Abstract
The scoping review reported by this paper aimed to analyze and synthesize state-of-the-art studies focused on the application of machine learning methods to enhance the cyber resilience of cyber-physical systems. An electronic search was conducted, and 24 studies were included in this review after the selection process. The most representative application domains were computer networks and power systems, while in terms of cyber resilience functions, risk identification, risk mitigation or protection, and detection of anomalous situations were the most implemented functions. Moreover, the results of this scoping review show that the interest in the topic of cyber resilience and machine learning is quite recent, which justifies the heterogeneity of the included studies in terms of machine learning methods and datasets being used for the experimental validations, as well as in terms of outcomes being measured.
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
Similar content being viewed by others
References
Ahmadi-Assalemi G, Al-Khateeb H, Epiphaniou G, Aggoun A (2022) Super learner ensemble for anomaly detection and cyber-risk quantification in industrial control systems. IEEE Internet Things J 9(15):13279–13297
Aliyu I, Van Engelenburg S, Mu’Azu MB, Kim J, Lim CG (2022) Statistical detection of adversarial examples in blockchain-based federated forest in-vehicle network intrusion detection systems. IEEE Access 10:109366–109384
Almuhammadi S, Alsaleh M (2017) Information security maturity model for NIST cyber security framework. Comput Sci Inform Technol 7(3):51–62
Barker E, Dang Q (2016) NIST special publication 800–57 part 1, revision 4. NIST, Tech Rep 16
Butt UA, Mehmood M, Shah SBH, Amin R, Shaukat MW, Raza SM et al (2020) A review of machine learning algorithms for cloud computing security. Electronics 9(9):1379
D’Hooge L, Wauters T, Volckaert B, De Turck F (2019) Classification hardness for supervised learners on 20 years of intrusion detection data. IEEE Access 7:167455–167469
Dabbaghjamanesh M, Moeini A, Senemmar S, Zhang J (2021) Resiliency enhancement of distribution power grids using mobile marine power source. IEEE Trans Indus Appl
Del Fabro L, Bondi E, Serio F, Maggioni E, D’Agostino A, Brambilla P (2023) Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 13(1):75
Elmarady AA, Rahouma K (2021) Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B. Chin J Aeronaut 34(2):217–228
Gyawali S, Qian Y, Hu RQ (2021) A privacy-preserving misbehavior detection system in vehicular communication networks. IEEE Trans Veh Technol 70(6):6147–6158
Hassan MM, Gumaei A, Huda S, Almogren A (2020) Increasing the trustworthiness in the industrial IoT networks through a reliable cyberattack detection model. IEEE Trans Industr Inf 16(9):6154–6162
He Z, Khazaei J, Moazeni F, Freihaut JD (2022) Detection of false data injection attacks leading to line congestions using neural networks. Sustain Cities Soc 82:103861
Katzir Z, Elovici Y (2018) Quantifying the resilience of machine learning classifiers used for cyber security. Expert Syst Appl 92:419–429
Kaur K, Dhir R, Ouaissa M (2023) SSAMH–a systematic survey on ai-enabled cyber physical systems in healthcare. Convergence of cloud with AI for big data analytics: foundations and innovation. Wiley, Hoboken, pp 277–297
Khan F, Alturki R, Rahman MA, Mastorakis S, Razzak I, Shah ST (2022) Trustworthy and reliable deep-learning-based cyberattack detection in industrial IoT. IEEE Trans Industr Inf 19(1):1030–1038
Kim S, Park KJ (2021) A survey on machine-learning based security design for cyber-physical systems. Appl Sci 11(12):5458
Kim H, Kim SH, Hwang JY, Seo C (2019) Efficient privacy-preserving machine learning for blockchain network. IEEE Access 7:136481–136495
Lakshminarayana S, Karachiwala JS, Teng TZ, Tan R, Yau DK (2019) Performance and resilience of cyber-physical control systems with reactive attack mitigation. IEEE Trans Smart Grid 10(6):6640–6654
Mills R, Marnerides AK, Broadbent M, Race N (2022) Practical intrusion detection of emerging threats. IEEE Trans Netw Serv Manage 19(1):582–600
Pashamokhtari A, Batista G, Gharakheili HH (2022) AdIoTack: quantifying and refining resilience of decision tree ensemble inference models against adversarial volumetric attacks on IoT networks. Comput Secur 120:102801
Ramani S, Jhaveri RH (2022) ML-based delay attack detection and isolation for fault-tolerant software-defined industrial networks. Sensors 22(18):6958
Said D, Elloumi M, Khoukhi L (2022) Cyber-attack on P2P energy transaction between connected electric vehicles: a false data injection detection based machine learning model. IEEE Access 10:63640–63647
Savaliya A, Jhaveri RH, Xin Q, Alqithami S, Ramani S, Ahanger TA (2021) Securing industrial communication with software-defined networking. Math Biosci Eng 18(6):8298–8314
Serrano W (2021) The blockchain random neural network for cybersecure IoT and 5G infrastructure in smart cities. J Netw Comput Appl 175:102909
Vivek S, Conner H (2022) Urban road network vulnerability and resilience to large-scale attacks. Saf Sci 147:105575
Wang P, Govindarasu M (2020) Multi-agent based attack-resilient system integrity protection for smart grid. IEEE Trans Smart Grid 11(4):3447–3456
Wang W, Harrou F, Bouyeddou B, Senouci SM, Sun Y (2022) A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Clust Comput 25:561–578
Wazid M, Das AK, Chamola V, Park Y (2022) Uniting cyber security and machine learning: advantages, challenges and future research. ICT Expr 8(3):313–321
Wen J, Zhao BZH, Xue M, Oprea A, Qian H (2021) With great dispersion comes greater resilience: efficient poisoning attacks and defenses for linear regression models. IEEE Trans Inf Forensics Secur 16:3709–3723
Xue D, Jing X, Liu H (2019) Detection of false data injection attacks in smart grid utilizing ELM-based OCON framework. IEEE Access 7:31762–31773
Zhang J, Tai Y (2022) Secure medical digital twin via human-centric interaction and cyber vulnerability resilience. Connect Sci 34(1):895–910
Ziegler V, Schneider P, Viswanathan H, Montag M, Kanugovi S, Rezaki A (2021) Security and trust in the 6G Era. IEEE Access 9:142314–142327
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pavão, J., Bastardo, R., Rocha, N.P. (2024). Cyber Resilience of Cyber-Physical Systems and Machine Learning, a Scoping Review. In: Ullah, A., Anwar, S., Calandra, D., Di Fuccio, R. (eds) Proceedings of International Conference on Information Technology and Applications. ICITA 2022. Lecture Notes in Networks and Systems, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-99-8324-7_42
Download citation
DOI: https://doi.org/10.1007/978-981-99-8324-7_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8323-0
Online ISBN: 978-981-99-8324-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)