A. Zrelli, and T. Ezzedine, ''On the detection of disasters: Optical sensors and IoT technologies,'' in Proc. Int. Conf. Internet Things, Embedded Syst. Commun. (IINTEC), Hammamet, Tunisia, Dec. 2018, pp. 142-146.
  11. A. H. Farooqi and F. A. Khan, ''A survey of intrusion detection systems for wireless sensor networks,'' Int. J. Ad Hoc Ubiquitous Comput., vol. 9, no. 2, pp. 69-83, 2012.
  12. A. R. Dhakne and P. N. Chatur, ''A comprehensive survey on intrusion detection systems in wireless sensor network,'' in Smart Trends in Infor- mation Technology and Computer Communications, vol. 628. Singapore: Springer, 2016, pp. 541-549.
  13. B. B. Zarpelāo, R. S Miani, C. T. Kawakani, and S. C. de Alvarenga, ''A survey of intrusion detection in Internet of Things,'' J. Netw. Comput. Appl., vol. 84, pp. 25-37, Apr. 2017.
  14. M. F. Elrawy, A. I. Awad, and H. F. A. Hamed, ''Intrusion detection systems for IoT-based smart environments: A survey,'' J. Cloud Comput., vol. 7, no. 1, p. 21, Dec. 2018.
  15. Z. A. Khan and P. Herrmann, ''Recent advancements in intrusion detec- tion systems for the Internet of Things,'' Secur. Commun. Netw., vol. 2019, pp. 1-19, Jul. 2019, doi: 10.1155/2019/4301409. · DOI
  16. J. Li, Y. Liu, J. Xie, M. Li, M. Sun, Z. Liu, and S. Jiang, ''A remote moni- toring and diagnosis method based on four-layer IoT frame perception,'' IEEE Access, vol. 7, pp. 144324-144338, 2019.
  17. O. Elijah, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. N. Hindia, ''An overview of Internet of Things (IoT) and data analytics in agricul- ture: Benefits and challenges,'' IEEE Internet Things J., vol. 5, no. 5, pp. 3758-3773, Oct. 2018.
  18. M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H.-M. Aggoune, ''Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk,'' IEEE Access, vol. 7, pp. 129551-129583, 2019.
  19. F.-H. Tseng, H.-H. Cho, and H.-T. Wu, ''Applying big data for intel- ligent agriculture-based crop selection analysis,'' IEEE Access, vol. 7, pp. 116965-116974, 2019.
  20. S. Chatterjee and A. K. Das, ''An effective ECC-based user access control scheme with attribute-based encryption for wireless sensor networks,'' Secur. Commun. Netw., vol. 8, no. 9, pp. 1752-1771, Jun. 2015.
  21. A. K. Das, ''A secure and robust temporal credential-based three-factor user authentication scheme for wireless sensor networks,'' Peer-to-Peer Netw. Appl., vol. 9, no. 1, pp. 223-244, Jan. 2016.
  22. A. K. Das, ''A secure and effective biometric-based user authentication scheme for wireless sensor networks using smart card and fuzzy extrac- tor,'' Int. J. Commun. Syst., vol. 30, no. 1, Jan. 2017, Art. no. e2933.
  23. A. K. Das, ''A secure and efficient user anonymity-preserving three- factor authentication protocol for large-scale distributed wireless sensor networks,'' Wireless Pers. Commun., vol. 82, no. 3, pp. 1377-1404, Jun. 2015.
  24. M. Wazid, A. K. Das, S. Kumari, and M. K. Khan, ''Design of sinkhole node detection mechanism for hierarchical wireless sensor networks,'' Secur. Commun. Netw., vol. 9, no. 17, pp. 4596-4614, Nov. 2016.
  25. D. Dolev and A. Yao, ''On the security of public key protocols,'' IEEE Trans. Inf. Theory, vol. IT-29, no. 2, pp. 198-208, Mar. 1983.
  26. M. Wazid, A. K. Das, N. Kumar, and J. J. P. C. Rodrigues, ''Secure three- factor user authentication scheme for renewable-energy-based smart grid environment,'' IEEE Trans. Ind. Informat., vol. 13, no. 6, pp. 3144-3153, Dec. 2017.
