1.
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. doi:10.1109/comst.2020.2988293.
2.
F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IOT security: Current solutions and future challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020. doi:10.1109/comst.2020.2986444.
3.
A. Thakkar and R. Lohiya, “A review on machine learning and Deep Learning Perspectives of ids for IOT: Recent updates, security issues, and challenges,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 3211–3243, Oct. 2020. doi:10.1007/s11831-020-09496-0.
4.
U. Farooq, N. Tariq, M. Asim, T. Baker, and A. Al-Shamma’a, “Machine learning and the internet of things security: Solutions and open challenges,” Journal of Parallel and Distributed Computing, vol. 162, pp. 89–104, Apr. 2022. doi:10.1016/j.jpdc.2022.01.015.
5.
V. Gugueoth, S. Safavat, and S. Shetty, “Security of internet of things (IOT) using Federated Learning and deep learning — recent advancements, issues and prospects,” ICT Express, vol. 9, no. 5, pp. 941–960, Oct. 2023. doi:10.1016/j.icte.2023.03.006.
6.
B. Patel, J. Vasa, and P. Shah, “IOT concepts, characteristics, enabling technologies, applications and protocol stack: Issues and Imperatives,” International Journal of Wireless and Mobile Computing, vol. 25, no. 4, pp. 397–406, 2023. doi:10.1504/ijwmc.2023.135404.
7.
X. Liang and Y. Kim, “A survey on security attacks and solutions in the IOT Network,” 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2021. doi:10.1109/ccwc51732.2021.9376174.
8.
H. Mrabet, S. Belguith, A. Alhomoud, and A. Jemai, “A survey of IOT security based on a layered architecture of sensing and data analysis,” Sensors, vol. 20, no. 13, p. 3625, Jun. 2020. doi:10.3390/s20133625.
9.
N. Verma, S. Singh, and D. Prasad, “A review on existing IOT architecture and communication protocols used in Healthcare Monitoring System,” Journal of The Institution of Engineers (India): Series B, vol. 103, no. 1, pp. 245–257, Jun. 2021. doi:10.1007/s40031-021-00632-3.
10.
A. Thakkar and R. Lohiya, “A review on machine learning and Deep Learning Perspectives of ids for IOT: Recent updates, security issues, and challenges,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 3211–3243, Oct. 2020. doi:10.1007/s11831-020-09496-0.
11.
V. Hassija et al., “A survey on IOT security: Application areas, security threats, and solution architectures,” IEEE Access, vol. 7, pp. 82721–82743, 2019. doi:10.1109/access.2019.2924045.
12.
D. Swessi and H. Idoudi, “A survey on internet-of-things security: Threats and emerging countermeasures,” Wireless Personal Communications, vol. 124, no. 2, pp. 1557–1592, Jan. 2022. doi:10.1007/s11277-021-09420-0.
13.
B. B. Gupta and M. Quamara, “An overview of internet of things (IOT): Architectural aspects, challenges, and protocols,” Concurrency and Computation: Practice and Experience, vol. 32, no. 21, Sep. 2018. doi:10.1002/cpe.4946.
14.
A. H. Mohd Aman, E. Yadegaridehkordi, Z. S. Attarbashi, R. Hassan, and Y.-J. Park, “A survey on trend and classification of internet of things reviews,” IEEE Access, vol. 8, pp. 111763–111782, 2020. doi:10.1109/access.2020.3002932.
15.
L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, “IOT privacy and security: Challenges and solutions,” Applied Sciences, vol. 10, no. 12, p. 4102, Jun. 2020. doi:10.3390/app10124102.
16.
T. M. Ghazal et al., “IOT for Smart Cities: Machine Learning Approaches in smart healthcare—A Review,” Future Internet, vol. 13, no. 8, p. 218, Aug. 2021. doi:10.3390/fi13080218.
17.
J. Asharf et al., “A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions,” Electronics, vol. 9, no. 7, p. 1177, Jul. 2020. doi:10.3390/electronics9071177.
18.
P. Malhotra et al., “Internet of things: Evolution, concerns and security challenges,” Sensors, vol. 21, no. 5, p. 1809, Mar. 2021. doi:10.3390/s21051809.
19.
M. Yu, J. Zhuge, M. Cao, Z. Shi, and L. Jiang, “A survey of security vulnerability analysis, discovery, detection, and mitigation on IOT devices,” Future Internet, vol. 12, no. 2, p. 27, Feb. 2020. doi:10.3390/fi12020027.
20.
B. I. Mukhtar, M. S. Elsayed, A. D. Jurcut, and M. A. Azer, “IOT vulnerabilities and attacks: Silex malware case study,” Symmetry, vol. 15, no. 11, p. 1978, Oct. 2023. doi:10.3390/sym15111978.
21.
J. Sengupta, S. Ruj, and S. Das Bit, “A comprehensive survey on attacks, security issues and blockchain solutions for IOT and Iiot,” Journal of Network and Computer Applications, vol. 149, p. 102481, Jan. 2020. doi:10.1016/j.jnca.2019.102481.
22.
T. J. Saleem and M. A. Chishti, “Deep learning for the internet of things: Potential benefits and use-cases,” Digital Communications and Networks, vol. 7, no. 4, pp. 526–542, Nov. 2021. doi:10.1016/j.dcan.2020.12.002.
23.
S. Khanam, I. B. Ahmedy, M. Y. Idna Idris, M. H. Jaward, and A. Q. Bin Md Sabri, “A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things,” IEEE Access, vol. 8, pp. 219709–219743, 2020. doi:10.1109/access.2020.3037359.
24.
N. Abosata, S. Al-Rubaye, G. Inalhan, and C. Emmanouilidis, “Internet of things for system integrity: A comprehensive survey on security, attacks and countermeasures for Industrial Applications,” Sensors, vol. 21, no. 11, p. 3654, May 2021. doi:10.3390/s21113654.
25.
F. B. H.J. and S. S., “A survey on IOT security: Attacks, challenges and countermeasures,” Webology, vol. 19, no. 1, pp. 3741–3763, Jan. 2022. doi:10.14704/web/v19i1/web19246.
26.
R. R. Chowdhury and P. E. Abas, “A survey on device fingerprinting approach for Resource-constraint IOT devices: Comparative study and research challenges,” Internet of Things, vol. 20, p. 100632, Nov. 2022. doi:10.1016/j.iot.2022.100632.
27.
A. Maatallaoui, H. Touil, and L. Setti, “The impact of radio frequency (RF) attacks on Security and Privacy: A Comprehensive Review,” Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security, May 2023. doi:10.1145/3607720.3607771.
28.
A. Barua, M. A. Al Alamin, Md. S. Hossain, and E. Hossain, “Security and privacy threats for Bluetooth Low Energy in IOT and wearable devices: A comprehensive survey,” IEEE Open Journal of the Communications Society, vol. 3, pp. 251–281, 2022. doi:10.1109/ojcoms.2022.3149732.
29.
K. Chetioui, B. Bah, A. O. Alami, and A. Bahnasse, “Overview of social engineering attacks on social networks,” Procedia Computer Science, vol. 198, pp. 656–661, 2022. doi:10.1016/j.procs.2021.12.302.
30.
N. Ahmed et al., “A survey on location privacy attacks and prevention deployed with IOT in Vehicular Networks,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–15, Apr. 2022. doi:10.1155/2022/6503299.
31.
M. R. Kadri, A. Abdelli, J. Ben Othman, and L. Mokdad, “Survey and classification of Dos and DDOS attack detection and validation approaches for IOT Environments,” Internet of Things, vol. 25, p. 101021, Apr. 2024. doi:10.1016/j.iot.2023.101021.
32.
P. Kumari and A. K. Jain, “A comprehensive study of ddos attacks over IOT network and their countermeasures,” Computers & Security, vol. 127, p. 103096, Apr. 2023. doi:10.1016/j.cose.2023.103096.
33.
P. Vennam, S. K. Mouleeswaran, S. Shamila, and S. R. Kasarla, “A comprehensive analysis of fog layer and man in the middle attacks in IOT Networks,” 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), vol. 9, pp. 1–5, Oct. 2022. doi:10.1109/mysurucon55714.2022.9972612.
34.
D. Panda, B. Kishore Mishra, and K. Sharma, “A taxonomy on man-in-the-middle attack in IOT Network,” 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), vol. 7, pp. 1907–1912, Dec. 2022. doi:10.1109/icac3n56670.2022.10074170.
35.
P. Victor et al., “IOT malware: An attribute-based taxonomy, detection mechanisms and challenges,” Peer-to-Peer Networking and Applications, vol. 16, no. 3, pp. 1380–1431, May 2023. doi:10.1007/s12083-023-01478-w.
36.
C. S. Yadav et al., “Malware analysis in IOT & Android systems with Defensive Mechanism,” Electronics, vol. 11, no. 15, p. 2354, Jul. 2022. doi:10.3390/electronics11152354.
37.
H. Pirayesh and H. Zeng, “Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 767–809, 2022. doi:10.1109/comst.2022.3159185.
38.
F. Alrefaei, A. Alzahrani, H. Song, and S. Alrefaei, “A survey on the jamming and spoofing attacks on the Unmanned Aerial Vehicle Networks,” 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Jun. 2022. doi:10.1109/iemtronics55184.2022.9795809.
39.
A. A. Al-chikh Omar, B. Soudan, and Ala’ Altaweel, “A comprehensive survey on detection of sinkhole attack in routing over low power and lossy network for internet of things,” Internet of Things, vol. 22, p. 100750, Jul. 2023. doi:10.1016/j.iot.2023.100750.
40.
R. Ahmad, I. Alsmadi, W. Alhamdani, and L. Tawalbeh, “Zero-Day attack detection: A systematic literature review,” Artificial Intelligence Review, vol. 56, no. 10, pp. 10733–10811, Feb. 2023. doi:10.1007/s10462-023-10437-z.
41.
R. Patnaik, N. Padhy, and K. Srujan Raju, “A systematic survey on IOT security issues, vulnerability and open challenges,” Advances in Intelligent Systems and Computing, pp. 723–730, Aug. 2020. doi:10.1007/978-981-15-5400-1_68.
42.
K. Shaukat et al., “A review on security challenges in internet of things (IOT),” 2021 26th International Conference on Automation and Computing (ICAC), Sep. 2021. doi:10.23919/icac50006.2021.9594183.
43.
K. M. Sadique, R. Rahmani, and P. Johannesson, “Towards security on internet of things: Applications and challenges in Technology,” Procedia Computer Science, vol. 141, pp. 199–206, 2018. doi:10.1016/j.procs.2018.10.168.
44.
R. R. Krishna et al., “State-of-the-art review on IOT threats and attacks: Taxonomy, challenges and solutions,” Sustainability, vol. 13, no. 16, p. 9463, Aug. 2021. doi:10.3390/su13169463.
45.
V. N and P. T. V. Bhuavneswari, “ADBIS: Anomaly detection to bolster IOT security using machine learning,” 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), pp. 1–6, Nov. 2023. doi:10.1109/aespc59761.2023.10390100.
46.
Rachit, S. Bhatt, and P. R. Ragiri, “Security trends in internet of things: A survey,” SN Applied Sciences, vol. 3, no. 1, Jan. 2021. doi:10.1007/s42452-021-04156-9.
47.
S. Balogh, O. Gallo, R. Ploszek, P. Špa?ek, and P. Zajac, “IOT security challenges: Cloud and blockchain, Postquantum cryptography, and evolutionary techniques,” Electronics, vol. 10, no. 21, p. 2647, Oct. 2021. doi:10.3390/electronics10212647.
48.
R. F. Ali, A. Muneer, P. D. Dominic, S. M. Taib, and E. A. Ghaleb, “Internet of things (IOT) security challenges and solutions: A systematic literature review,” Communications in Computer and Information Science, pp. 128–154, 2021. doi:10.1007/978-981-16-8059-5_9.
49.
M. Azrour, J. Mabrouki, A. Guezzaz, and A. Kanwal, “Internet of things security: Challenges and key issues,” Security and Communication Networks, vol. 2021, pp. 1–11, Sep. 2021. doi:10.1155/2021/5533843.
50.
B. Yedle, G. Shrivastava, A. Kumar, A. K. Mishra, and T. K. Mishra, “A survey: Security issues and challenges in internet of things,” Lecture Notes in Networks and Systems, pp. 75–86, Jun. 2020. doi:10.1007/978-981-15-4218-3_8.
51.
S. Chesney, K. Roy, and S. Khorsandroo, “Machine learning algorithms for preventing IOT cybersecurity attacks,” Advances in Intelligent Systems and Computing, pp. 679–686, Aug. 2020. doi:10.1007/978-3-030-55190-2_53.
52.
M. Bagaa, T. Taleb, J. B. Bernabe, and A. Skarmeta, “A Machine Learning Security Framework for IOT systems,” IEEE Access, vol. 8, pp. 114066–114077, 2020. doi:10.1109/access.2020.2996214.
53.
S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of internet of things (IOT): A survey,” Journal of Network and Computer Applications, vol. 161, p. 102630, Jul. 2020. doi:10.1016/j.jnca.2020.102630.
54.
A. S. Ahmed and H. A. Salah, “Development a software defined network (SDN) with internet of things (IOT) security for medical issues,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 15, no. 3, Sep. 2023. doi:10.29304/jqcm.2023.15.3.1268.
55.
M. Shen, X. Tang, L. Zhu, X. Du, and M. Guizani, “Privacy-preserving support vector machine training over blockchain-based encrypted IOT data in Smart Cities,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 7702–7712, Oct. 2019. doi:10.1109/jiot.2019.2901840.
56.
M. Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani, “CORRAUC: A malicious bot-IOT traffic detection method in IOT network using machine-learning techniques,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3242–3254, Mar. 2021. doi:10.1109/jiot.2020.3002255.
57.
C. Ioannou and V. Vassiliou, “Experimentation with local intrusion detection in IOT networks using supervised learning,” 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), May 2020. doi:10.1109/dcoss49796.2020.00073.
58.
D. Puthal et al., “Decision tree based user-centric security solution for critical IOT infrastructure,” Computers and Electrical Engineering, vol. 99, p. 107754, Apr. 2022. doi:10.1016/j.compeleceng.2022.107754.
59.
A. Churcher et al., “An experimental analysis of attack classification using machine learning in IOT Networks,” Sensors, vol. 21, no. 2, p. 446, Jan. 2021. doi:10.3390/s21020446.
60.
Y. N. Soe, Y. Feng, P. I. Santosa, R. Hartanto, and K. Sakurai, “Machine learning-based IOT-botnet attack detection with sequential architecture,” Sensors, vol. 20, no. 16, p. 4372, Aug. 2020. doi:10.3390/s20164372.
61.
S. Nakhodchi, A. Upadhyay, and A. Dehghantanha, “A comparison between different machine learning models for IOT malware detection,” Security of Cyber-Physical Systems, pp. 195–202, 2020. doi:10.1007/978-3-030-45541-5_10.
62.
R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning ddos detection for consumer internet of things devices,” 2018 IEEE Security and Privacy Workshops (SPW), May 2018. doi:10.1109/spw.2018.00013.
63.
S. Li, Q. Zhang, X. Wu, W. Han, and Z. Tian, “Attribution classification method of APT Malware in IOT using machine learning techniques,” Security and Communication Networks, vol. 2021, pp. 1–12, Sep. 2021. doi:10.1155/2021/9396141.
64.
I. S. Thaseen, V. Mohanraj, S. Ramachandran, K. Sanapala, and S.-S. Yeo, “A Hadoop based framework integrating machine learning classifiers for anomaly detection in the internet of things,” Electronics, vol. 10, no. 16, p. 1955, Aug. 2021. doi:10.3390/electronics10161955.
65.
S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of internet of things (IOT): A survey,” Journal of Network and Computer Applications, vol. 161, p. 102630, Jul. 2020. doi:10.1016/j.jnca.2020.102630.
66.
B. Zhang, Z. Liu, Y. Jia, J. Ren, and X. Zhao, “Network intrusion detection method based on PCA and Bayes algorithm,” Security and Communication Networks, vol. 2018, pp. 1–11, Nov. 2018. doi:10.1155/2018/1914980.
67.
H. H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, and K.-K. R. Choo, “A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IOT backbone networks,” IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 2, pp. 314–323, Apr. 2019. doi:10.1109/tetc.2016.2633228.
68.
Z. H. Abdaljabar, O. N. Ucan, and K. M. Ali Alheeti, “An intrusion detection system for IOT using KNN and decision-tree based classification,” 2021 International Conference of Modern Trends in Information and Communication Technology Industry (MTICTI), Dec. 2021. doi:10.1109/mticti53925.2021.9664772.
69.
A. Kumar and T. J. Lim, “Edima: Early detection of IOT malware network activity using Machine Learning Techniques,” 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Apr. 2019. doi:10.1109/wf-iot.2019.8767194.
70.
Sharipuddin et al., “Features extraction on IOT intrusion detection system using Principal Components Analysis (PCA),” 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI), vol. 8491, pp. 114–118, Oct. 2020. doi:10.23919/eecsi50503.2020.9251292.
71.
S. K. Dash et al., “Enhancing ddos attack detection in IOT using PCA,” Egyptian Informatics Journal, vol. 25, p. 100450, Mar. 2024. doi:10.1016/j.eij.2024.100450 G. Abbas, A. Mehmood, M. Carsten, G. Epiphaniou, and J. Lloret, “Safety, security and privacy in machine learning based internet of things,” Journal of Sensor and Actuator Networks, vol. 11, no. 3, p. 38, Jul. 2022. doi:10.3390/jsan11030038.
72.
L. Liu, X. Xu, Y. Liu, Z. Ma, and J. Peng, “A detection framework against CPMA attack based on trust evaluation and Machine Learning in IOT network,” IEEE Internet of Things Journal, vol. 8, no. 20, pp. 15249–15258, Oct. 2021. doi:10.1109/jiot.2020.3047642.
73.
Y. Alotaibi and M. Ilyas, “Ensemble-Learning Framework for intrusion detection to enhance internet of things’ devices security,” Sensors, vol. 23, no. 12, p. 5568, Jun. 2023. doi:10.3390/s23125568.
74.
P. K. Danso et al., “Ensemble-based intrusion detection for internet of things devices,” 2022 IEEE 19th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), vol. 30, pp. 034–039, Dec. 2022. doi:10.1109/honet56683.2022.10019140.
75.
H. Li, K. Ota, and M. Dong, “Learning IOT in edge: Deep learning for the internet of things with Edge Computing,” IEEE Network, vol. 32, no. 1, pp. 96–101, Jan. 2018. doi:10.1109/mnet.2018.1700202.
76.
S. Li, Q. Zhang, X. Wu, W. Han, and Z. Tian, “Attribution classification method of APT Malware in IOT using machine learning techniques,” Security and Communication Networks, vol. 2021, pp. 1–12, Sep. 2021. doi:10.1155/2021/9396141.
77.
T. A. Tuan et al., “Performance evaluation of botnet ddos attack detection using machine learning,” Evolutionary Intelligence, vol. 13, no. 2, pp. 283–294, Nov. 2019. doi:10.1007/s12065-019-00310-w.
78.
M. Shafiq, Z. Tian, Y. Sun, X. Du, and M. Guizani, “Selection of effective machine learning algorithm and bot-IOT attacks traffic identification for internet of things in Smart City,” Future Generation Computer Systems, vol. 107, pp. 433–442, Jun. 2020. doi:10.1016/j.future.2020.02.017.
79.
A. Arshad et al., “A novel ensemble method for enhancing internet of things device security against botnet attacks,” Decision Analytics Journal, vol. 8, p. 100307, Sep. 2023. doi:10.1016/j.dajour.2023.100307.
80.
K. Alissa et al., “Botnet attack detection in IOT using machine learning,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–14, Oct. 2022. doi:10.1155/2022/4515642.
81.
T. Gaber, A. El-Ghamry, and A. E. Hassanien, “Injection attack detection using machine learning for smart IOT Applications,” Physical Communication, vol. 52, p. 101685, Jun. 2022. doi:10.1016/j.phycom.2022.101685.
82.
V. Tomer and S. Sharma, “Detecting IOT attacks using an ensemble machine learning model,” Future Internet, vol. 14, no. 4, p. 102, Mar. 2022. doi:10.3390/fi14040102.
83.
S. Rabhi, T. Abbes, and F. Zarai, “IOT routing attacks detection using machine learning algorithms,” Wireless Personal Communications, vol. 128, no. 3, pp. 1839–1857, Sep. 2022. doi:10.1007/s11277-022-10022-7.
84.
H. Gebrye, Y. Wang, and F. Li, “Traffic data extraction and labeling for machine learning based attack detection in IOT networks,” International Journal of Machine Learning and Cybernetics, vol. 14, no. 7, pp. 2317–2332, Jan. 2023. doi:10.1007/s13042-022-01765-7.
85.
S. A. Al-Juboori, F. Hazzaa, Z. S. Jabbar, S. Salih, and H. M. Gheni, “Man-in-the-middle and denial of service attacks detection using machine learning algorithms,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 1, pp. 418–426, Feb. 2023. doi:10.11591/eei.v12i1.4555.
86.
H. M. Saleh, H. Marouane, and A. Fakhfakh, “Stochastic gradient descent intrusions detection for wireless sensor network attack detection system using machine learning,” IEEE Access, vol. 12, pp. 3825–3836, 2024. doi:10.1109/access.2023.3349248.
87.
J. P, J. Shareena, A. Ramdas, and H. A P, “Intrusion detection system for IOT botnet attacks using Deep Learning,” SN Computer Science, vol. 2, no. 3, Apr. 2021. doi:10.1007/s42979-021-00516-9.
88.
B. Alabsi, M. Anbar, and S. Rihan, “CNN-CNN: Dual Convolutional Neural Network Approach for feature selection and attack detection on internet of things networks,” Sensors, vol. 23, no. 14, p. 6507, Jul. 2023. doi:10.3390/s23146507.
89.
A. Dahou et al., “Intrusion detection system for IOT based on Deep Learning and modified reptile search algorithm,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–15, Jun. 2022. doi:10.1155/2022/6473507.
90.
I. Ullah and Q. H. Mahmoud, “Design and development of RNN Anomaly Detection Model for IOT Networks,” IEEE Access, vol. 10, pp. 62722–62750, 2022. doi:10.1109/access.2022.3176317.
91.
P. Sanju, “Enhancing intrusion detection in IOT systems: A hybrid metaheuristics-deep learning approach with ensemble of Recurrent Neural Networks,” Journal of Engineering Research, vol. 11, no. 4, pp. 356–361, Dec. 2023. doi:10.1016/j.jer.2023.100122.
92.
A. Basati and M. M. Faghih, “APAE: An IOT intrusion detection system using asymmetric parallel auto-encoder,” Neural Computing and Applications, vol. 35, no. 7, pp. 4813–4833, Apr. 2021. doi:10.1007/s00521-021-06011-9.
93.
Y. Hou et al., “Hybrid intrusion detection model based on a designed autoencoder,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10799–10809, Sep. 2022. doi:10.1007/s12652-022-04350-6.
94.
Q. E. Ul Haq, M. Imran, K. Saleem, T. Zia, and J. Al Muhtadi, “Review on variants of restricted boltzmann machines and autoencoders for Cyber-Physical Systems,” Internet of Things Security and Privacy, pp. 188–207, Oct. 2023. doi:10.1201/9781003199410-8.
95.
V. S. Desanamukula et al., “A comprehensive analysis of machine learning and deep learning approaches towards IOT Security,” 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), vol. 29, pp. 1165–1168, Jul. 2023. doi:10.1109/icesc57686.2023.10193209.
96.
G. H. Rosa, M. Roder, D. F. Santos, and K. A. Costa, “Enhancing anomaly detection through restricted Boltzmann machine features projection,” International Journal of Information Technology, vol. 13, no. 1, pp. 49–57, Oct. 2020. doi:10.1007/s41870-020-00535-4.
97.
I. Sohn, “Deep belief network based Intrusion Detection Techniques: A Survey,” Expert Systems with Applications, vol. 167, p. 114170, Apr. 2021. doi:10.1016/j.eswa.2020.114170.
98.
T. K. Boppana and P. Bagade, “Gan-ae: An unsupervised intrusion detection system for MQTT Networks,” Engineering Applications of Artificial Intelligence, vol. 119, p. 105805, Mar. 2023. doi:10.1016/j.engappai.2022.105805.
99.
R. Lin et al., “Physical-layer security enhancement in energy-harvesting-based cognitive internet of things: A gan-powered deep reinforcement learning approach,” IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4899–4913, Feb. 2024. doi:10.1109/jiot.2023.3300770.
100.
A. Aleroud, M. Shariah, R. Malkawi, S. Y. Khamaiseh, and A. Al-Alaj, “A privacy-enhanced human activity recognition using Gan & entropy ranking of Microaggregated Data,” Cluster Computing, vol. 27, no. 2, pp. 2117–2132, Jun. 2023. doi:10.1007/s10586-023-04063-1.
101.
M. Alshamkhany et al., “Botnet attack detection using machine learning,” 2020 14th International Conference on Innovations in Information Technology (IIT), Nov. 2020. doi:10.1109/iit50501.2020.9299061.
102.
G. De La Torre Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting internet of things attacks using distributed deep learning,” Journal of Network and Computer Applications, vol. 163, p. 102662, Aug. 2020. doi:10.1016/j.jnca.2020.102662.
103.
D. Papamartzivanos, F. Gomez Marmol, and G. Kambourakis, “Introducing deep learning self-adaptive misuse network intrusion detection systems,” IEEE Access, vol. 7, pp. 13546–13560, 2019. doi:10.1109/access.2019.2893871.
104.
S. I. Popoola et al., “Federated deep learning for Zero-Day botnet attack detection in IOT-edge devices,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3930–3944, Mar. 2022. doi:10.1109/jiot.2021.3100755.
105.
N. Balakrishnan, A. Rajendran, D. Pelusi, and V. Ponnusamy, “Deep belief network enhanced intrusion detection system to prevent security breach in the internet of things,” Internet of Things, vol. 14, p. 100112, Jun. 2021. doi:10.1016/j.iot.2019.100112.
106.
R. H. Randhawa, N. Aslam, M. Alauthman, H. Rafiq, and F. Comeau, “Security hardening of botnet detectors using generative adversarial networks,” IEEE Access, vol. 9, pp. 78276–78292, 2021. doi:10.1109/access.2021.3083421.
107.
G. H. Rosa, M. Roder, D. F. Santos, and K. A. Costa, “Enhancing anomaly detection through restricted Boltzmann machine features projection,” International Journal of Information Technology, vol. 13, no. 1, pp. 49–57, Oct. 2020. doi:10.1007/s41870-020-00535-4.
108.
J. Kumar and G. Ranganathan, “Malware attack detection in large scale networks using the ensemble deep restricted Boltzmann machine,” Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11773–11778, Oct. 2023. doi:10.48084/etasr.6204.
109.
B. Sharma, L. Sharma, C. Lal, and S. Roy, “Anomaly based network intrusion detection for IOT attacks using Deep Learning Technique,” Computers and Electrical Engineering, vol. 107, p. 108626, Apr. 2023. doi:10.1016/j.compeleceng.2023.108626.
110.
A. M. Banaamah and I. Ahmad, “Intrusion detection in IOT using Deep Learning,” Sensors, vol. 22, no. 21, p. 8417, Nov. 2022. doi:10.3390/s22218417.
111.
J. Simon, N. Kapileswar, P. K. Polasi, and M. A. Elaveini, “Hybrid intrusion detection system for wireless IOT networks using Deep Learning algorithm,” Computers and Electrical Engineering, vol. 102, p. 108190, Sep. 2022. doi:10.1016/j.compeleceng.2022.108190.
112.
O. Jullian et al., “Deep-learning based detection for cyber-attacks in IOT Networks: A distributed attack detection framework,” Journal of Network and Systems Management, vol. 31, no. 2, Feb. 2023. doi:10.1007/s10922-023-09722-7.
113.
S. Ahmed et al., “Effective and efficient ddos attack detection using deep learning algorithm, Multi-Layer Perceptron,” Future Internet, vol. 15, no. 2, p. 76, Feb. 2023. doi:10.3390/fi15020076.
114.
V. Shakya, J. Choudhary, and D. P. Singh, “Irada: Integrated Reinforcement Learning and deep learning algorithm for attack detection in wireless sensor networks,” Multimedia Tools and Applications, vol. 83, no. 28, pp. 71559–71578, Feb. 2024. doi:10.1007/s11042-024-18289-7.
115.
N. Sakthipriya, V. Govindasamy, and V. Akila, “Security-aware IOT botnet attack detection framework using dilated and cascaded deep learning mechanism with conditional adversarial autoencoder-based features,” Peer-to-Peer Networking and Applications, vol. 17, no. 3, pp. 1467–1485, