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A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security

Yıl 2024, Cilt: 12 Sayı: 1, 1 - 28, 21.06.2024
https://doi.org/10.51354/mjen.1197753

Öz

IIoT “Industrial Internet of Things” refers to a subset of Internet of Things technology designed for industrial processes and industrial environments. IIoT aims to make manufacturing facilities, energy systems, transportation networks, and other industrial systems smarter, more efficient and connected. IIoT aims to reduce costs, increase productivity, and support more sustainable operations by making industrial processes more efficient. In this context, the use of IIoT is increasing in production, energy, healthcare, transportation, and other sectors. IoT has become one of the fastest-growing and expanding areas in the history of information technology. Billions of devices communicate with the Internet of Things with almost no human intervention. IIoT consists of sophisticated analysis and processing structures that handle data generated by internet-connected machines. IIoT devices vary from sensors to complex industrial robots. Security measures such as patch management, access control, network monitoring, authentication, service isolation, encryption, unauthorized entry detection, and application security are implemented for IIoT networks and devices. However, these methods inherently contain security vulnerabilities. As deep learning (DL) and machine learning (ML) models have significantly advanced in recent years, they have also begun to be employed in advanced security methods for IoT systems. The primary objective of this systematic survey is to address research questions by discussing the advantages and disadvantages of DL and ML algorithms used in IoT security. The purpose and details of the models, dataset characteristics, performance measures, and approaches they are compared to are covered. In the final section, the shortcomings of the reviewed manuscripts are identified, and open issues in the literature are discussed.

Kaynakça

  • [1] L.S. Vailshery, Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2023, with forecasts from 2022 to 2030, https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/ , Statista, Last accessed: October 31, 2021.
  • [2] M. Hatton, The IoT in 2030: Which applications account for the biggest chunk of the $1.5 trillion opportunity? TransformaInsights, https://www.kisa.link/PsHW, Last accessed: October 31, 2021..
  • [3] F. Meneghello, et al., IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices, IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8182–8201, 2019.
  • [4] C. Xenofontos, et al. Consumer, commercial and industrial iot (in) security: attack taxonomy and case studies. IEEE Internet of Things Journal, 2021.
  • [5] D. Antonioli, et al., Blurtooth: Exploiting cross-transport key derivation in bluetooth classic and bluetooth low energy, arXiv preprint arXiv:2009.11776, 2020.
  • [6] L. L. Dhirani, E. Armstrong, and T. Newe, Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap. Sensors, 21(11), 3901, 2021
  • [7] A. R. Sadeghi, C. Wachsmann, & M. Waidner, Security and privacy challenges in industrial internet of things. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE, June 2015.
  • [8] J. P. Anderson, Computer security threat monitoring and surveillance, Technical Report, James P. Anderson Company, 1980
  • [9] B. B. Zarpelão, et al, A survey of intrusion detection in Internet of Things, Journal of Network and Computer Applications, Volume 84, Pages 25-37, ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2017.02.009, 2017
  • [10] E. Hodo, et al, Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE, May 2016.
  • [11] E. Anthi, et al, A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), 9042-9053, 2019.
  • [12] S. Raza, L. Wallgren, & T. Voigt, SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674, 2013.
  • [13] V. Kumar, A. K. Das, & D. Sinha, UIDS: A unified intrusion detection system for IoT environment. Evolutionary Intelligence, 14(1), 47-59, 2021.
  • [14] M. Eskandari, et al, Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal, 7(8), 6882-6897, 2020.
  • [15] E. Aydogan, et al. A central intrusion detection system for rpl-based industrial internet of things. In 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS) (pp. 1-5). IEEE, May 2019.
  • [16] M. Zolanvari, et al., Machine learning-based network vulnerability analysis of industrial Internet of Things. IEEE Internet of Things Journal, 6(4), 6822-6834, 2019.
  • [17] J. B. Awotunde, C. Chakraborty, & A. E. Adeniyi, Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection. Wireless Communications and Mobile Computing, 2021.
  • [18] A. H. Muna, N. Moustafa & E. Sitnikova, Identification of malicious activities in industrial internet of things based on deep learning models. Journal of Information security and applications, 41, 1-11, 2018.
  • [19] G. E. I. Selim, et al. Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms. Multimedia Tools and Applications, 80(8), 12619- 12640, 2021.
  • [20] A. F. M. Agarap, A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. In Proceedings of the 2018 10th international conference on machine learning and computing (pp. 26-30). 2018, February.
  • [21] S. Aljawarneh, M. Aldwairi, & M. B. Yassein. Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152-160. 2018.
  • [22] L. Breiman, et al, Classification and regression trees. Routledge. 2017.
  • [23] L. Li, H. Zhang, H. Peng, & Y. Yang, Nearest neighbors based density peaks approach to intrusion detection. Chaos, Solitons & Fractals, 110, 33-40. 2018.
  • [24] A. L. Buczak & E. Guven. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176. 2015.
  • [25] A. P. Muniyandi, R. Rajeswari, & R. Rajaram, Network anomaly detection by cascading k-Means clustering and C4. 5 decision tree algorithm. Procedia Engineering, 30, 174-182. 2012.
  • [26] R. Vinayakumar, K. P. Soman, & P. Poornachandran, Applying convolutional neural network for network intrusion detection. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1222-1228). IEEE. September, 2017.
  • [27] A. A. Diro, & N. Chilamkurti. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768. 2018.
  • [28] J. Kim, et al. Long short term memory recurrent neural network classifier for intrusion detection. In 2016 International Conference on Platform Technology and Service (PlatCon) (pp. 1-5). IEEE. (2016, February).
  • [29] P. Liu, X. Qiu, & X. Huang, X. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101. 2016.
  • [30] M. Yousefi-Azar, et al. Autoencoder-based feature learning for cyber security applications. In 2017 International joint conference on neural networks (IJCNN) (pp. 3854-3861). IEEE. (2017, May).
  • [31] T. Salimans, et al. Improved techniques for training gans. Advances in neural information processing systems, 29, 2234-2242. 2016.
  • [32] U. Fiore, et al. Network anomaly detection with the restricted Boltzmann machine. Neurocomputing, 122, 13-23. 2013.
  • [33] Y. Zhang, P. Li, & X. Wang, Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722. 2019.
  • [34] K. Tange, et al. Towards a systematic survey of industrial IoT security requirements: research method and quantitative analysis, Proceedings of the Workshop on Fog Computing and the IoT, 2019.
  • [35] K. Tange, et al, A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities, in IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2489-2520, Fourthquarter 2020.
  • [36] T. Soo Fun, & A. Samsudin, Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey. Sensors, 21(19), 6647. 2021.
  • [37] S. Bhatt, & P.R. Ragiri, Security trends in Internet of Things: A survey. SN Applied Sciences, 3(1), 1-14. 2021.
  • [38] M. Serror, et al, Challenges and Opportunities in Securing the Industrial Internet of Things, IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 2985-2996, doi: 10.1109/TII.2020.3023507, May 2021.
  • [39] Y. Wu, et al. Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks, 191, 108004. 2021.
  • [40] K. Tsiknas, et al, Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures. IoT, 2(1), 163- 188, 2021.
  • [41] M. A. Al-Garadi, et al, A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security, IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020.
  • [42] R. A. Khalil, et al. Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications, IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11016-11040, 15 July15, 2021.
  • [43] R. Ahmad & I. Alsmadi, Machine learning approaches to IoT security: A systematic literature review. Internet of Things, 100365. 2021.
  • [44] L. Aversano, et al. A systematic review on Deep Learning approaches for IoT security. Computer Science Review, 40, 100389. 2021
  • [45] Rudenko, R., Pires, I. M., Oliveira, P., Barroso, J., & Reis, A. (2022). A Brief Review on Internet of Things, Industry 4.0 and Cybersecurity. Electronics, 11(11), 1742.
  • [46] Ahanger, T. A., Aljumah, A., & Atiquzzaman, M. (2022). State-of-the-art survey of artificial intelligent techniques for IoT security. Computer Networks, 108771.
  • [47] L. Tan and N. Wang, Future internet: The Internet of Things, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), pp. V5-376-V5-380, 2010
  • [48] F. A. Alaba, et al, Internet of Things security: A survey, J. Netw. Comput. Appl., 88, 10–28, 2017.
  • [49] H. Boyes, et al. The industrial internet of things (IIoT): An analysis framework. Computers in industry, 101, 1-12. 2018.
  • [50] J. Sengupta, S. Ruj & S. D. Bit, A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, 102481. 2020.
  • [51] U. Saxena, J. S Sodhi, & Y. Singh. An Analysis of DDoS Attacks in a Smart Home Networks. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 272-276). IEEE. January 2020.
  • [52] S. Alzahrani and L. Hong, Generation of DDoS attack dataset for effective IDS development and evaluation, J. Inf. Secur. 9 (4), 225–241, 2018.
  • [53] Y. Gu, et al, Semi-supervised K-means DDoS detection method using hybrid feature selection algorithm, IEEE Access 7, 64351–64365, 2019.
  • [54] Y.N. Soe, et al, DDoS attack detection based on simple ANN with SMOTE for IoT environment, in: 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 1–5, 2019.
  • [55] N. Chaabouni, et al, Network intrusion detection for iot security based on learning techniques, IEEE Commun. Surv. Tutor. 21 (3), 2671–2701, 2019.
  • [56] P. García-Teodoro, et al, Anomaly-based network intrusion detection: techniques, systems and challenges, Comput. Secur. 28 (1), 18–28, 2009.
  • [57] I. Andrea, C. Chrysostomou, G. Hadjichristofi Internet of things: security vulnerabilities and challenges, 2015 IEEE Symposium on Computers and Communication (ISCC), ), pp. 180-187, 2015.
  • [58] M.M. Ahemd, M.A. Shah, A. Wahid, Iot security: a layered approach for attacks and defenses, 2017 International Conference on Communication Technologies (ComTech), pp. 104-110, 2017.
  • [59] M. R. Bartolacci, et al, Personal denial of service (PDOS) attacks: A discussion and exploration of a new category of cyber crime. Journal of Digital Forensics, Security and Law, 9(1), 2. 2014.
  • [60] M.N. Aman, et al, A light-weight mutual authentication protocol for iot systems, GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1-6, 2017.
  • [61] T. Gomes, et al, Cute mote, a customizable and trustable end-device for the internet of things, IEEE Sens. J., 17 (20), pp. 6816-6824, 2017.
  • [62] P. Porambage, et al, Pauthkey: a pervasive authentication protocol and key establishment scheme for wireless sensor networks in distributed iot applications, Int. J. Distributed Sens. Netw., 10 (7), 2014.
  • [63] X. Hei, et al, Defending resource depletion attacks on implantable medical devices, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1-5. 2010.
  • [64] J. Choi and Y. Kim, An improved lea block encryption algorithm to prevent side-channel attack in the iot system 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1-4, 2016.
  • [65] S. Sicari, et al, Reato: reacting to denial of service attacks in the internet of things, Comput. Network., 137, pp. 37-48, 2018.
  • [66] P. Varga, et al, Security threats and issues in automation iot, 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1-6, 2017.
  • [67] J. Liu, et al, Epic: a differential privacy framework to defend smart homes against internet traffic analysis, IEEE Internet Things J., 5 (2), 2018.
  • [68] U. Guin, et al, A secure low-cost edge device authentication scheme for the internet of things, 31st International Conference on VLSI Design and 17th International Conference on Embedded Systems (VLSID). 2018.
  • [69] G. Glissa, et al, A secure routing protocol based on rpl for internet of things, IEEE Global Communications Conference (GLOBECOM), 2016.
  • [70] C. Pu and S. Hajjar, Mitigating forwarding misbehaviors in rpl-based low power and lossy networks, 2018 15th IEEE Annual Consumer Communications Networking Conference (CCNC), 2018.
  • [71] C. Cervantes, et al, Detection of sinkhole attacks for supporting secure routing on 6lowpan for internet of things, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015.
  • [72] P. Shukla, Ml-ids: A machine learning approach to detect wormhole attacks in internet of things, Intelligent Systems Conference (IntelliSys), 2017.
  • [73] D. Airehrour, J.A. Gutierrez & S.K. Ray, Sectrust-rpl: a secure trust-aware rpl routing protocol for internet of things, Future Gener. Comput. Syst., 2019.
  • [74] M. Singh, et al, Secure mqtt for internet of things (iot), 5th International Conference on Communication Systems and Network Technologies, 2015.
  • [75] Y. Ashibani, Q.H. Mahmoud, An efficient and secure scheme for smart home communication using identity-based signcryption, 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), 2017.
  • [76] V. Adat, B.B. Gupta, A ddos attack mitigation framework for internet of things, 2017 International Conference on Communication and Signal Processing (ICCSP), 2017.
  • [77] D. Yin, et al, A ddos attack detection and mitigation with software-defined internet of things framework, IEEE Access, 6, 2018.
  • [78] C. Liu, P. Cronin, C. Yang, A mutual auditing framework to protect iot against hardware trojans, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 2016.
  • [79] S.T.C. Konigsmark, D. Chen, M.D.F. Wong, Information dispersion for trojan defense through high-level synthesis, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC), 2016.
  • [80] H. Naeem, et al, A light-weight malware static visual analysis for iot infrastructure, International Conference on Artificial Intelligence and Big Data (ICAIBD), 2018.
  • [81] J. Su, et al, Lightweight classification of iot malware based on image recognition, IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, 2018.
  • [82] T. Song, et al, A privacy preserving communication protocol for iot applications in smart homes, IEEE Internet Things J., 4 (6), 2017.
  • [83] C. Machado, A.A.M. Frhlich, Iot data integrity verification for cyber-physical systems using blockchain, 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), 2018.
  • [84] Y. Rahulamathavan, et al, Privacy-preserving blockchain based iot ecosystem using attribute-based encryption, IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2017.
  • [85] D. Zheng, et al, Efficient and privacy-preserving medical data sharing in internet of things with limited computing power, IEEE Access, 6, 2018.
  • [86] P. Gope, B. Sikdar, Lightweight and privacy-preserving two-factor authentication scheme for iot devices, IEEE Internet Things J., 2018.
  • [87] J. Sengupta, et al, End to end secure anonymous communication for secure directed diffusion in iot, Proceedings of the 20th International Conference on Distributed Computing and Networking, ICDCN '19, 2019.
  • [88] F. Li, et al, System statistics learning-based IoT security: Feasibility and suitability, IEEE Internet Things J., vol. 6, no. 4, pp. 6396-6403, Aug. 2019.
  • [89] Magaia, Naercio, et al. Industrial Internet-of-Things Security Enhanced with Deep Learning Approaches for Smart Cities. IEEE Internet of Things Journal 8.8, 2020
  • [90] Sharma, M., Pant, S., Kumar Sharma, D., Datta Gupta, K., Vashishth, V., & Chhabra, A. Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions. Transactions on Emerging Telecommunications Technologies, 32(7), e4137. 2021.
  • [91] M. M. N. Aboelwafa, et al, A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT, in IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8462-8471, Sept. 2020.
  • [92] Z. E. Huma et al., A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things, in IEEE Access, vol. 9, pp. 55595-55605, 2021.
  • [93] S. Liu, et al, Network Log Anomaly Detection Based on GRU and SVDD, 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1244-1249, 2019.
  • [94] S. Latif, et al, A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network," in IEEE Access, vol. 8, pp. 89337-89350, 2020.
  • [95] S. Latif, et al, DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT, 2020 International Conference on UK-China Emerging Technologies (UCET), pp. 1-4, 2020.
  • [96] M. M. Hassan, M. R. Hassan, S. Huda and V. H. C. de Albuquerque, A Robust Deep-Learning-Enabled Trust- Boundary Protection for Adversarial Industrial IoT Environment, in IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9611-9621, 15 June15, 2021.
  • [97] M. M. Hassan, A. Gumaei, S. Huda and A. Almogren, Increasing the Trustworthiness in the Industrial IoT Networks Through a Reliable Cyberattack Detection Model, in IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6154-6162, Sept. 2020.
  • [98] Y. Liu et al., Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach, in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348-6358, 15 April15, 2021.
  • [99] M. Khoda, T. Imam, J. Kamruzzaman, I. Gondal and A. Rahman, Robust Malware Defense in Industrial IoT Applications Using Machine Learning With Selective Adversarial Samples, in IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4415-4424, July-Aug. 2020.
  • [100] A. N. Jahromi, H. Karimipour, A. Dehghantanha and K. -K. R. Choo, Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems, in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13712-13722, 1 Sept.1, 2021.
  • [101] A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood and A. Anwar, TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems, in IEEE Access, vol. 8, pp. 165130-165150, 2020.
  • [102] J. Zhao, et al, Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data. Wireless Communications and Mobile Computing, 2021.
  • [103] T. Primya & G. Subashini, Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment. International Journal of Communication Systems, 34(17), e4976, 2021.
  • [104] F. Hussain et al, A Framework for Malicious Traffic Detection in IoT Healthcare Environment. Sensors, 21(9), 3025. 2021.
  • [105] M. Alqahtani et al, IoT botnet attack detection based on optimized extreme gradient boosting and feature selection. Sensors, 20(21), 6336, 2020.
  • [106] I. Campero-Jurado et al. Smart Helmet 5.0 for industrial internet of things using artificial intelligence. Sensors, 20(21), 6241. 2020
  • [107] M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras and H. Janicke, "Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning," in IEEE Access, vol. 10, pp. 40281-40306, 2022, doi: 10.1109/ACCESS.2022.3165809.
  • [108] Kumar, A., Shridhar, M., Swaminathan, S., & Lim, T. J. Machine learning-based early detection of IoT botnets using network-edge traffic. Computers & Security, 117, 102693. 2022
  • [109] Tharewal, S., Ashfaque, M. W., Banu, S. S., Uma, P., Hassen, S. M., & Shabaz, M. Intrusion detection system for industrial Internet of Things based on deep reinforcement learning. Wireless Communications and Mobile Computing, 2022.
  • [110] Javeed, D., Gao, T., Khan, M. T., & Shoukat, D. A hybrid intelligent framework to combat sophisticated threats in secure industries. Sensors, 22(4), 1582. 2022.
  • [111] D. Arp, et al. Drebin: Effective and explainable detection of android malware in your pocket. In Ndss (Vol. 14, pp. 23-26), February 2014.
  • [112] H. Satilmiş & S. Akleylek, A review of machine learning and deep learning models used for IoT security. Bilişim Teknolojileri Dergisi, 14(4), 457-481. 2021.
  • [113] N. Koroniotis, et al. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100, 779-796. 2019.
  • [114] Sethi, P., & Sarangi, S. R. (2017). Internet of things: architectures, protocols, and applications. Journal of electrical and computer engineering, 2017.
  • [115] AlSalem, T. S., Almaiah, M. A., & Lutfi, A. (2023). Cybersecurity Risk Analysis in the IoT: A Systematic Review. Electronics, 12(18), 3958.
  • [116] Rodríguez, E., Otero, B., & Canal, R. (2023). A survey of machine and deep learning methods for privacy protection in the Internet of Things. Sensors, 23(3), 1252.
  • [117] Santhosh Kumar, S. V. N., Selvi, M., & Kannan, A. (2023). A comprehensive survey on machine learning- based intrusion detection systems for secure communication in internet of things. Computational Intelligence and Neuroscience, 2023.
  • [118] Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2023). Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mobile Networks and Applications, 28(1), 296-312.
  • [119] Nuaimi, M., Fourati, L. C., & Hamed, B. B. (2023). Intelligent approaches toward intrusion detection systems for Industrial Internet of Things: A systematic comprehensive review. Journal of Network and Computer Applications, 103637.
  • [120] Mohy-Eddine, M., Guezzaz, A., Benkirane, S., Azrour, M., & Farhaoui, Y. (2023). An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security. Big Data Mining and Analytics, 6(3), 273-287.
  • [121] Alshahrani, H., Khan, A., Rizwan, M., Reshan, M. S. A., Sulaiman, A., & Shaikh, A. (2023). Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability, 15(11), 9001.
  • [122] Huang, J. C., Zeng, G. Q., Geng, G. G., Weng, J., & Lu, K. D. (2023). SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things. IET Cyber‐Systems and Robotics, 5(1), e12085.
  • [123] Mehedi, S. T., Anwar, A., Rahman, Z., Ahmed, K., & Islam, R. (2022). Dependable intrusion detection system for IoT: A deep transfer learning based approa ch. IEEE Transactions on Industrial Informatics, 19(1), 1006-1017.
Yıl 2024, Cilt: 12 Sayı: 1, 1 - 28, 21.06.2024
https://doi.org/10.51354/mjen.1197753

Öz

Kaynakça

  • [1] L.S. Vailshery, Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2023, with forecasts from 2022 to 2030, https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/ , Statista, Last accessed: October 31, 2021.
  • [2] M. Hatton, The IoT in 2030: Which applications account for the biggest chunk of the $1.5 trillion opportunity? TransformaInsights, https://www.kisa.link/PsHW, Last accessed: October 31, 2021..
  • [3] F. Meneghello, et al., IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices, IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8182–8201, 2019.
  • [4] C. Xenofontos, et al. Consumer, commercial and industrial iot (in) security: attack taxonomy and case studies. IEEE Internet of Things Journal, 2021.
  • [5] D. Antonioli, et al., Blurtooth: Exploiting cross-transport key derivation in bluetooth classic and bluetooth low energy, arXiv preprint arXiv:2009.11776, 2020.
  • [6] L. L. Dhirani, E. Armstrong, and T. Newe, Industrial IoT, Cyber Threats, and Standards Landscape: Evaluation and Roadmap. Sensors, 21(11), 3901, 2021
  • [7] A. R. Sadeghi, C. Wachsmann, & M. Waidner, Security and privacy challenges in industrial internet of things. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE, June 2015.
  • [8] J. P. Anderson, Computer security threat monitoring and surveillance, Technical Report, James P. Anderson Company, 1980
  • [9] B. B. Zarpelão, et al, A survey of intrusion detection in Internet of Things, Journal of Network and Computer Applications, Volume 84, Pages 25-37, ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2017.02.009, 2017
  • [10] E. Hodo, et al, Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE, May 2016.
  • [11] E. Anthi, et al, A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), 9042-9053, 2019.
  • [12] S. Raza, L. Wallgren, & T. Voigt, SVELTE: Real-time intrusion detection in the Internet of Things. Ad hoc networks, 11(8), 2661-2674, 2013.
  • [13] V. Kumar, A. K. Das, & D. Sinha, UIDS: A unified intrusion detection system for IoT environment. Evolutionary Intelligence, 14(1), 47-59, 2021.
  • [14] M. Eskandari, et al, Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet of Things Journal, 7(8), 6882-6897, 2020.
  • [15] E. Aydogan, et al. A central intrusion detection system for rpl-based industrial internet of things. In 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS) (pp. 1-5). IEEE, May 2019.
  • [16] M. Zolanvari, et al., Machine learning-based network vulnerability analysis of industrial Internet of Things. IEEE Internet of Things Journal, 6(4), 6822-6834, 2019.
  • [17] J. B. Awotunde, C. Chakraborty, & A. E. Adeniyi, Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection. Wireless Communications and Mobile Computing, 2021.
  • [18] A. H. Muna, N. Moustafa & E. Sitnikova, Identification of malicious activities in industrial internet of things based on deep learning models. Journal of Information security and applications, 41, 1-11, 2018.
  • [19] G. E. I. Selim, et al. Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms. Multimedia Tools and Applications, 80(8), 12619- 12640, 2021.
  • [20] A. F. M. Agarap, A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data. In Proceedings of the 2018 10th international conference on machine learning and computing (pp. 26-30). 2018, February.
  • [21] S. Aljawarneh, M. Aldwairi, & M. B. Yassein. Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science, 25, 152-160. 2018.
  • [22] L. Breiman, et al, Classification and regression trees. Routledge. 2017.
  • [23] L. Li, H. Zhang, H. Peng, & Y. Yang, Nearest neighbors based density peaks approach to intrusion detection. Chaos, Solitons & Fractals, 110, 33-40. 2018.
  • [24] A. L. Buczak & E. Guven. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176. 2015.
  • [25] A. P. Muniyandi, R. Rajeswari, & R. Rajaram, Network anomaly detection by cascading k-Means clustering and C4. 5 decision tree algorithm. Procedia Engineering, 30, 174-182. 2012.
  • [26] R. Vinayakumar, K. P. Soman, & P. Poornachandran, Applying convolutional neural network for network intrusion detection. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1222-1228). IEEE. September, 2017.
  • [27] A. A. Diro, & N. Chilamkurti. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems, 82, 761-768. 2018.
  • [28] J. Kim, et al. Long short term memory recurrent neural network classifier for intrusion detection. In 2016 International Conference on Platform Technology and Service (PlatCon) (pp. 1-5). IEEE. (2016, February).
  • [29] P. Liu, X. Qiu, & X. Huang, X. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101. 2016.
  • [30] M. Yousefi-Azar, et al. Autoencoder-based feature learning for cyber security applications. In 2017 International joint conference on neural networks (IJCNN) (pp. 3854-3861). IEEE. (2017, May).
  • [31] T. Salimans, et al. Improved techniques for training gans. Advances in neural information processing systems, 29, 2234-2242. 2016.
  • [32] U. Fiore, et al. Network anomaly detection with the restricted Boltzmann machine. Neurocomputing, 122, 13-23. 2013.
  • [33] Y. Zhang, P. Li, & X. Wang, Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access, 7, 31711-31722. 2019.
  • [34] K. Tange, et al. Towards a systematic survey of industrial IoT security requirements: research method and quantitative analysis, Proceedings of the Workshop on Fog Computing and the IoT, 2019.
  • [35] K. Tange, et al, A Systematic Survey of Industrial Internet of Things Security: Requirements and Fog Computing Opportunities, in IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2489-2520, Fourthquarter 2020.
  • [36] T. Soo Fun, & A. Samsudin, Recent Technologies, Security Countermeasure and Ongoing Challenges of Industrial Internet of Things (IIoT): A Survey. Sensors, 21(19), 6647. 2021.
  • [37] S. Bhatt, & P.R. Ragiri, Security trends in Internet of Things: A survey. SN Applied Sciences, 3(1), 1-14. 2021.
  • [38] M. Serror, et al, Challenges and Opportunities in Securing the Industrial Internet of Things, IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 2985-2996, doi: 10.1109/TII.2020.3023507, May 2021.
  • [39] Y. Wu, et al. Deep reinforcement learning for blockchain in industrial IoT: A survey. Computer Networks, 191, 108004. 2021.
  • [40] K. Tsiknas, et al, Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures. IoT, 2(1), 163- 188, 2021.
  • [41] M. A. Al-Garadi, et al, A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security, IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020.
  • [42] R. A. Khalil, et al. Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications, IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11016-11040, 15 July15, 2021.
  • [43] R. Ahmad & I. Alsmadi, Machine learning approaches to IoT security: A systematic literature review. Internet of Things, 100365. 2021.
  • [44] L. Aversano, et al. A systematic review on Deep Learning approaches for IoT security. Computer Science Review, 40, 100389. 2021
  • [45] Rudenko, R., Pires, I. M., Oliveira, P., Barroso, J., & Reis, A. (2022). A Brief Review on Internet of Things, Industry 4.0 and Cybersecurity. Electronics, 11(11), 1742.
  • [46] Ahanger, T. A., Aljumah, A., & Atiquzzaman, M. (2022). State-of-the-art survey of artificial intelligent techniques for IoT security. Computer Networks, 108771.
  • [47] L. Tan and N. Wang, Future internet: The Internet of Things, 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), pp. V5-376-V5-380, 2010
  • [48] F. A. Alaba, et al, Internet of Things security: A survey, J. Netw. Comput. Appl., 88, 10–28, 2017.
  • [49] H. Boyes, et al. The industrial internet of things (IIoT): An analysis framework. Computers in industry, 101, 1-12. 2018.
  • [50] J. Sengupta, S. Ruj & S. D. Bit, A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications, 149, 102481. 2020.
  • [51] U. Saxena, J. S Sodhi, & Y. Singh. An Analysis of DDoS Attacks in a Smart Home Networks. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 272-276). IEEE. January 2020.
  • [52] S. Alzahrani and L. Hong, Generation of DDoS attack dataset for effective IDS development and evaluation, J. Inf. Secur. 9 (4), 225–241, 2018.
  • [53] Y. Gu, et al, Semi-supervised K-means DDoS detection method using hybrid feature selection algorithm, IEEE Access 7, 64351–64365, 2019.
  • [54] Y.N. Soe, et al, DDoS attack detection based on simple ANN with SMOTE for IoT environment, in: 2019 Fourth International Conference on Informatics and Computing (ICIC), pp. 1–5, 2019.
  • [55] N. Chaabouni, et al, Network intrusion detection for iot security based on learning techniques, IEEE Commun. Surv. Tutor. 21 (3), 2671–2701, 2019.
  • [56] P. García-Teodoro, et al, Anomaly-based network intrusion detection: techniques, systems and challenges, Comput. Secur. 28 (1), 18–28, 2009.
  • [57] I. Andrea, C. Chrysostomou, G. Hadjichristofi Internet of things: security vulnerabilities and challenges, 2015 IEEE Symposium on Computers and Communication (ISCC), ), pp. 180-187, 2015.
  • [58] M.M. Ahemd, M.A. Shah, A. Wahid, Iot security: a layered approach for attacks and defenses, 2017 International Conference on Communication Technologies (ComTech), pp. 104-110, 2017.
  • [59] M. R. Bartolacci, et al, Personal denial of service (PDOS) attacks: A discussion and exploration of a new category of cyber crime. Journal of Digital Forensics, Security and Law, 9(1), 2. 2014.
  • [60] M.N. Aman, et al, A light-weight mutual authentication protocol for iot systems, GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1-6, 2017.
  • [61] T. Gomes, et al, Cute mote, a customizable and trustable end-device for the internet of things, IEEE Sens. J., 17 (20), pp. 6816-6824, 2017.
  • [62] P. Porambage, et al, Pauthkey: a pervasive authentication protocol and key establishment scheme for wireless sensor networks in distributed iot applications, Int. J. Distributed Sens. Netw., 10 (7), 2014.
  • [63] X. Hei, et al, Defending resource depletion attacks on implantable medical devices, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010, pp. 1-5. 2010.
  • [64] J. Choi and Y. Kim, An improved lea block encryption algorithm to prevent side-channel attack in the iot system 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1-4, 2016.
  • [65] S. Sicari, et al, Reato: reacting to denial of service attacks in the internet of things, Comput. Network., 137, pp. 37-48, 2018.
  • [66] P. Varga, et al, Security threats and issues in automation iot, 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1-6, 2017.
  • [67] J. Liu, et al, Epic: a differential privacy framework to defend smart homes against internet traffic analysis, IEEE Internet Things J., 5 (2), 2018.
  • [68] U. Guin, et al, A secure low-cost edge device authentication scheme for the internet of things, 31st International Conference on VLSI Design and 17th International Conference on Embedded Systems (VLSID). 2018.
  • [69] G. Glissa, et al, A secure routing protocol based on rpl for internet of things, IEEE Global Communications Conference (GLOBECOM), 2016.
  • [70] C. Pu and S. Hajjar, Mitigating forwarding misbehaviors in rpl-based low power and lossy networks, 2018 15th IEEE Annual Consumer Communications Networking Conference (CCNC), 2018.
  • [71] C. Cervantes, et al, Detection of sinkhole attacks for supporting secure routing on 6lowpan for internet of things, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015.
  • [72] P. Shukla, Ml-ids: A machine learning approach to detect wormhole attacks in internet of things, Intelligent Systems Conference (IntelliSys), 2017.
  • [73] D. Airehrour, J.A. Gutierrez & S.K. Ray, Sectrust-rpl: a secure trust-aware rpl routing protocol for internet of things, Future Gener. Comput. Syst., 2019.
  • [74] M. Singh, et al, Secure mqtt for internet of things (iot), 5th International Conference on Communication Systems and Network Technologies, 2015.
  • [75] Y. Ashibani, Q.H. Mahmoud, An efficient and secure scheme for smart home communication using identity-based signcryption, 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), 2017.
  • [76] V. Adat, B.B. Gupta, A ddos attack mitigation framework for internet of things, 2017 International Conference on Communication and Signal Processing (ICCSP), 2017.
  • [77] D. Yin, et al, A ddos attack detection and mitigation with software-defined internet of things framework, IEEE Access, 6, 2018.
  • [78] C. Liu, P. Cronin, C. Yang, A mutual auditing framework to protect iot against hardware trojans, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 2016.
  • [79] S.T.C. Konigsmark, D. Chen, M.D.F. Wong, Information dispersion for trojan defense through high-level synthesis, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC), 2016.
  • [80] H. Naeem, et al, A light-weight malware static visual analysis for iot infrastructure, International Conference on Artificial Intelligence and Big Data (ICAIBD), 2018.
  • [81] J. Su, et al, Lightweight classification of iot malware based on image recognition, IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, 2018.
  • [82] T. Song, et al, A privacy preserving communication protocol for iot applications in smart homes, IEEE Internet Things J., 4 (6), 2017.
  • [83] C. Machado, A.A.M. Frhlich, Iot data integrity verification for cyber-physical systems using blockchain, 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), 2018.
  • [84] Y. Rahulamathavan, et al, Privacy-preserving blockchain based iot ecosystem using attribute-based encryption, IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2017.
  • [85] D. Zheng, et al, Efficient and privacy-preserving medical data sharing in internet of things with limited computing power, IEEE Access, 6, 2018.
  • [86] P. Gope, B. Sikdar, Lightweight and privacy-preserving two-factor authentication scheme for iot devices, IEEE Internet Things J., 2018.
  • [87] J. Sengupta, et al, End to end secure anonymous communication for secure directed diffusion in iot, Proceedings of the 20th International Conference on Distributed Computing and Networking, ICDCN '19, 2019.
  • [88] F. Li, et al, System statistics learning-based IoT security: Feasibility and suitability, IEEE Internet Things J., vol. 6, no. 4, pp. 6396-6403, Aug. 2019.
  • [89] Magaia, Naercio, et al. Industrial Internet-of-Things Security Enhanced with Deep Learning Approaches for Smart Cities. IEEE Internet of Things Journal 8.8, 2020
  • [90] Sharma, M., Pant, S., Kumar Sharma, D., Datta Gupta, K., Vashishth, V., & Chhabra, A. Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions. Transactions on Emerging Telecommunications Technologies, 32(7), e4137. 2021.
  • [91] M. M. N. Aboelwafa, et al, A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT, in IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8462-8471, Sept. 2020.
  • [92] Z. E. Huma et al., A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things, in IEEE Access, vol. 9, pp. 55595-55605, 2021.
  • [93] S. Liu, et al, Network Log Anomaly Detection Based on GRU and SVDD, 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), pp. 1244-1249, 2019.
  • [94] S. Latif, et al, A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network," in IEEE Access, vol. 8, pp. 89337-89350, 2020.
  • [95] S. Latif, et al, DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT, 2020 International Conference on UK-China Emerging Technologies (UCET), pp. 1-4, 2020.
  • [96] M. M. Hassan, M. R. Hassan, S. Huda and V. H. C. de Albuquerque, A Robust Deep-Learning-Enabled Trust- Boundary Protection for Adversarial Industrial IoT Environment, in IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9611-9621, 15 June15, 2021.
  • [97] M. M. Hassan, A. Gumaei, S. Huda and A. Almogren, Increasing the Trustworthiness in the Industrial IoT Networks Through a Reliable Cyberattack Detection Model, in IEEE Transactions on Industrial Informatics, vol. 16, no. 9, pp. 6154-6162, Sept. 2020.
  • [98] Y. Liu et al., Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach, in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6348-6358, 15 April15, 2021.
  • [99] M. Khoda, T. Imam, J. Kamruzzaman, I. Gondal and A. Rahman, Robust Malware Defense in Industrial IoT Applications Using Machine Learning With Selective Adversarial Samples, in IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4415-4424, July-Aug. 2020.
  • [100] A. N. Jahromi, H. Karimipour, A. Dehghantanha and K. -K. R. Choo, Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems, in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13712-13722, 1 Sept.1, 2021.
  • [101] A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood and A. Anwar, TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems, in IEEE Access, vol. 8, pp. 165130-165150, 2020.
  • [102] J. Zhao, et al, Anomaly Detection Collaborating Adaptive CEEMDAN Feature Exploitation with Intelligent Optimizing Classification for IIoT Sparse Data. Wireless Communications and Mobile Computing, 2021.
  • [103] T. Primya & G. Subashini, Swarm intelligence‐based secure high‐order optimal density selection for industrial internet‐of‐things (IIoT) data on cloud environment. International Journal of Communication Systems, 34(17), e4976, 2021.
  • [104] F. Hussain et al, A Framework for Malicious Traffic Detection in IoT Healthcare Environment. Sensors, 21(9), 3025. 2021.
  • [105] M. Alqahtani et al, IoT botnet attack detection based on optimized extreme gradient boosting and feature selection. Sensors, 20(21), 6336, 2020.
  • [106] I. Campero-Jurado et al. Smart Helmet 5.0 for industrial internet of things using artificial intelligence. Sensors, 20(21), 6241. 2020
  • [107] M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras and H. Janicke, "Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning," in IEEE Access, vol. 10, pp. 40281-40306, 2022, doi: 10.1109/ACCESS.2022.3165809.
  • [108] Kumar, A., Shridhar, M., Swaminathan, S., & Lim, T. J. Machine learning-based early detection of IoT botnets using network-edge traffic. Computers & Security, 117, 102693. 2022
  • [109] Tharewal, S., Ashfaque, M. W., Banu, S. S., Uma, P., Hassen, S. M., & Shabaz, M. Intrusion detection system for industrial Internet of Things based on deep reinforcement learning. Wireless Communications and Mobile Computing, 2022.
  • [110] Javeed, D., Gao, T., Khan, M. T., & Shoukat, D. A hybrid intelligent framework to combat sophisticated threats in secure industries. Sensors, 22(4), 1582. 2022.
  • [111] D. Arp, et al. Drebin: Effective and explainable detection of android malware in your pocket. In Ndss (Vol. 14, pp. 23-26), February 2014.
  • [112] H. Satilmiş & S. Akleylek, A review of machine learning and deep learning models used for IoT security. Bilişim Teknolojileri Dergisi, 14(4), 457-481. 2021.
  • [113] N. Koroniotis, et al. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems, 100, 779-796. 2019.
  • [114] Sethi, P., & Sarangi, S. R. (2017). Internet of things: architectures, protocols, and applications. Journal of electrical and computer engineering, 2017.
  • [115] AlSalem, T. S., Almaiah, M. A., & Lutfi, A. (2023). Cybersecurity Risk Analysis in the IoT: A Systematic Review. Electronics, 12(18), 3958.
  • [116] Rodríguez, E., Otero, B., & Canal, R. (2023). A survey of machine and deep learning methods for privacy protection in the Internet of Things. Sensors, 23(3), 1252.
  • [117] Santhosh Kumar, S. V. N., Selvi, M., & Kannan, A. (2023). A comprehensive survey on machine learning- based intrusion detection systems for secure communication in internet of things. Computational Intelligence and Neuroscience, 2023.
  • [118] Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2023). Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mobile Networks and Applications, 28(1), 296-312.
  • [119] Nuaimi, M., Fourati, L. C., & Hamed, B. B. (2023). Intelligent approaches toward intrusion detection systems for Industrial Internet of Things: A systematic comprehensive review. Journal of Network and Computer Applications, 103637.
  • [120] Mohy-Eddine, M., Guezzaz, A., Benkirane, S., Azrour, M., & Farhaoui, Y. (2023). An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security. Big Data Mining and Analytics, 6(3), 273-287.
  • [121] Alshahrani, H., Khan, A., Rizwan, M., Reshan, M. S. A., Sulaiman, A., & Shaikh, A. (2023). Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability, 15(11), 9001.
  • [122] Huang, J. C., Zeng, G. Q., Geng, G. G., Weng, J., & Lu, K. D. (2023). SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things. IET Cyber‐Systems and Robotics, 5(1), e12085.
  • [123] Mehedi, S. T., Anwar, A., Rahman, Z., Ahmed, K., & Islam, R. (2022). Dependable intrusion detection system for IoT: A deep transfer learning based approa ch. IEEE Transactions on Industrial Informatics, 19(1), 1006-1017.
Toplam 123 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Derleme
Yazarlar

Ersin Enes Eryılmaz 0000-0003-1163-970X

Sedat Akleylek 0000-0001-7005-6489

Yankı Ertek 0000-0003-3998-1419

Erdal Kılıç 0000-0003-1585-0991

Yayımlanma Tarihi 21 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 1

Kaynak Göster

APA Eryılmaz, E. E., Akleylek, S., Ertek, Y., Kılıç, E. (2024). A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MANAS Journal of Engineering, 12(1), 1-28. https://doi.org/10.51354/mjen.1197753
AMA Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. Haziran 2024;12(1):1-28. doi:10.51354/mjen.1197753
Chicago Eryılmaz, Ersin Enes, Sedat Akleylek, Yankı Ertek, ve Erdal Kılıç. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering 12, sy. 1 (Haziran 2024): 1-28. https://doi.org/10.51354/mjen.1197753.
EndNote Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E (01 Haziran 2024) A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MANAS Journal of Engineering 12 1 1–28.
IEEE E. E. Eryılmaz, S. Akleylek, Y. Ertek, ve E. Kılıç, “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”, MJEN, c. 12, sy. 1, ss. 1–28, 2024, doi: 10.51354/mjen.1197753.
ISNAD Eryılmaz, Ersin Enes vd. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering 12/1 (Haziran 2024), 1-28. https://doi.org/10.51354/mjen.1197753.
JAMA Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. 2024;12:1–28.
MLA Eryılmaz, Ersin Enes vd. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering, c. 12, sy. 1, 2024, ss. 1-28, doi:10.51354/mjen.1197753.
Vancouver Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. 2024;12(1):1-28.

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