Abstract
Since the beginning of the Internet of Things (IoT), the number of IoT devices connected to the Internet has grown rapidly. However, many IoT devices lack the security standards that non-IoT devices have. This means that billions of smart devices could be used as part of a botnet attack or point of entry into a secured network. The potential to exploit an IoT device makes the search to find suitable IoT security measures extremely important. In order to fill this need, this study explores the use of machine learning in IoT security measures. Upon reviewing recent developments of machine learning in IoT security, it was found that the methods with the highest threat detection accuracy utilized the random forest and K-nearest neighbor algorithms and the most efficient methods utilized software-defined networks (SDN) and the fog layer of networks. In addition, the methods which determine the type of IoT device one is when it connects to a network primarily used the random forest algorithm. This study will take an in-depth look at the use of machine learning algorithms to detect malicious and anomalous data within IoT systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davis G (2018) 2020: life with 50 billion connected devices. In: 2018 IEEE international conference on consumer electronics (ICCE). https://doi.org/10.1109/icce.2018.8326056
Bull P, Austin R, Popov E, Sharma M, Watson R (2016) Flow based security for IoT devices using an SDN gateway. In: 2016 IEEE 4th international conference on future internet of things and cloud (FiCloud). https://doi.org/10.1109/ficloud.2016.30
Kolias C, Kambourakis G, Stavrou A, Voas J (2017) DDoS in the IoT: Mirai and other botnets. Computer 50(7):80–84. https://doi.org/10.1109/mc.2017.201
Mamdouh M, Elrukhsi MAI, Khattab A (2018) Securing the internet of things and wireless sensor networks via machine learning: a survey. In: 2018 international conference on computer and applications (ICCA). https://doi.org/10.1109/comapp.2018.8460440
Azmoodeh A, Dehghantanha A, Conti M et al (2018) Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J Ambient Intell Hum Comput 9:1141–1152
Ashton K (2009) That ‘internet of things’ thing. RFID J 22(7):97–114
Cenedese A, Zanella A, Vangelista L, Zorzi M (2014) Padova smart city: an urban internet of things experimentation. In: Proceeding of IEEE international symposium on a world of wireless, mobile and multimedia networks 2014. https://doi.org/10.1109/wowmom.2014.6918931
Zhang Z-K, Cho MCY, Wang C-W, Hsu C-W, Chen C-K, Shieh S (2014) IoT security: ongoing challenges and research opportunities. In: 2014 IEEE 7th international conference on service-oriented computing and applications. https://doi.org/10.1109/soca.2014.58
Raza S, Shafagh H, Hewage K, Hummen R, Voigt T (2013) Lithe: lightweight secure CoAP for the internet of things. IEEE Sens J 13(10):3711–3720. https://doi.org/10.1109/jsen.2013.2277656
Dorri A, Kanhere SS, Jurdak R (2016) Blockchain in internet of things: challenges and solutions. arXiv preprint arXiv:1608.05187
Gunn DJ et al (2019) Touch-based active cloud authentication using traditional machine learning and LSTM on a distributed tensorflow framework. Int J Comput Intell Appl 18:1950022:1–1950022:16
Mason J, Dave R, Chatterjee P, Graham-Allen I, Esterline A, Roy K (2020) An investigation of biometric authentication in the healthcare environment. Array 8:100042. https://doi.org/10.1016/j.array.2020.100042
Shelton J et al (2018) Palm print authentication on a cloud platform. In: 2018 international conference on advances in big data, computing and D
Kelley T, Furey E (2018) Getting prepared for the next botnet attack: detecting algorithmically generated domains in botnet command and control. In: 2018 29th Irish signals and systems conference (ISSC), Belfast, pp 1–6. https://doi.org/10.1109/ISSC.2018.8585344
Xiao L, Wan X, Lu X, Zhang Y, Wu D (2018) IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Signal Process Mag 35(5):41–49. https://doi.org/10.1109/MSP.2018.2825478
Kotenko I, Saenko I, Skorik F, Bushuev S (2015) Neural network approach to forecast the state of the internet of things elements. In: 2015 XVIII international conference on soft computing and measurements (SCM). https://doi.org/10.1109/scm.2015.7190434
Canedo J, Skjellum A (2016) Using machine learning to secure IoT systems. In: 2016 14th annual conference on privacy, security and trust (PST). https://doi.org/10.1109/pst.2016.7906930
Moh M, Raju R (2018) Machine learning techniques for security of internet of things (IoT) and fog computing systems. In: 2018 international conference on high performance computing & simulation (HPCS). https://doi.org/10.1109/hpcs.2018.00116
Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
Meidan Y et al (2017) Detection of unauthorized IoT devices using machine learning techniques. arXiv preprint arXiv:1709.04647
Meidan Y et al (2017) ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis. In: Proceedings of the symposium on applied computing
Doshi R, Apthorpe N, Feamster N (2018) Machine learning DDoS detection for consumer internet of things devices. In: 2018 IEEE security and privacy workshops (SPW). https://doi.org/10.1109/spw.2018.00013
Hindy H et al (2020) Machine learning based IoT intrusion detection system: an MQTT case study. arXiv preprint arXiv:2006.15340. https://doi.org/10.1016/j.iot.2019.100059
Alrashdi I et al (2019) AD-IoT: Anomaly detection of IoT cyberattacks in smart city using machine learning. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC). IEEE
Hasan M, Milon Islam M, Islam I, Hashem MMA (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 100059
Chhabra GS, Singh VP, Singh M (2020) Cyber forensics framework for big data analytics in IoT environment using machine learning. Multimed Tools Appl 79(23):15881–15900
Fang He, Qi A, Wang X (2020) Fast authentication and progressive authorization in large-scale IoT: how to leverage AI for security enhancement. IEEE Netw 34(3):24–29
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Strecker, S., Van Haaften, W., Dave, R. (2021). An Analysis of IoT Cyber Security Driven by Machine Learning. In: Kumar, S., Purohit, S.D., Hiranwal, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3246-4_55
Download citation
DOI: https://doi.org/10.1007/978-981-16-3246-4_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3245-7
Online ISBN: 978-981-16-3246-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)