International Journal of Nonlinear Analysis and Applications
Review on machine learning and deep learning algorithms for IoT security
Document Type : Research Paper
Author
Department of Computer Science, University of Kufa, Najaf, Iraq
Abstract
With its rapid expansion in more sectors, for instance, wearables, smart sensors, plus house devices, the Internet of Things (IoT) is drifted to have a significant influence on many parts of our life. IoT devices stand out for their connectivity, ubiquity, and low processing power. By 2025, there will likely be 30.9 billion devices adjoined to the Internet, since the count of IoT devices in use worldwide is growing quickly. This eruption about IoT devices, which in analogy to desktop PCs, can be quickly increased, has caused an increase in occurrences of IoT-based cyber intrusions. It is necessary to create new methods for identifying attacks launched from hacked IoT devices in order to address this challenge. The best detective control solution against attacks caused by IoT devices, in this context, uses machine and deep learning approaches. This paper attempts some analysis of technologies, threats arising from IoT devices, and intrusion detection system overview as they associate with IoT systems. The investigation of several machine learning plus deep learning concepts appropriate for identifying IoT devices linked with cyberattacks is also included in this paper.
Keywords
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[46] Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, and G. Cottrell, A dual-stage attention-based recurrent neural network for time series prediction, arXiv Prepr. arXiv1704.02971 (2017).
[47] R. Zaheer R. Khan, S.U. Khan and S. Khan, Future internet: the internet of things architecture, possible applications and key challenges, 10th Int. Conf. Front. Inf. Technol., 2012, pp. 257–260.
[48] B. Rajagopalan and U. Lall, A k-nearest-neighbor simulator for daily precipitation and other weather variables, Water Resources Res. 35 (1999), no. 10, 3089–3101.
[49] A. Rajan, J. Jithish, and S. Sankaran, Sybil attack in IOT: Modelling and defenses, Int. Conf. Adv. Comput. Commun. Inf.(ICACCI), IEEE, 2017, pp. 2323–2327.
[50] Syed Rizvi, Aaron Kurtz, Joshua Pfeffer, and Mohammad Rizvi, Securing the internet of things (IoT): A securitytaxonomy for IoT, 17th IEEE Int. Conf. Trust, Secur. Privacy Comput. Commun./12th IEEE Int. Conf. Big Data Sci. Engin. (TrustCom/BigDataSE), IEEE, 2018, pp. 163–168.
[51] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors, Nature 323 (1986), no. 6088, 533–536.
[52] C. Ryan, J.J. Collins, and M.O’N. Neill, Grammatical evolution: Evolving programs for an arbitrary language, Eur. Conf. Genetic Program., 1998, pp. 83–96.
[53] T. Saba, T. Sadad, A. Rehman, Z. Mehmood, and Q. Javaid, Intrusion detection system through advance machine learning for the internet of things networks, IT Profess. 23 (2021), no. 2, 58–64.
[54] S. Samonas and D. Coss, The CIA strikes back: Redefining confidentiality, integrity and availability in security, J. Inf. Syst. Secur. 10 (2014), no. 3.
[55] B. Sch¨olkopf, Z. Luo, and V. Vovk, Empirical inference: Festschrift in honor of Vladimir N. Vapnik, Springer Science & Business Media, 2013.
[56] D.T. Shipmon, J.M. Gurevitch, P.M. Piselli, and S.T. Edwards, Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data, arXiv Prepr. arXiv1708.03665 (2017).
[57] K. Sonar and H. Upadhyay, A survey: Ddos attack on internet of things, Int. J. Eng. Res. Dev. 10 (2014), no. 11, 58–63.
[58] S. Tong and D. Koller, Support vector machine active learning with applications to text classification, J. Mach.
Learn. Res. 2 (2001), no. Nov, 45–66
[59] A. Torkaman and M.A. Seyyedi, Analyzing iot reference architecture models, Int. J. Comput. Sci. Softw. Eng. 5 (2016), no. 8, 154.
[60] P. Torres, C. Catania, S. Garcia, and C.G. Garino, An analysis of recurrent neural networks for botnet detection behavior, IEEE Biennial Cong. Argentina (ARGENCON), 2016, pp. 1–6.
[61] S. Vashi, J. Ram, J. Modi, S. Verma, and C. Prakash, Internet of things (IoT): A vision, architectural elements, and security issues, Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2017, pp. 492–496.
[62] L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, Iot security techniques based on machine learning: How do iot devices use AI to enhance security?, IEEE Signal Process. Mag. 35 (2018), no. 5, 41–49.
[63] L. Yang, C. Ding, M. Wu, and K. Wang, Robust detection of false data injection attacks for data aggregation in an internet of things-based environmental surveillance, Computer Networks 129 (2017), 410–428.
[64] I. Yaqoob, H. Alasmary, A. Alashaikh, E. Ahmed, H. Song, and J.J.P.C. Rodrigues, The rise of ransomware and emerging security challenges in the internet of things, Comput. Networks 129 (2017), 444–458.
[65] Y. Yang Y. Peng X. Wang Z. Yang, Y. Yue and W. Liu, Study and application on the architecture and key technologies for IOT, Int. Conf. Multimedia Technol., 2011, pp. 747–751.
[66] Y. Zhang, Y. Shen, H. Wang, J. Yong, and X. Jiang, On secure wireless communications for iot under eavesdropper collusion, IEEE Trans. Autom. Sci. Eng. 13 (2015), no. 3, 1281–1293.
[67] K. Zhao and L. Ge, A survey on the internet of things security, Ninth Int. Conf. Comput. Intell. Secur., 2013,pp. 663 667.
[68] L. Zhu and N. Laptev, Deep and confident prediction for time series at uber, IEEE Int. Conf. Data Min. Workshops (ICDMW), 2017, pp. 103–110.
)
Volume 14, Issue 5
May 2023Pages 27-35
- Receive Date: 11 December 2022
- Revise Date: 26 January 2023
- Accept Date: 14 March 2023