Skip to main content
SpringerLink
Log in

An Overview on Security and Privacy Concerns in IoT-Based Smart Environments

  • Conference paper
  • First Online:
Security, Privacy and Data Analytics (ISPDA 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1049))

Included in the following conference series:

  • 146 Accesses

Abstract

Urban surroundings and human quality of life have significantly improved because of smart environments which include transportation, healthcare, smart buildings, public safety, smart parking, traffic systems, smart agriculture and other areas. They are completely capable of controlling the physical objects in real-time and delivering intelligent information to citizens. Technologies for smart cities can compile personal information. However, security and privacy issues may arise at several architectural levels. Therefore, it is crucial to take these security and privacy issues into account while creating and implementing the applications. In addition to discussing the significant issues of privacy and security throughout the development of the applications for smart cities, the article highlights the main applications of smart cities. We have analysed various possible attacks on IoT networks and moved towards Intrusion detection and highlighted the impact of communication technologies like 4G, 5G and 6G on the security and privacy on the IoT environment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Weber RH (2010) Internet of things-new security and privacy challenges. Comput Law Secur Rev 26(1):23–30

    Article  MathSciNet  Google Scholar 

  2. Malliga S, Nandhini PS, Kogilavani SV (2022) A comprehensive review of deep learning techniques for the detection of (distributed) denial of service attacks. Inf Technol Control 51(1):180–215

    Article  Google Scholar 

  3. Maselli G, Piva M, Restuccia F (2020) HyBloSE: hybrid block chain for secure-by-design smart environments. In: Proceedings of the 3rd workshop on cryptocurrencies and block chains for distributed systems (pp 23–28)

    Google Scholar 

  4. Khan Z, Pervez Z, Ghafoor A (2014) Towards cloud based smart cities data security and privacy management. In: 2014 IEEE/ACM 7th International conference on utility and cloud computing. IEEE, pp 806–811

    Google Scholar 

  5. Dorri A, Kanhere SS, Jurdak R, Gauravaram P (2017) Block chain for IoT security and privacy: the case study of a smart home. In: 2017 IEEE international conference on pervasive computing and communications workshops (PerCom workshops). IEEE, pp 618–623

    Google Scholar 

  6. Waheed N, He X, Ikram M, Usman M, Hashmi SS, Usman M (2020) Security and privacy in IoT using machine learning and blockchain: threats and countermeasures. ACM Comput Surv 53(6), Article 122, 37 p. https://doi.org/10.1145/3417987

  7. Ling Z, Liu K, Xu Y, Jin Y, Fu X (2017) An end-to-end view of IoT security and privacy. In: GLOBECOM 2017—2017 IEEE global communications conference, pp 1–7. https://doi.org/10.1109/GLOCOM.2017.8254011

  8. Shakarami M, Benson J, Sandhu R (2022) Blockchain-based administration of access in smart home IoT. In: Proceedings of the 2022 ACM workshop on secure and trustworthy cyber-physical systems, pp 57–66

    Google Scholar 

  9. Barhamgi M, Yang M, Yu CM, Yu Y, Bandara AK, Benslimane D, Nuseibeh B (2017) Enabling end-users to protect their privacy. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security, pp 905–907

    Google Scholar 

  10. Karie NM, Sahri NM, Yang W, Valli C, Kebande VR (2021) A review of security standards and frameworks for IoT-based smart environments. IEEE Access

    Google Scholar 

  11. Kulyk O, Reinheimer B, Aldag L, Mayer P, Gerber N, Volkamer M (2020) Security and privacy awareness in smart environments–a cross-country investigation. In: International conference on financial cryptography and data security. Springer, Cham, pp 84–101

    Google Scholar 

  12. Hajjaji Y, Boulila W, Farah IR, Romdhani I, Hussain A (2021) Big data and IoT-based applications in smart environments: a systematic review. Comput Sci Rev 39:100318

    Article  Google Scholar 

  13. Salim MM, Rathore S, Park JH (2020) Distributed denial of service attacks and its defenses in IoT: a survey. J Supercomput 76(7):5320–5363

    Article  Google Scholar 

  14. Elrawy MF, Awad AI, Hamed HF (2018) Intrusion detection systems for IoT-based smart environments: a survey. J Cloud Comput 7(1):1–20

    Article  Google Scholar 

  15. Granjal J, Monteiro E, Silva JS (2015) Security for the internet of things: a survey of existing protocols and open research issues. IEEE Commun Surv Tutor 17(3):1294–1312

    Article  Google Scholar 

  16. Ahmed E, Yaqoob I, Gani A, Imran M, Guizani M (2016) Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wirel Commun 23(5):10–16. https://doi.org/10.1109/MWC.2016.772

    Article  Google Scholar 

  17. Smys S, Basar A, Wang H (2020) Hybrid intrusion detection system for internet of things (IoT). J ISMAC 2(04):190–199

    Article  Google Scholar 

  18. Zarpelão BB, Miani RS, Kawakani CT, de Alvarenga SC (2017) A survey of intrusion detection in Internet of Things. J Netw Comput Appl 84:25–37

    Article  Google Scholar 

  19. Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, Atkinson R (2016) Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 2016 International symposium on networks, computers and communications (ISNCC). IEEE, pp 1–6

