A Review on Machine Learning-based Malware Detection Techniques for Internet of Things (IoT) Environments
Pages 1961 - 1974
Abstract
Internet of Things (IoT) is the recent digital trend that connects the physical and virtual world. The strong bonding between the people, objects, machines and the web are assisting to develop new business models and also ensuring a better communication framework. On the other side, IoT devices are the main targets for cybercriminals that take vulnerable action over the authentication model, outdated data services and the malware. Henceforth, the security metrics of IoT devices is explored by several researchers while focusing on IoT malware. Many studies on the security issues for IoT systems are explored. Specifically, the employment of Machine learning techniques used for detecting the IoT malwares is studied. In this paper, a detailed survey on detecting the IoT malware using ML techniques are presented. Initially, the fundamentals of the malware analysis and the process and tools used to identify the malwares are discussed. The main intention of this survey is to support the security analysts who are interested to understand and innovate new trends in ML for IoT devices. This study is categorized into two groups, namely, machine learning techniques and neural networks. Both the groups are reviewed from the aspects of preprocessing and feature extraction process of the suggested ML techniques. The study ends the research issues in this field from the aspects of evaluating the performance of methods, as dataset collection, parameter optimization, neural network structure, throughput and scalability.
References
[1]
Koppula S and Muthukuru J Secure Digital Signature Scheme Based on Elliptic Curves for Internet of Things International Journal of Electrical and Computer Engineering 2016 6 3 1002
[2]
Su X, Wang Z, Liu X, Choi C, and Choi D Study to improve security for IoT smart device controller: Drawbacks and countermeasures Security and Communication Networks 2018 2018 1-4
[3]
Alizai, Z. A., Tareen, N. F., & Jadoon, I. (2018, September). Improved IoT device authentication scheme using device capability and digital signatures. In 2018 International Conference on Applied and Engineering Mathematics (ICAEM) (pp. 1–5). IEEE.
[4]
Farooq, Hasan, Baig, & Shehzad UNUIN Efficient adaptive framework for securing the internet of things devices EURASIP Journal on Wireless Communications and Networking 2019 2019 1 210
[5]
Henriques, M. S., & Vernekar, N. K. (2017, May). Using symmetric and asymmetric cryptography to secure communication between devices in IoT. In 2017 International Conference on IoT and Application (ICIOT) (pp. 1–4). IEEE.
[6]
Rajesh S, Paul V, Menon VG, and Khosravi MR A secure and efficient lightweight symmetric encryption scheme for transfer of text files between embedded IoT devices Symmetry 2019 11 2 293
[7]
Malina L, Hajny J, Fujdiak R, and Hosek J On perspective of security and privacy-preserving solutions in the internet of things Computer Networks 2016 102 83-95
[8]
Hammi MT, Hammi B, Bellot P, and Serhrouchni A Bubbles of Trust: A decentralized blockchain-based authentication system for IoT Computers & Security 2018 78 126-142
[9]
P. Pabitha, J. Chandra Priya, R. Praveen, S. Jagatheswari. (2023). ModChain: a hybridized secure and scaling blockchain framework for IoT environment. International Journal of Information Technology, 15(3), 1741-1754.
[10]
Ramalingam, P., & Pabitha, P. (2022). Cryptanalysis of biometric based secure user authentication protocol for IoT applications. In 2022 3rd International Conference on Communication, Computing and Industry 4.0 (C2I4) (pp. 1–6). IEEE.
[11]
Sudhakar, T., Ramalingam, P., & Jagatheswari, S. (2022). An improved proxy-vehicle based authentication scheme for vehicular ad-hoc networks. International Journal of Information Technology, 14(5), 2441–2449.
[12]
Dang TK, Pham CD, and Nguyen TL A pragmatic elliptic curve cryptography-based extension for energy-efficient device-to-device communications in smart cities Sustainable Cities and Society 2020 56 102097
[13]
Suárez-Albela M, Fraga-Lamas P, and Fernández-Caramés TM A practical evaluation on RSA and ECC-based cipher suites for IoT high-security energy-efficient fog and mist computing devices Sensors (Basel, Switzerland) 2018 18 11 3868
[14]
Capossele, A., Cervo, V., De Cicco, G., & Petrioli, C. (2015, June). Security as a CoAP resource: an optimized DTLS implementation for the IoT. In 2015 IEEE international conference on communications (ICC) (pp. 549–554). IEEE.
