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Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT)

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Abstract

The immense growth in the cyber world has given birth to various types of cybercrimes in the Internet of things (IoT). Cybercrimes have breached the multiple levels of cybersecurity that is one of the major issues in the IoT networks. Due to the rise in IoT applications, both devices and services are prone to security attacks and intrusions. The intrusion breaches the data packet extracted from different nodes deployed in the IoT network. Most of the intrusive attacks are very near variants of previously marked cyberattacks containing many repetitive data and features. And to detect the intrusion, the data packet needs to be analyzed. This article presents a novel scheme, i.e., dual-axis dimensionality reduction, that utilizes Kalman filter and salp swarm algorithm (coded as KF-SSA) for analyzing and minimizing the data packet. The proposed data reduction scheme is utilized with KELM-based multiclass classifier to efficiently detect intrusion in the IoT network (KF-SSA with KELM). The proposed method’s overall results are evaluated using standard intrusion detection datasets, i.e., NSL-KDD, KYOTO 2006+ (2015), CICIDS2017, and CICIDS2018 (AWS). The result from the proposed data reduction technique obtains highly reduced data, i.e., 70.% for NSL-KDD and 86.43% for CICIDS2017. The analyzed result shows high detection accuracy of 99.9% for NSL-KDD and 95.68% for CICIDS2017 with decreased computational time.

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References

  1. Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160

    Article  Google Scholar 

  2. Alrawais A, Alhothaily A, Hu C, Cheng X (2017a) Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput 21(2):34–42

    Article  Google Scholar 

  3. Alrawais A, Alhothaily A, Hu C, Cheng X (2017b) Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput 21(2):34–42

    Article  Google Scholar 

  4. Anastasi G, Conti M, Di Francesco M, Passarella A (2009) Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw 7(3):537–568

    Article  Google Scholar 

  5. Biswas P, Charitha R, Gavel S, Raghuvanshi AS (2019) Fault detection using hybrid of kf-elm for wireless sensor networks. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), IEEE, pp 746–750

  6. Bouraoui A, Jamoussi S, BenAyed Y (2017) A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines. Artif Intell Rev 50:261–281

    Article  Google Scholar 

  7. Chaabouni N, Mosbah M, Zemmari A, Sauvignac C, Faruki P (2019) Network intrusion detection for IoT security based on learning techniques. IEEE Commun Surv Tutor 21(3):2671–2701

    Article  Google Scholar 

  8. Cup K (2007) Available on: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  9. da Costa KA, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147–157

    Article  Google Scholar 

  10. Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer 49(8):112–116

    Article  Google Scholar 

  11. Deng L, Li D, Yao X, Cox D, Wang H (2019) Mobile network intrusion detection for iot system based on transfer learning algorithm. Clust Comput 22(4):9889–9904

    Article  Google Scholar 

  12. Deshpande A, Guestrin C, Madden SR, Hellerstein JM, Hong W (2004) Model-driven data acquisition in sensor networks. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases-Volume 30, VLDB Endowment, pp 588–599

  13. Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things. Future Gen Comput Syst 82:761–768

    Article  Google Scholar 

  14. Frahim J, Pignataro C, Apcar J, Morrow M (2015) Securing the internet of things: a proposed framework. Cisco White Paper

  15. Fu H, Vong CM, Wong PK, Yang Z (2016) Fast detection of impact location using kernel extreme learning machine. Neural Comput Appl 27(1):121–130

    Article  Google Scholar 

  16. Gavel S, Raghuvanshi AS, Tiwari S (2020a) Comparative study of anomaly detection in wireless sensor networks using different kernel functions. In: Advances in VLSI, Communication, and Signal Processing, Springer, pp 81–89

  17. Gavel S, Raghuvanshi AS, Tiwari S (2020b) A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks. Int J Netw Manag, p e2144

  18. Gavel S, Raghuvanshi AS, Tiwari S (2020c) A novel density estimation based intrusion detection technique with Pearson’s divergence for wireless sensor networks. In: ISA Transactions

  19. Hsieh CJ, Si S, Dhillon IS (2014) Fast prediction for large-scale kernel machines. In: NIPS, Citeseer, pp 3689–3697

  20. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  21. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B (Cybern) 42(2):513–529

    Article  Google Scholar 

  22. Ibrahim MH (2016) Octopus: an edge-fog mutual authentication scheme. IJ Netw Secur 18(6):1089–1101

    Google Scholar 

  23. Jamei M, Stewart E, Peisert S, Scaglione A, McParland C, Roberts C, McEachern A (2016) Micro synchrophasor-based intrusion detection in automated distribution systems: toward critical infrastructure security. IEEE Internet Comput 20(5):18–27

    Article  Google Scholar 

  24. Joachims T, Yu CNJ (2009) Sparse kernel svms via cutting-plane training. Mach Learn 76(2):179–193

    Article  Google Scholar 

  25. Kashef S, Nezamabadi-pour H (2015) An advanced aco algorithm for feature subset selection. Neurocomputing 147:271–279

    Article  Google Scholar 

  26. Kim J, Shin N, Jo SY, Kim SH (2017) Method of intrusion detection using deep neural network. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE, pp 313–316

  27. Kolias C, Kambourakis G, Stavrou A, Gritzalis S (2016) Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun Surv Tutor 18(1):184–208

    Article  Google Scholar 

  28. Li W, Tug S, Meng W, Wang Y (2019) Designing collaborative blockchained signature-based intrusion detection in iot environments. Future Gen Comput Syst 96:481–489

    Article  Google Scholar 

  29. Luo J, Vong CM, Wong PK (2014) Sparse Bayesian extreme learning machine for multi-classification. IEEE Trans Neural Netw Learn Syst 25(4):836–843

    Article  Google Scholar 

  30. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  31. Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Article  Google Scholar 

  32. Mohammadi FG, Abadeh MS (2014) Image steganalysis using a bee colony based feature selection algorithm. Eng Appl Artif Intell 31:35–43

    Article  Google Scholar 

  33. Moradi P, Gholampour M (2016) A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy. Appl Soft Comput 43:117–130

    Article  Google Scholar 

  34. Musolesi M, Hailes S, Mascolo C (2005) Adaptive routing for intermittently connected mobile ad hoc networks. In: Sixth IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks, IEEE, pp 183–189

  35. Ozdemir S, Xiao Y (2009) Secure data aggregation in wireless sensor networks: a comprehensive overview. Comput Netw 53(12):2022–2037

    Article  Google Scholar 

  36. Panigrahi R, Borah S (2018) A detailed analysis of cicids2017 dataset for designing intrusion detection systems. Int J Eng Technol 7(3.24):479–482

    Google Scholar 

  37. Pásztor B, Musolesi M, Mascolo C (2007) Opportunistic mobile sensor data collection with scar. In: 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems, IEEE, pp 1–12

  38. Ramos CC, Souza AN, Chiachia G, Falcao AX, Papa JP (2011) A novel algorithm for feature selection using harmony search and its application for non-technical losses detection. Comput Electr Eng 37(6):886–894

    Article  Google Scholar 

  39. 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 

  40. Rodrigues D, Pereira LA, Nakamura RY, Costa KA, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syste Appl 41(5):2250–2258

    Article  Google Scholar 

  41. Sarafrazi S, Nezamabadi-pour H (2013) Facing the classification of binary problems with a gsa-svm hybrid system. Math Comput Modell 57(1–2):270–278

    Article  MathSciNet  Google Scholar 

  42. Singh T, Kumar N (2020) Machine learning models for intrusion detection in Iot environment: a comprehensive review. Comput Commun. https://doi.org/10.1016/j.comcom.2020.02.001

    Article  Google Scholar 

  43. Song J, Takakura H, Okabe Y (2006) Description of kyoto university benchmark data. Available at link: http://www.takakura.com/Kyoto_data/BenchmarkData-Description-v5.pdf [Accessed on 15 March 2016]

  44. Stojmenovic I, Wen S (2014) The fog computing paradigm: Scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems, IEEE, pp 1–8

  45. Vaidyanathan K, Sur S, Narravula S, Sinha P (2004) Data aggregation techniques in sensor networks. Osu-cisrc-11/04-tr60, The Ohio State University

  46. Wei G, Ling Y, Guo B, Xiao B, Vasilakos AV (2011) Prediction-based data aggregation in wireless sensor networks: combining grey model and Kalman filter. Comput Commun 34(6):793–802

    Article  Google Scholar 

  47. Wu S, Wang Y, Cheng S (2013) Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102:163–175

    Article  Google Scholar 

  48. Yi S, Qin Z, Li Q (2015) Security and privacy issues of fog computing: A survey. In: International Conference on Wireless Algorithms, Systems, and Applications, Springer, pp 685–695

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Correspondence to Shashank Gavel.

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Gavel, S., Raghuvanshi, A.S. & Tiwari, S. Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT). J Supercomput 77, 10488–10511 (2021). https://doi.org/10.1007/s11227-021-03697-5

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