  27. M. Wazid, A. K. Das, S. Kumari, X. Li, and F. Wu, ''Provably secure biometric-based user authentication and key agreement scheme in cloud computing,'' Secur. Commun. Netw., vol. 9, no. 17, pp. 4103-4119, Nov. 2016.
  28. T. Messerges, E. Dabbish, and R. Sloan, ''Examining smart-card security under the threat of power analysis attacks,'' IEEE Trans. Comput., vol. 51, no. 5, pp. 541-552, May 2002.
  29. J. Ryoo, D.-G. Han, S.-K. Kim, and S. Lee, ''Performance enhancement of differential power analysis attacks with signal companding methods,'' IEEE Signal Process. Lett., vol. 15, pp. 625-628, Oct. 2008.
  30. M. Wazid and A. K. Das, ''An efficient hybrid anomaly detection scheme using k-means clustering for wireless sensor networks,'' Wireless Pers. Commun., vol. 90, no. 4, pp. 1971-2000, Oct. 2016.
  31. M. Wazid and A. K. Das, ''A secure group-based blackhole node detec- tion scheme for hierarchical wireless sensor networks,'' Wireless Pers. Commun., vol. 94, no. 3, pp. 1165-1191, Jun. 2017.
  32. S. Zeadally, A. K. Das, and N. Sklavos, ''Cryptographic technolo- gies and protocol standards for Internet of Things,'' Internet Things, to be published. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/S2542660519301799
  33. A. K. Das and S. Zeadally, ''Data security in the smart grid environment,'' in Pathways to a Smarter Power System, A. Tascikaraoglu and O. Erdinc, Eds. Amsterdam, The Netherlands: Elsevier, 2019, pp. 371-395.
  34. R. Kumar, X. Zhang, W. Wang, R. U. Khan, J. Kumar, and A. Sharif, ''A multimodal malware detection technique for android IoT devices using various features,'' IEEE Access, vol. 7, pp. 64411-64430, 2019.
  35. A. Azmoodeh, A. Dehghantanha, and K.-K.-R. Choo, ''Robust mal- ware detection for Internet of (battlefield) Things devices using deep eigenspace learning,'' IEEE Trans. Sustain. Comput., vol. 4, no. 1, pp. 88-95, Jan. 2019.
  36. F. Ullah, H. Naeem, S. Jabbar, S. Khalid, M. A. Latif, F. Al-turjman, and L. Mostarda, ''Cyber security threats detection in Internet of Things using deep learning approach,'' IEEE Access, vol. 7, pp. 124379-124389, 2019.
  37. A. Mahboubi, S. Camtepe, and H. Morarji, ''A study on formal meth- ods to generalize heterogeneous mobile malware propagation and their impacts,'' IEEE Access, vol. 5, pp. 27740-27756, 2017.
  38. M. Wazid, A. K. Das, R. Hussain, G. Succi, and J. J. Rodrigues, ''Authen- tication in cloud-driven IoT-based big data environment: Survey and outlook,'' J. Syst. Archit., vol. 97, pp. 185-196, Aug. 2019.
  39. S. Challa, M. Wazid, A. K. Das, N. Kumar, A. G. Reddy, E.-J. Yoon, and K.-Y. Yoo, ''Secure signature-based authenticated key establishment scheme for future IoT applications,'' IEEE Access, vol. 5, pp. 3028-3043, 2017.
  40. I. Medeiros, M. Beatriz, N. Neves, and M. Correia, ''SEPTIC: Detecting injection attacks and vulnerabilities inside the DBMS,'' IEEE Trans. Rel., vol. 68, no. 3, pp. 1168-1188, Sep. 2019.
  41. J. Fonseca, M. Vieira, and H. Madeira, ''Evaluation of Web secu- rity mechanisms using vulnerability & attack injection,'' IEEE Trans. Depend. Sec. Comput., vol. 11, no. 5, pp. 440-453, Sep. 2014.
  42. D. Mitropoulos, P. Louridas, M. Polychronakis, and A. D. Keromytis, ''Defending against Web application attacks: Approaches, challenges and implications,'' IEEE Trans. Depend. Sec. Comput., vol. 16, no. 2, pp. 188-203, Mar. 2019.
  43. M. Wazid, ''Design and analysis of intrusion detection protocols for hierarchical wireless sensor networks,'' Ph.D. dissertation, Center Secur., Theory Algorithmic Res., Int. Inst. Inf. Technol., Hyderabad, India, 2017.