[15]
Praveen, Ramalingam & P. Pabitha. (2023). ASK-RAM-IMOT: Autonomous Shared Keys based Remote Authentication Method for Internet of Medical Things Applications. Wireless Personal Communications, 131(1), 273–293.
[16]
Praveen, R., & Pabitha, P. (2023). Improved Gentry–Halevi’s fully homomorphic encryption‐based lightweight privacy preserving scheme for securing medical Internet of Things. Transactions on Emerging Telecommunications Technologies, 34(4),
[17]
R. Praveen and P. Pabitha A secure lightweight fuzzy embedder based user authentication scheme for internet of medical things applications Journal of Intelligent & Fuzzy Systems 2023 44 5 7523-7542
[18]
Das AK, Wazid M, Yannam AR, Rodrigues JJ, and Park Y Provably secure ECC-based device access control and key agreement protocol for IoT environment Ieee Access : Practical Innovations, Open Solutions 2019 7 55382-55397
[19]
Li W A new intrusion detection system based on KNN classification algorithm in wireless sensor network Journal of Electrical and Computer Engineering 2014 2014 240217
[20]
Ahmed, M. M. (2018). An Optimized K-Nearest Neighbor Algorithm for Extending Wireless Sensor Network Lifetime. International Conference on Advanced Machine Learning Technologies and Applications. Springer, Cham.
[21]
Azmoodeh A Detecting crypto-ransomware in iot networks based on energy consumption footprint Journal of Ambient Intelligence and Humanized Computing 2017 9 1141-1152
[22]
Peng, Y. (2016). “An iterative weighted KNN (IW-KNN) based indoor localization method in bluetooth low energy (BLE) environment.“ Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Intl IEEE Conferences. IEEE, 2016.
[23]
Whitehouse, K. The design of calamari: an ad-hoc localization system for sensor networks (Doctoral dissertation, Master’s thesis, University of California at Berkeley).
[24]
Kwon, Y., Mechitov, K., Sundresh, S., Kim, W., & Agha, G. (Resilient localization for sensor netwoks in outdoor environments. InDistributed Computing Systems, 2005. ICDCS 2005. Proceedings. 25th IEEE International Conference on 2005). Jun 10 (pp. 643–652). IEEE.
[25]
Tran DA Localization in wireless sensor networks based on support vector machines IEEE Transactions on Parallel and Distributed Systems 2008 19 7 981-994
[26]
Zhang Y, Meratnia N, and Havinga PJ Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine Ad hoc networks 2013 11 3 1062-1074
[27]
Ham HS, Kim HH, Kim MS, and Choi MJ Linear SVM-based android malware detection for reliable IoT services Journal of Applied Mathematics. 2014 2014 594501
[28]
Srinivas, M.S., & Rao Emani, B. B. (2017) Epilepsy Seizure Detection using IoT and Support Vector Machine with spline
[29]
Lau, Bill CP, Eden WM, Ma, Tommy WS, and Chow Probabilistic fault detector for wireless sensor network Expert Systems with Applications 2014 41 8 3703-3711
[30]
Shinde, T. A., Jayashree, R., & Prasad (2017). IoT based Animal Health Monitoring with Naive Bayes Classification IJETT 1.2.
[31]
Jogarah KK et al. Hybrid machine learning algorithms for fault detection in android smartphones Transactions on Emerging Telecommunications Technologies 2018 29 2 e3272
[32]
Cheng X et al. Hierarchical distributed data classification in wireless sensor networks Computer Communications 2010 33 12 1404-1413
[33]
Edwards-Murphy F et al. B + WSN: Smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring Computers and Electronics in Agriculture 2016 124 211-219
[34]
Yang J Botanical internet of things: Toward smart indoor farming by connecting people, plant, data and clouds Mobile Networks and Applications 2017 23 188-202
[35]
Ilapakurti, A., Vuppalapati C. (2015) Building an IoT framework for connected dairy. Big Data Computing Service and Applications (BigDataService), 2015 IEEE First International Conference on IEEE.
[36]
Xiao-Mei, L., & Yong, Q. (2019) Research on LED lightweight cryptographic algorithm based on RFID tag of Internet of things. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) (pp. 1717–1720). IEEE.
[37]
Fiore U, Palmieri F, Castiglione A, and De Santis A Network anomaly detection with the restricted Boltzmann machine Neurocomputing 2013 122 13-23
[38]
Gao, N., Gao, L., Gao, Q., & Wang, H. (2014). An intrusion detection model based on deep belief networks. In Proc. of IEEE International Conference on Advanced Cloud and Big Data, 247–252.
[39]
Alom, Z., Bontupalli, V., & Taha, T. M. (2015). Intrusion detection using deep belief networks. In Proc. of IEEE NAECON, 339–344.
[40]
Ding, Y., Chen, S., & Xu, J. (2016). Application of deep belief networks for opcode based malware detection. In Proc. of IJCNN, 3901–3908.
[41]
Erfani SM, Rajasegarar S, Karunasekera S, and Leckie C High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning Pattern Recognition 2016 58 121-134
[42]
Kang MJ and Kang JW Intrusion detection system using deep neural network for in-vehicle network security PloS ONE 2016 11 1-17
[43]
Zhang H, Li Y, Lv Z, Sangaiah AK, and Huang T A real-time and ubiquitous network attack detection based on deep belief network and support vector machine IEEE/CAA Journal of Automatica Sinica 2020 7 790-799
[44]
Qu, F., Zhang, J., Shao, Z., & Qi, S. (2017). An intrusion detection model based on deep belief network. In Proc. of ICNCC, 97–101.
[45]
Huda S, Miah S, Yearwood J, and Alyahya S A malicious threat detection model for cloud assisted internet of things (CoT) based industrial control system (ICS) networks using deep belief network Journal of Parallel and Distributed Computing 2018 120 23-31
[46]
Gondalia A, Dixit D, Parashar S, Raghava V, Sengupta A, and Sarobin VR IoT-based Healthcare Monitoring System for War Soldiers using machine learning Procedia computer science 2018 133 1005-1013
[47]
Dawoud A, Shahristani S, and Raun C Deep learning and software-defined networks: Towards secure IoT architecture Internet of Things 2018 3 82-89
[48]
Sarker IH A machine learning based robust prediction model for real-life mobile phone data Internet of Things 2019 5 180-193
[49]
Zolanvari M, Teixeira MA, Gupta L, Khan KM, and Jain R Machine learning based network vulnerability analysis of industrial internet of things IEEE Internet of Things Journal 2019 6 4 6822-6834
[50]
Hasan M, Islam MM, Zarif MII, and Hashem M Attack and anomaly detection in iot sensors in iot sites using machine learning approaches Internet of Things 2019 7 100059
[51]
McGinthy JM, Wong LJ, and Michaels AJ Groundwork for neural network-based specific Emitter Identification Authentication for IoT IEEE Internet of Things Journal 2019 6 4 6429-6440
[52]
Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P. (2017). Blockchain for IoT security and privacy: The case study of a smart home, in: IEEE international conference on pervasive computing and communications workshops (PerCom workshops), IEEE, 2017, pp. 618–623.
[53]
Sadique KM, Rahmani R, and Johannesson P Towards security on internet of things: Applications and Challenges in Technology Procedia Computer Science 2018 141 199-206
[54]
Banerjee M, Lee J, and Choo KKR Super Artificial Intelligence Medical Care Systems with IoT Wireless Sensor International Conference on Distributed Computing and Electrical Circuits and Electronics 2018 4 1-4
[55]
Dedeoglu, V., Jurdak, R., Dorri, A., Lunardi, R., Michelin, R., Zorzo, A., & Kanhere, S. (2020). Blockchain technologies for iot (pp. 55–89). In: Advanced Applications of Blockchain Technology, Springer.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
Published: 24 August 2023
Accepted: 24 July 2023
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