    Google Scholar 

  20. Tabassum A, Erbad A, Guizani M (2019) A survey on recent approaches in intrusion detection system in IoTs. In: 2019 15th international wireless communications & mobile computing conference (IWCMC). IEEE, pp 1190–1197

    Google Scholar 

  21. Ahmed M, Mahmood AN, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl 60:19–31

    Article  Google Scholar 

  22. Nisioti A, Mylonas A, Yoo PD, Katos V (2018) From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods. IEEE Commun Surv Tutor 20(4):3369–3388

    Article  Google Scholar 

  23. Bhuyan MH, Bhattacharyya DK, Kalita JK (2013) Network anomaly detection: methods, systems and tools. IEEE Commun Surv Tutor 16(1):303–336

    Article  Google Scholar 

  24. Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176

    Article  Google Scholar 

  25. Hande Y, Muddana A (2021) A survey on intrusion detection system for software defined networks (SDN). In: Research anthology on artificial intelligence applications in security. IGI Global, pp 467–489

    Google Scholar 

  26. Haq NF, Onik AR, Hridoy MAK, Rafni M, Shah FM, Farid DM (2015) Application of machine learning approaches in intrusion detection system: a survey. IJARAI-Int J Adv Res Artif Intell 4(3):9–18

    Google Scholar 

  27. Hodo E, Bellekens X, Hamilton A, Tachtatzis C, Atkinson R (2017) Shallow and deep networks intrusion detection system: a taxonomy and survey. ArXiv preprint arXiv 1701:02145

    Google Scholar 

  28. Milenkoski A, Vieira M, Kounev S, Avritzer A, Payne BD (2015) Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput Surv (CSUR) 48(1):1–41

    Article  Google Scholar 

  29. Mishra P, Varadharajan V, Tupakula U, Pilli ES (2018) A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun Surv Tutor 21(1):686–728

    Article  Google Scholar 

  30. Ring M, Wunderlich S, Scheuring D, Landes D, Hotho A (2019) A survey of network-based intrusion detection data sets. Comput Secur 86:147–167

    Article  Google Scholar 

  31. Thakkar A, Lohiya R (2020) A review of the advancement in intrusion detection datasets. Proc Comput Sci 167:636–645

    Article  Google Scholar 

  32. Wang L, Jones R (2017) Big data analytics for network intrusion detection: a survey. Int J Netw Commun 7(1):24–31

    Google Scholar 

  33. Yang Z, Liu X, Li T, Wu D, Wang J, Zhao Y, Han H (2022) A systematic literature review of methods and datasets for anomaly-based network intrusion detection. Comput Secur 102675

    Google Scholar 

  34. Singh KJ, Thongam K, De T (2018) Detection and differentiation of application layer DDoS attack from flash events using fuzzy-GA computation. IET Inf Secur 12(6):502–512

    Article  Google Scholar 

  35. Johnson Singh K, Thongam K, De T (2016) Entropy-based application layer DDoS attack detection using artificial neural networks. Entropy 18(10):350

    Article  Google Scholar 

  36. Meitei IL, Singh KJ, De T (2016) Detection of DDoS DNS amplification attack using classification algorithm. In: Proceedings of the international conference on informatics and analytics, pp 1–6

    Google Scholar 

  37. Specht S, Lee R (2003) Taxonomies of distributed denial of service networks, attacks, tools and countermeasures. In: CEL2003–03. Princeton University, Princeton, NJ, USA

    Google Scholar 

  38. Sonar K, Upadhyay H (2014) A survey: DDOS attack on Internet of Things. Int J Eng Res Dev 10(11):58–63

    Google Scholar 

  39. Zargar ST, Joshi J, Tipper D (2013) A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun Surv Tutor 15(4):2046–2069

    Article  Google Scholar 

  40. Zhang C, Green R (2015) Communication security in internet of thing: preventive measure and avoid DDoS attack over IoT network. In: Proceedings of the 18th symposium on communications and networking, pp 8–15

    Google Scholar 

  41. Abdul-Ghani HA, Konstantas D, Mahyoub M (2018) A comprehensive IoT attacks survey based on a building-blocked reference model. Int J Adv Comput Sci Appl 9(3)

    Google Scholar 

  42. Yang Y, Wu L, Yin G, Li L, Zhao H (2017) A survey on security and privacy issues in Internet-of-Things. IEEE Internet Things J 4(5):1250–1258

    Article  Google Scholar 

  43. Vishwakarma R, Jain AK (2020) A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommun Syst 73(1):3–25

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Institute of Technology, Manipur, Imphal, 795004, Manipur, India

    Nitin Anand & Khundrakpam Johnson Singh

Authors
  1. Nitin Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khundrakpam Johnson Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nitin Anand .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India

    Udai Pratap Rao

  2. Faculty of Science and Technology, Charles Darwin University, Northern Territory, NT, Australia

    Mamoun Alazab

  3. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India

    Bhavesh N. Gohil

  4. Reliance Jio Platforms, Bangalore, India

    Pethuru Raj Chelliah

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anand, N., Singh, K.J. (2023). An Overview on Security and Privacy Concerns in IoT-Based Smart Environments. In: Rao, U.P., Alazab, M., Gohil, B.N., Chelliah, P.R. (eds) Security, Privacy and Data Analytics. ISPDA 2022. Lecture Notes in Electrical Engineering, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-99-3569-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3569-7_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3568-0

  • Online ISBN: 978-981-99-3569-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation