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Ensemble Feature Selection Approach for Detecting Denial of Service Attacks in RPL Networks

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Advances in Cyber Security (ACeS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1487))

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Abstract

The Internet of Things (IoTs) is regarded as a future trend following the Internet revolution. Many of us now use physical and electronic devices in our daily lives to perform and deliver specific services. All physical and electronic devices are linked together in IoT networks. Some of these devices, known as constrained devices, are battery-powered and operate in low-energy mode. Therefore, to allow communication and forward packets between constrained devices. The routing protocol for a low-power and lossy network (RPL) is proposed. RPL, on the other hand, is not an energy-aware protocol, making it vulnerable to a wide range of security threats. Denial of Service (DDoS) flooding attacks were the most significant attacks that targeted RPL. Hence, a reliable method for detecting DDoS flooding-based RPL attacks is required. In this paper, an ensemble Feature Selection (FS) approach for detecting DDoS attacks in RPL networks is presented. The proposed approach employs three bio-inspired algorithms to select the optimal subset of features that contribute to high detection accuracy. Furthermore, Support Vector Machine (SVM) is used as a classification algorithm to evaluate the subset of features produced by bio-inspired algorithms. Finally, the proposed approach is expected to significantly detect and identify DDoS flooding attack patterns in RPL networks.

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References

  1. Al-Hadhrami, Y., Hussain, F.K.: DDoS attacks in IoT networks: a comprehensive systematic literature review (2021)

    Google Scholar 

  2. Alamiedy, T.A., Anbar, M., Al-Ani, A.K., Al-Tamimi, B.N., Faleh, N.: Review on feature selection algorithms for anomaly-based intrusion detection system. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds.) Recent Trends in Data Science and Soft Computing. Advances in Intelligent Systems and Computing, pp. 605–619. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99007-1_57

    Chapter  Google Scholar 

  3. Mahmoud, R., Yousuf, T., Aloul, F., Zualkernan, I.: Internet of things (IoT) security: Current status, challenges and prospective measures. In: 2015 10th International Conference for Internet Technology and Secured Transactions, ICITST 2015, pp. 336–341. IEEE (2016)

    Google Scholar 

  4. Cisco: Cisco Annual Internet Report (2018–2023). Comput. Fraud Secur. 2020, 4 (2020)

    Google Scholar 

  5. Fields, B.K.K., Demirjian, N.L., Gholamrezanezhad, A.: Coronavirus Disease 2019 (COVID-19) diagnostic technologies: a country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic (2020). https://doi.org/10.1016/j.clinimag.2020.08.014

  6. Whitelaw, S., Mamas, M.A., Topol, E., Van Spall, H.G.C.: Applications of digital technology in COVID-19 pandemic planning and response (2020)

    Google Scholar 

  7. Chick, R.C., et al.: Using technology to maintain the education of residents during the COVID-19 pandemic. J. Surg. Educ. 77, 729–732 (2020). https://doi.org/10.1016/j.jsurg.2020.03.018

    Article  Google Scholar 

  8. Kaharuddin, Ahmad, D., Mardiana, Rusni: Contributions of technology, culture, and attitude to English learning motivation during COVID-19 outbreaks. Syst. Rev. Pharm. 11, 76–84 (2020). https://doi.org/10.31838/srp.2020.11.13

  9. Alashhab, Z.R., Anbar, M., Singh, M.M., Leau, Y.B., Al-Sai, Z.A., Alhayja’a, S.A.: Impact of coronavirus pandemic crisis on technologies and cloud computing applications. J. Electron. Sci. Technol. 19, 25–40 (2021). https://doi.org/10.1016/j.jnlest.2020.100059

  10. Lueth, K.L.: The impact of Covid-19 on the Internet of Things Part 2. https://iot-analytics.com/the-impact-of-covid-19-on-the-internet-of-things-part-2/

  11. Ligero, R.: Accent Systems developed a connected wristband to contain Covid-19. https://accent-systems.com/blog/accent-systems-developed-connected-wristband-technology-contain-covid19/?v=75dfaed2dded

  12. Chen, Y., Chanet, J.P., Hou, K.M., Zhou, P.: A context-aware tool-set for routing-targeted mutual configuration and optimization of LLNs through bridging virtual and physical worlds. In: New and smart Information Communication Science and Technology to support Sustainable Development (NICST 2014) (2014). 5 p.

    Google Scholar 

  13. Ammar Rafea, S., Abdulrahman Kadhim, A.: Routing with energy threshold for WSN-IoT based on RPL protocol. Iraqi J. Comput. Commun. Control Syst. Eng. 71–81 (2019). https://doi.org/10.33103/uot.ijccce.19.1.9

  14. Tennina, S., Gaddour, O., Koubâa, A., Royo, F., Alves, M., Abid, M.: Z-Monitor: A protocol analyzer for IEEE 802.15.4-based low-power wireless networks. Comput. Netw. 95, 77–96 (2016). https://doi.org/10.1016/j.comnet.2015.12.002

  15. Fallis, A.: RFC6550 RPL: IPv6 routing protocol for low-power and lossy networks. J. Chem. Inf. Model. 53, 1689–1699 (2013)

    Article  Google Scholar 

  16. Palattella, M.R., et al.: Standardized protocol stack for the internet of (important) things (2013)

    Google Scholar 

  17. Mahmoud, C., Aouag, S.: Security for internet of things: a state of the art on existing protocols and open research issues. In: ACM International Conference Proceedings Series (2019). https://doi.org/10.1145/3361570.3361622

  18. Kim, H.S., Cho, H., Kim, H., Bahk, S.: DT-RPL: diverse bidirectional traffic delivery through RPL routing protocol in low power and lossy networks. Comput. Netw. 126, 150–161 (2017). https://doi.org/10.1016/j.comnet.2017.07.001

    Article  Google Scholar 

  19. Tian, H., Qian, Z., Wang, X., Liang, X.: QoI-Aware DODAG construction in RPL-based event detection wireless sensor networks. J. Sens. 2017 (2017). https://doi.org/10.1155/2017/1603713

  20. Xiao, W., Liu, J., Jiang, N., Shi, H.: An optimization of the object function for routing protocol of low-power and Lossy networks. In: 2014 2nd International Conference on Systems and Informatics, ICSAI 2014, pp. 515–519 (2015). https://doi.org/10.1109/ICSAI.2014.7009341

  21. Lamaazi, H., Benamar, N., Jara, A.J.: RPL-based networks in static and mobile environment: a performance assessment analysis. J. King Saud Univ. - Comput. Inf. Sci. 30, 320–333 (2018). https://doi.org/10.1016/j.jksuci.2017.04.001

  22. Ma, G., Li, X., Pei, Q., Li, Z.: A security routing protocol for internet of things based on RPL. In: Proceedings - 2017 International Conference on Networking and Network Applications, NaNA 2017, pp. 209–213. Institute of Electrical and Electronics Engineers Inc. (2017)

    Google Scholar 

  23. Le, A., Loo, J., Lasebae, A., Vinel, A., Chen, Y., Chai, M.: The impact of rank attack on network topology of routing protocol for low-power and lossy networks. IEEE Sens. J. 13, 3685–3692 (2013). https://doi.org/10.1109/JSEN.2013.2266399

    Article  Google Scholar 

  24. Raoof, A., Matrawy, A., Lung, C.H.: Routing attacks and mitigation methods for RPL-based internet of things. IEEE Commun. Surv. Tutor. 21, 1582–1606 (2019). https://doi.org/10.1109/COMST.2018.2885894

    Article  Google Scholar 

  25. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17, 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095

    Article  Google Scholar 

  26. AlSawafi, Y., Touzene, A., Day, K., Alzeidi, N.: Hybrid RPL-based sensing and routing protocol for smart city. Int. J. Pervasive Comput. Commun. 16, 279–306 (2020). https://doi.org/10.1108/IJPCC-11-2019-0088

    Article  Google Scholar 

  27. Winter, T., Thubert, P.: RPL: IPv6 routing protocol for low power and lossy networks, draft-ietf-roll-rpl-04.txt. IETF, Internet Draft (work progress) (2009)

    Google Scholar 

  28. Fatima-Tuz-Zahra, Jhanjhi, N.Z., Brohi, S.N., Malik, N.A.: Proposing a rank and wormhole attack detection framework using machine learning. In: MACS 2019 - 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics Proceedings (2019). https://doi.org/10.1109/MACS48846.2019.9024821

  29. Fatima-Tuz-Zahra, Jhanjhi, N.Z., Brohi, S.N., Malik, N.A., Humayun, M.: Proposing a hybrid RPL protocol for rank and wormhole attack mitigation using machine learning. In: 2020 2nd International Conference on Computer and Information Sciences, ICCIS 2020, pp. 1–6. IEEE (2020)

    Google Scholar 

  30. Perazzo, P., Vallati, C., Arena, A., Anastasi, G., Dini, G.: An implementation and evaluation of the security features of RPL. In: Puliafito, A., Bruneo, D., Distefano, S., Longo, F. (eds.) ADHOC-NOW 2017. LNCS, vol. 10517, pp. 63–76. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67910-5_6

    Chapter  Google Scholar 

  31. Mayzaud, A., Badonnel, R., Chrisment, I.: A taxonomy of attacks in RPL-based internet of things (2016)

    Google Scholar 

  32. Wallgren, L., Raza, S., Voigt, T.: Routing attacks and countermeasures in the RPL-based internet of things. Int. J. Distrib. Sens. Netw. 2013, 11 (2013). https://doi.org/10.1155/2013/794326

    Article  Google Scholar 

  33. Alzubaidi, M., Anbar, M., Hanshi, S.M.: Neighbor-passive monitoring technique for detecting sinkhole attacks in RPL networks. In: Proceedings of the 2017 International Conference on Computer Science and Artificial Intelligence - CSAI 2017. ACM Press, New York (2017)

    Google Scholar 

  34. Alzubaidi, M., Anbar, M., Chong, Y.W., Al-Sarawi, S.: Hybrid monitoring technique for detecting abnormal behaviour in RPL-based network. J. Commun. 13, 198–208 (2018). https://doi.org/10.12720/jcm.13.5.198-208

  35. Alzubaidi, M., Anbar, M., Al-Saleem, S., Al-Sarawi, S., Alieyan, K.: Review on mechanisms for detecting sinkhole attacks on RPLs. In: ICIT 2017 - 8th International Conference on Information Technology, Proceedings, pp. 369–374. Institute of Electrical and Electronics Engineers Inc. (2017)

    Google Scholar 

  36. Pongle, P., Chavan, G.: A survey: attacks on RPL and 6LoWPAN in IoT. In: 2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015 (2015)

    Google Scholar 

  37. Napiah, M.N., Bin Idris, M.Y.I., Ramli, R., Ahmedy, I.: Compression header analyzer intrusion detection system (CHA - IDS) for 6LoWPAN communication protocol. IEEE Access 6, 16623–16638 (2018). https://doi.org/10.1109/ACCESS.2018.2798626

  38. Amin, S.O., Siddiqui, M.S., Hong, C.S., Lee, S.: RIDES: Robust intrusion detection system for IP-based Ubiquitous Sensor Networks. Sensors 9, 3447–3468 (2009). https://doi.org/10.3390/s90503447

    Article  Google Scholar 

  39. Kasinathan, P., Costamagna, G., Khaleel, H., Pastrone, C., Spirito, M.A.: Demo: an IDS framework for internet of things empowered by 6LoWPAN. In: Proceedings of the ACM Conference on Computer & Communications Security, pp. 1337–1339 (2013). https://doi.org/10.1145/2508859.2512494

  40. Medjek, F., Tandjaoui, D., Romdhani, I., Djedjig, N.: A trust-based intrusion detection system for mobile RPL based networks. In: Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017, pp. 735–742. Institute of Electrical and Electronics Engineers Inc. (2018)

    Google Scholar 

  41. Airehrour, D., Gutierrez, J., Ray, S.K.: Securing RPL routing protocol from blackhole attacks using a trust-based mechanism. In: 26th International Telecommunication Networks and Applications Conference, ITNAC 2016, pp. 115–120. Institute of Electrical and Electronics Engineers Inc. (2017)

    Google Scholar 

  42. Airehrour, D., Gutierrez, J., Ray, S.: A trust-aware RPL routing protocol to detect blackhole and selective forwarding attacks. Aust. J. Telecommun. Digit. Econ. 5 (2017). https://doi.org/10.18080/ajtde.v5n1.2

  43. Alabsi, B.A., Anbar, M., Manickam, S., Elejla, O.E.: DDoS attack aware environment with secure clustering and routing based on RPL protocol operation. IET Circuits Devices Syst. 13, 748–755 (2019). https://doi.org/10.1049/iet-cds.2018.5079

