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NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks

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Abstract

Internet of Things (IoT) makes physical objects and devices interact with each other through wireless technologies. IoT is expected to deliver a significant role in our lives in near future. However, at the current stage, IoT is vulnerable to various kinds of security threats just like other wired and wireless networks. Our work mainly focuses on protecting an IoT infrastructure from distributed denial-of-service attacks generated by the intruders. We present a new approach of using Naïve Bayes classification algorithm applied in intrusion detection systems (IDSs). IDSs are deployed in the form of multi-agents throughout the network to sense the misbehaving or irregular traffic and actions of nodes. In the paper, we also discuss the fundamental concepts related to our work and recent research done in similar area.

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Notes

  1. https://web.archive.org/web/20150205070216/http://nsl.cs.unb.ca/NSL-KDD/ accessed on 21-April, 2018.

  2. https://sourceforge.net/projects/weka/ Accessed on April 21, 2018.

References

  1. Miraz MH, Ali M, Excell PS et al (2017) A review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT). arXiv preprint arXiv

  2. Kumar JS, Patel DR (2014) A survey on Internet of Things: security and privacy issues. Int J Comput Appl 90(11):20–26

    Google Scholar 

  3. Farooq MU et al (2015) A review on Internet of Things (IoT). Int J Comput Appl 113(1):1–7

    MathSciNet  Google Scholar 

  4. Said O (2013) Development of an innovative Internet of Things security system. Int J Comput Sci Issues (IJCSI) 10(6):155–161

    Google Scholar 

  5. Wang S, Wan J, Li D, Zhang C (2016) Implementing smart factory of industrie 4.0: an outlook. Int J Distrib Sens Netw 12(1):3159805

    Article  Google Scholar 

  6. Mansor MN, Muna NU, Muhammad AS (2015) The potential of radio frequency identification (RFID) technology implementation in Malaysian Shipbuilding Industry. J Transp Syst Eng 2:31–36

    Google Scholar 

  7. Coskun V, Ozdenizci B, Ok K (2013) A survey on near field communication (NFC) technology. Wireless Pers Commun 71(3):2259–2294

    Article  Google Scholar 

  8. Gao B et al (2015) On the overhead reduction of millimeter-wave beamforming training in wireless M2M network via multidevice multipath simultaneous training. Int J Distrib Sens Netw 1328–1333

  9. Kuang LW, Mei-Tso L, Yu-Hsuan Y (2015) A machine learning system for routing decision-making in urban vehicular ad hoc networks. Int J Distrib Sens Netw 11:374391

    Article  Google Scholar 

  10. Ploennigs J, Ryssel U, Kabitzsch K (2010) Performance analysis of the EnOcean wireless sensor network protocol. In: 2010 IEEE Conference on Emerging Technologies and Factory Automation (ETFA). IEEE

  11. Aman W (2016) Assessing the feasibility of adaptive security models for the Internet of Things. In: International Conference on Human Aspects of Information Security, Privacy, and Trust. Springer International Publishing, pp 201–211

  12. Vermesan O, Friess P, Guillemin P, Gusmeroli S, Sundmaeker H, Bassi A et al (2011) Internet of things strategic research roadmap. Internet Things Glob Technol Soc Trends 1:9–52

    Google Scholar 

  13. Mehmood A, Khanan A, Umar MM, Abdullah S, Ariffin KAZ, Song H (5694) Secure knowledge and cluster-based intrusion detection mechanism for smart wireless sensor networks. IEEE Access 6:5688

    Article  Google Scholar 

  14. Khan R et al (2012) Future internet: the Internet of Things architecture, possible applications and key challenges. In: 2012 10th International Conference on Frontiers of Information Technology (FIT). IEEE

  15. Ullah I, Shah MA, Wahid A, Mehmood A, Song H (2018) ESOT: a new privacy model for preserving location privacy in Internet of Things. Telecommun Syst 67(4):553–575

    Article  Google Scholar 

  16. Borgohain T, Kumar U, Sanyal S (2015) Survey of security and privacy issues of Internet of Things. arXiv preprint arXiv:1501.02211

  17. Mehmood A, Lloret J, Sendra S (2016) A secure and low energy zone-based wireless sensor networks routing protocol for pollution monitoring. Wirel Commun Mob Comput 16(17):2869–2883

    Article  Google Scholar 

  18. Fremantle P, Scott P (2015) A security survey of middleware for the Internet of Things. PeerJ PrePrints 3:e1521

    Google Scholar 

  19. Mehmood A, Nouman M, Umar MM, Song H (2016) ESBL: an energy-efficient scheme by balancing load in group based WSNs. KSII Trans Internet Inf Syst 10(10):1–19

    Google Scholar 

  20. Jing Q et al (2014) Security of the Internet of Things: perspectives and challenges. Wirel Netw 20(8):2481–2501

    Article  Google Scholar 

  21. Umar MM, Mehmood A, Song H (2016) SeCRoP: secure cluster head centered multihop routing protocol for mobile ad hoc networks. Secur Commun Netw 9(16):3378–3387

    Article  Google Scholar 

  22. Palmer J (2011) Naïve Bayes classification for intrusion detection using live packet capture. In: Palmer J (ed) Data mining in bioinformatics. Springer, Berlin

  23. Mehmood A, Umar MM, Song H (2017) ICMDS: secure inter-cluster multiple-key distribution scheme for wireless sensor networks. Ad Hoc Netw 55:97–106

    Article  Google Scholar 

  24. Prasad KM, Reddy ARM, Rao KV (2014) DoS and DDoS attacks: defense, detection and traceback mechanisms–a survey. Glob J Comput Sci Technol 14(7):1–19

    Google Scholar 

  25. Zargar ST, Jyoti 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 

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

    Google Scholar 

  27. Sun B et al (2007) Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wirel Commun 14(5):56–63

    Article  Google Scholar 

  28. Liao H-J et al (2013) Intrusion detection system: a comprehensive review. J Netw Comput Appl 36(1):16–24

    Article  Google Scholar 

  29. Daneshfar F, Hassan B (2009) Multi-agent systems in control engineering: a survey. J. Control Sci. Eng. Article ID 531080, p 12. https://doi.org/10.1155/2009/531080

    Article  Google Scholar 

  30. Mechtri L, Tolba FD, Ghanemi S (2012) MASID: multi-agent system for intrusion detection in MANET. In: 2012 Ninth International Conference on Information Technology: New Generations (ITNG). IEEE

  31. Le A et al (2012) 6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach. Int J Commun Syst 25(9):1189–1212

    Article  Google Scholar 

  32. Marsh D et al (2004) Autonomic wireless sensor networks. Eng Appl Artif Intell 17(7):741–748

    Article  Google Scholar 

  33. Kasinathan P et al (2013) Denial-of-service detection in 6LoWPAN based Internet of Things. In: 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE

  34. Sen J (2010) An agent-based intrusion detection system for local area networks. arXiv preprint arXiv:1011.1531

  35. Panda M, Patra MR (2007) Network intrusion detection using Naive Bayes. Int J Comput Sci Netw Secur 7(12):258–263

    Google Scholar 

  36. Raza S, Wallgren L, Voigt T (2013) SVELTE: real-time intrusion detection in the Internet of Things. Ad Hoc Netw 11(8):2661–2674

    Article  Google Scholar 

  37. Liu C et al (2011) Research on immunity-based intrusion detection technology for the internet of things. In: 2011 Seventh International Conference on Natural Computation (ICNC), vol 1. IEEE

  38. Marmol G, Perez M (2010) Providing trust in wireless sensor networks using a bioinspired technique. Telecommun Syst 46(2):163–180

    Article  Google Scholar 

  39. Srinivasan A, Teitelbaum J, Wu J (2006) DRBTS: distributed reputation-based beacon trust system. In: Proceedings of 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC’06), pp 277–283

  40. Xiang Y, Li K, Zhou W (2011) Low-rate DDoS attacks detection and traceback by using new information metrics. IEEE Trans Inf Forensics Secur 6(2):426–437

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Information Technology, Kohat University of Science and Technology, Kohat, Pakistan

    Amjad Mehmood

  2. Guangdong Provincial Key Lab of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, 525000, China

    Mithun Mukherjee

  3. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Syed Hassan Ahmed

  4. Department of Electrical, Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA

    Houbing Song

  5. Department of Computer Science and Engineering, Oakland University, 532 EC 115 Library Drive, Rochester, MI, 48309-4479, USA

    Khalid Mahmood Malik

Authors
  1. Amjad Mehmood
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  2. Mithun Mukherjee
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  3. Syed Hassan Ahmed
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  4. Houbing Song
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  5. Khalid Mahmood Malik
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Corresponding author

Correspondence to Houbing Song.

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Mehmood, A., Mukherjee, M., Ahmed, S.H. et al. NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks. J Supercomput 74, 5156–5170 (2018). https://doi.org/10.1007/s11227-018-2413-7

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  • DOI: https://doi.org/10.1007/s11227-018-2413-7

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