  44. M. Wazid, A. K. Das, V. Odelu, N. Kumar, M. Conti, and M. Jo, ''Design of secure user authenticated key management protocol for generic IoT networks,'' IEEE Internet Things J., vol. 5, no. 1, pp. 269-282, Feb. 2018.
  45. J. Yoo and H. Kim, ''Target localization in wireless sensor networks using online semi-supervised support vector regression,'' Sensors, vol. 15, no. 6, pp. 12539-12559, May 2015.
  46. S. Kumar, S. N. Tiwari, and R. M. Hegde, ''Sensor node tracking using semi-supervised hidden Markov models,'' Ad Hoc Netw., vol. 33, pp. 55-70, Oct. 2015.
  47. H. Geng, Y. Liang, F. Yang, L. Xu, and Q. Pan, ''Model-reduced fault detection for multi-rate sensor fusion with unknown inputs,'' Inf. Fusion, vol. 33, pp. 1-14, Jan. 2017.
  48. R. R. Swain, P. M. Khilar, and S. K. Bhoi, ''Heterogeneous fault diag- nosis for wireless sensor networks,'' Ad Hoc Netw., vol. 69, pp. 15-37, Feb. 2018.
  49. R. Palanikumar and K. Ramasamy, ''Effective failure nodes detection using matrix calculus algorithm in wireless sensor networks,'' Cluster Comput., vol. 22, no. S5, pp. 12127-12136, Sep. 2019.
  50. S. Zidi, T. Moulahi, and B. Alaya, ''Fault detection in wireless sensor networks through SVM classifier,'' IEEE Sensors J., vol. 18, no. 1, pp. 340-347, Jan. 2018.
  51. W. Wang, S. Jiang, H. Zhou, M. Yang, Y. Ni, and J. Ko, ''Time synchro- nization for acceleration measurement data of jiangyin bridge subjected to a ship collision,'' Struct. Control Health Monit., vol. 25, no. 1, Jan. 2018, Art. no. e2039.
  52. K.-P. Ng, C. Tsimenidis, and W. L. Woo, ''C-Sync: Counter-based syn- chronization for duty-cycled wireless sensor networks,'' Ad Hoc Netw., vol. 61, pp. 51-64, Jun. 2017.
  53. Y.-P. Tian, ''Time synchronization in WSNs with random bounded communication delays,'' IEEE Trans. Automat. Contr., vol. 62, no. 10, pp. 5445-5450, Oct. 2017.
  54. D. Capriglione, D. Casinelli, and L. Ferrigno, ''Analysis of quantities influencing the performance of time synchronization based on linear regression in low cost WSNs,'' Measurement, vol. 77, pp. 105-116, Jan. 2016.
  55. A. Davis, ''A survey of wireless sensor network architectures,'' Int. J. Comput. Sci. Eng. Surv., vol. 3, no. 6, pp. 1-22, Dec. 2012.
  56. S. Vishwakarma, ''An analysis of LEACH protocol in wireless sensor network: A survey,'' Int. J. Comput. Sci. Eng. Surv., vol. 6, no. 3, pp. 148-154, 2015.
  57. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, ''Internet of Things (IoT): A vision, architectural elements, and future directions,'' Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1645-1660, Sep. 2013.
  58. S. Xu, G. Yang, Y. Mu, and X. Liu, ''A secure IoT cloud storage system with fine-grained access control and decryption key exposure resistance,'' Future Gener. Comput. Syst., vol. 97, pp. 284-294, Aug. 2019.
  59. M. Wazid, A. K. Das, N. Kumar, and A. V. Vasilakos, ''Design of secure key management and user authentication scheme for fog computing services,'' Future Gener. Comput. Syst., vol. 91, pp. 475-492, Feb. 2019.
  60. M. Wazid, P. Reshma Dsouza, A. K. Das, V. Bhat K, N. Kumar, and J. J. P. C. Rodrigues, ''RAD-EI: A routing attack detection scheme for edge-based Internet of Things environment,'' Int. J. Commun. Syst., vol. 32, no. 15, p. e4024, Oct. 2019, doi: 10.1002/dac.4024. · DOI
  61. A. K. Das, S. Zeadally, and D. He, ''Taxonomy and analysis of security protocols for Internet of Things,'' Future Gener. Comput. Syst., vol. 89, pp. 110-125, Dec. 2018.