    Article  Google Scholar 

  44. Autonomous Networks Research Group: Cooja Simulator – Contiki. http://anrg.usc.edu/contiki/index.php/Cooja_Simulator

  45. Wireshark Foundation: Wireshark Ă‚ Go deep. https://www.wireshark.org/

  46. Pazhaniraja, N., Paul, P., Roja, G., Shanmugapriya, K., Sonali, B.: A study on recent bio-inspired optimization algorithms. ieeexplore.ieee.org (2017)

    Google Scholar 

  47. Rai, D., Garg, A.K., Tyagi, K.: Bio-inspired optimization techniques-a critical comparative study 38, 1–7 (2013). https://doi.org/10.1145/2492248.2492271, dl.acm.org

  48. Alzubi, Q.M., Anbar, M., Alqattan, Z.N.M., Al-Betar, M.A., Abdullah, R.: Intrusion detection system based on a modified binary grey wolf optimisation. Neural Comput. Appl. 32(10), 6125–6137 (2019). https://doi.org/10.1007/s00521-019-04103-1

    Article  Google Scholar 

  49. Alamiedy, T.A., Anbar, M., Alqattan, Z.N.M., Alzubi, Q.M.: Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J. Ambient Intell. Human. Comput. 11(9), 3735–3756 (2019). https://doi.org/10.1007/s12652-019-01569-8

    Article  Google Scholar 

  50. Altaher, A.: Malware detection based on evolving clustering method for classification. Sci. Res. Essays 7, 2031–2036 (2012). https://doi.org/10.5897/sre12.001

    Article  Google Scholar 

  51. Razak, M.F.A., Anuar, N.B., Othman, F., Firdaus, A., Afifi, F., Salleh, R.: Bio-inspired for features optimization and malware detection. Arab. J. Sci. Eng. 43(12), 6963–6979 (2017). https://doi.org/10.1007/s13369-017-2951-y

    Article  Google Scholar 

  52. Soliman, O.S., Rassem, A.: A network intrusions detection system based on a quantum bio inspired algorithm. Int. J. Eng. Trends Technol. 10, 370–379 (2014). https://doi.org/10.14445/22315381/ijett-v10p271

  53. Clerc, M.: Particle Swarm Optimization (2010). https://doi.org/10.1002/9780470612163

  54. Safaldin, M., Otair, M., Abualigah, L.: Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J. Ambient Intell. Human. Comput. 12(2), 1559–1576 (2020). https://doi.org/10.1007/s12652-020-02228-z

    Article  Google Scholar 

  55. Yang: Firefly algorithm - Google Scholar. https://scholar.google.com/scholar?cluster=3276324836150250709&hl=en&oi=scholarr

  56. Mohammadi, M., et al.: A comprehensive survey and taxonomy of the SVM-based intrusion detection systems (2021)

    Google Scholar 

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Acknowledgment

This research was pursued under the Research University (RU) Grant, Universiti Sains Malaysia (USM) No: 1001.PNAV.8011107.

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Authors and Affiliations

  1. National Advanced IPv6 Centre, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Taief Alaa Alamiedy, Mohammed F. R. Anbar, Arkan Hamoodi Kabla & Baidaa Hamza Khudayer

  2. ECE Department, Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq

    Taief Alaa Alamiedy

  3. School of Computer Sciences, Universiti Sains Malaysia (USM), 11800, Gelugor, Penang, Malaysia

    Bahari Belaton

  4. Information Technology Department, AlBuraimi University College, Buraimi, Oman

    Baidaa Hamza Khudayer

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  1. Taief Alaa Alamiedy
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  2. Mohammed F. R. Anbar
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  3. Bahari Belaton
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Correspondence to Mohammed F. R. Anbar .

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  1. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  2. Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

  3. Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

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Alamiedy, T.A., Anbar, M.F.R., Belaton, B., Kabla, A.H., Khudayer, B.H. (2021). Ensemble Feature Selection Approach for Detecting Denial of Service Attacks in RPL Networks. In: Abdullah, N., Manickam, S., Anbar, M. (eds) Advances in Cyber Security. ACeS 2021. Communications in Computer and Information Science, vol 1487. Springer, Singapore. https://doi.org/10.1007/978-981-16-8059-5_21

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