  62. Y.-W. Kao, K.-Y. Huang, H.-Z. Gu, and S.-M. Yuan, ''UCloud: A user- centric key management scheme for cloud data protection,'' IET Inf. Secur., vol. 7, no. 2, pp. 144-154, Jun. 2013.
  63. J. Li, X. Chen, M. Li, J. Li, P. P. Lee, and W. Lou, ''Secure deduplication with efficient and reliable convergent key management,'' IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 6, pp. 1615-1625, Jun. 2014.
  64. P. K. Tysowski and M. A. Hasan, ''Hybrid attribute-and re-encryption- based key management for secure and scalable mobile applications in clouds,'' IEEE Trans. Cloud Comput., vol. 1, no. 2, pp. 172-186, 2013.
  65. J. Yu, K. Ren, C. Wang, and V. Varadharajan, ''Enabling cloud storage auditing with key-exposure resistance,'' IEEE Trans. Inf. Forensics Secu- rity, vol. 10, no. 6, pp. 1167-1179, Jun. 2015.
  66. L. Eschenauer and V. D. Gligor, ''A key management scheme for dis- tributed sensor networks,'' in Proc. 9th ACM Conf. Comput. Commun. Secur., Nov. 2002, pp. 41-47.
  67. H. Chan, A. Perrig, and D. Song, ''Random key predistribution schemes for sensor networks,'' in Proc. IEEE Symp. Security Privacy, Berkeley, CA, USA, May 2003, pp. 197-213.
  68. W. Du, J. Deng, Y. S. Han, S. Chen, and P. K. Varshney, ''A key management scheme for wireless sensor networks using deployment knowledge,'' in Proc. 23rd Conf. IEEE Commun. Soc. (Infocom), Hong Kong, vol. 1, Mar. 2004, pp. 586-597.
  69. W. Du, J. Deng, Y. S. Han, and P. K. Varshney, ''A pairwise key pre- distribution scheme for wireless sensor networks,'' in Proc. ACM Conf. Comput. Commun. Secur. (CCS), Washington, DC, USA, Oct. 2003, pp. 42-51.
  70. C. Blundo, A. De Santis, A. Herzberg, S. Kutten, U. Vaccaro, and M. Yung, ''Perfectly-secure key distribution for dynamic conferences,'' in Advances in Cryptology-CRYPTO (Lecture Notes in Computer Sci- ence), vol. 740. Berlin, Germany: Springer, 1993, pp. 471-486.
  71. Y. Cheng and D. P. Agrawal, ''Efficient pairwise key establishment and management in static wireless sensor networks,'' in Proc. 2nd IEEE Int. Conf. Mobile Ad Hoc Sensor Syst., Washington, DC, USA, Nov. 2005, p. 550.
  72. D. Liu, P. Ning, and W. Du, ''Group-based key predistribution for wire- less sensor networks,'' in Proc. ACM Workshop Wireless Secur. (WiSe), Sep. 2005, pp. 1-10.
  73. Q. Dong and D. Liu, ''Using auxiliary sensors for pairwise key estab- lishment in WSN,'' in Proc. IFIP Int. Conf. Netw. (NETWORKING), in Lecture Notes in Computer Science, vol. 4479, 2007, pp. 251-262.
  74. S. Zhu, S. Setia, and S. Jajodia, ''LEAP+: Efficient security mechanisms for large-scale distributed sensor networks,'' ACM Trans. Sen. Netw., vol. 2, no. 4, pp. 500-528, Nov. 2006.
  75. A. Perrig, R. Szewczyk, J. D. Tygar, V. Wen, and D. E. Culler, ''SPINS: Security protocols for sensor networks,'' Wireless Netw., vol. 8, no. 5, pp. 521-534, Sep. 2002.
  76. D. Liu, P. Ning, and R. Li, ''Establishing pairwise keys in distributed sensor networks,'' ACM Trans. Inf. Syst. Secur., vol. 8, no. 1, pp. 41-77, Feb. 2005.
  77. A. K. Das, ''An identity-based random key pre-distribution scheme for direct key establishment to prevent attacks in wireless sensor networks,'' Int. J. Netw. Secur., vol. 6, no. 2, pp. 134-144, 2008.
  78. A. K. Das, ''ECPKS: An improved location-aware key management scheme in static sensor networks,'' Int. J. Netw. Secur., vol. 7, no. 3, pp. 358-369, 2008.
  79. A. K. Das, ''A random key establishment scheme for multi-phase deploy- ment in large-scale distributed sensor networks,'' Int. J. Inf. Secur., vol. 11, no. 3, pp. 189-211, Jun. 2012.
  80. Y. Hong, W. M. Liu, and L. Wang, ''Privacy preserving smart meter streaming against information leakage of appliance status,'' IEEE Trans. Inf. Forensics Security, vol. 12, no. 9, pp. 2227-2241, Sep. 2017.
  81. M. Wazid, S. Zeadally, A. K. Das, and V. Odelu, ''Analysis of security protocols for mobile healthcare,'' J. Med. Syst., vol. 40, no. 11, p. 229, Nov. 2016.
  82. M. Badra and S. Zeadally, ''Design and performance analysis of a virtual ring architecture for smart grid privacy,'' IEEE Trans. Inf. Forensics Security, vol. 9, no. 2, pp. 321-329, Feb. 2014.
  83. S. Zeadally, A.-S.-K. Pathan, C. Alcaraz, and M. Badra, ''Towards pri- vacy protection in smart grid,'' Wireless Pers. Commun., vol. 73, no. 1, pp. 23-50, Nov. 2013.
  84. N. Li, N. Zhang, S. K. Das, and B. Thuraisingham, ''Privacy preservation in wireless sensor networks: A state-of-the-art survey,'' Ad Hoc Netw., vol. 7, no. 8, pp. 1501-1514, Nov. 2009.
  85. N. Sharma and R. Bhatt, ''Privacy preservation in WSN for healthcare application,'' Procedia Comput. Sci., vol. 132, pp. 1243-1252, Jan. 2018.
  86. M. Yamin, Y. Alsaawy, A. B. Alkhodre, and A. A. A. Sen, ''An innovative method for preserving privacy in Internet of Things,'' Sensors, vol. 19, no. 15, p. 3355, Jul. 2019. [Online]. Available: https://www.mdpi.com/ 1424-8220/19/15/3355
  87. V. Bhuse and A. Gupta, ''Anomaly intrusion detection in wireless sensor networks,'' J. High Speed Netw., vol. 15, no. 1, pp. 33-51, 2006.
  88. E. Darra and S. K. Katsikas, ''A survey of intrusion detection systems in wireless sensor networks,'' Intrusion Detection Prevention Mobile Ecosyst., pp. 393-458, Sep. 2017.
  89. Y. Wang, X. Wang, B. Xie, D. Wang, and D. Agrawal, ''Intrusion detec- tion in homogeneous and heterogeneous wireless sensor networks,'' IEEE Trans. Mobile Comput., vol. 7, no. 6, pp. 698-711, Jun. 2008.
  90. S.-S. Wang, K.-Q. Yan, S.-C. Wang, and C.-W. Liu, ''An integrated intrusion detection system for cluster-based wireless sensor networks,'' Expert Syst. Appl., vol. 38, no. 12, pp. 15234-15243, Nov. 2011.
  91. S. A. Salehi, M. A. Razzaque, P. Naraei, and A. Farrokhtala, ''Detection of sinkhole attack in wireless sensor networks,'' in Proc. IEEE Int. Conf. Space Sci. Commun. (IconSpace), Malacca, Malaysia, Jul. 2013, pp. 361-365.
  92. K. Selvakumar, M. Karuppiah, L. Sairamesh, S. H. Islam, M. M. Hassan, G. Fortino, and K.-K.-R. Choo, ''Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs,'' Inf. Sci., vol. 497, pp. 77-90, Sep. 2019.
  93. V. T. Alaparthy and S. D. Morgera, ''A multi-level intrusion detection system for wireless sensor networks based on immune theory,'' IEEE Access, vol. 6, pp. 47364-47373, 2018.
  94. Z. Sun, Y. Xu, G. Liang, and Z. Zhou, ''An intrusion detection model for wireless sensor networks with an improved V-detector algorithm,'' IEEE Sensors J., vol. 18, no. 5, pp. 1971-1984, Mar. 2018.
  95. Y. Wang, W. Fu, and D. P. Agrawal, ''Gaussian versus uniform distri- bution for intrusion detection in wireless sensor networks,'' IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 2, pp. 342-355, Feb. 2013.
  96. S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, ''Toward a lightweight intrusion detection system for the Internet of Things,'' IEEE Access, vol. 7, pp. 42450-42471, 2019.
  97. V. Sharma, I. You, K. Yim, I.-R. Chen, and J.-H. Cho, ''BRIoT: Behavior rule specification-based misbehavior detection for IoT-embedded cyber- physical systems,'' IEEE Access, vol. 7, pp. 118556-118580, 2019.
  98. H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, and K.-K.-R. Choo, ''A two-layer dimension reduction and two-tier classifi- cation model for anomaly-based intrusion detection in IoT backbone net- works,'' IEEE Trans. Emerg. Topics Comput., vol. 7, no. 2, pp. 314-323, Apr. 2019.
  99. W. Li, S. Tug, W. Meng, and Y. Wang, ''Designing collaborative blockchained signature-based intrusion detection in IoT environments,'' Future Gener. Comput. Syst., vol. 96, pp. 481-489, Jul. 2019.
  100. D. Breitenbacher, I. Homoliak, Y. L. Aung, N. O. Tippenhauer, and Y. Elovici, ''HADES-IoT: A practical host-based anomaly detection sys- tem for IoT devices,'' in Proc. ACM Asia Conf. Comput. Commun. Secur., Auckland, New Zealand, 2019, pp. 479-484.
  101. A. Mudgerikar, P. Sharma, and E. Bertino, ''E-spion: A system-level intrusion detection system for iot devices,'' in Proc. ACM Asia Conf. Comput. Commun. Secur., Auckland, New Zealand, 2019, pp. 493-500.
  102. A. Saeed, A. Ahmadinia, A. Javed, and H. Larijani, ''Intelligent Intrusion Detection in Low-Power IoTs,'' ACM Trans. Internet Technol., vol. 16, no. 4, pp. 1-25, Dec. 2016.
  103. MOHAMMAD WAZID (Member, IEEE) received the M.Tech. degree in computer network engineer- ing from Graphic Era University, Dehradun, India, and the Ph.D. degree in computer science and engi- neering from the International Institute of Infor- mation Technology, Hyderabad, India. He was an Assistant Professor with the Department of Com- puter Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Edu- cation, Manipal, India. He was a Postdoctoral Researcher with the Cyber Security and Networks lab, Innopolis Univer- sity, Innopolis, Russia. He is currently an Associate Professor with the Department of Computer Science and Engineering, Graphic Era University. His current research interests include security, remote user authentication, the Internet of Things (IoT), and cloud computing. He has published more than 60 articles in international journals and conferences in these areas. He was a recipient of the University Gold Medal and the Young Scientist Award by UCOST, Department of Science and Technology, Government of Uttarakhand, India. He also received the Recognition of ''Best Reviewer of 2019'' from ICT Express (Elsevier) journal.

Authors
Pundir, S., Wazid, M., Singh, D. P., Das, A. K., Rodrigues, J. J. P. C., & Park, Y.
Affiliations
  • Graphic Era University (IN)
  • International Institute of Information Technology, Hyderabad (IN)
  • Universidade Federal do Piauí (BR)
  • Kyungpook National University (KR)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
3343-3363
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2962829
OpenAlex
W2997414109
Cited by
202
References
108
Citations by year
2026: 12 · 2025: 17 · 2024: 56 · 2023: 37 · 2022: 34 · 2021: 37 · 2020: 8 · 2018: 1
FWCI
10.81
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Research Foundation
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (309335/2017 - 5, Grant No. 309335/2017-5, No. 309335/2017-5)
  • Ministry of Science, ICT and Future Planning (2017R1A2B1002147)
  • National Research Foundation of Korea (2017R1A2B1002147)
  • International Institute of Information Technology, Hyderabad
  • Fundação para a Ciência e a Tecnologia (309335/2017-5, UID/EEA/500008/2019, /2017, UID/EEA/500008/2019 Project)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
Keywords
wireless sensor networks, Internet of Things, Internet of Things security, threat model, security requirements, security architecture, privacy preservation, resource-constrained devices, smart objects

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review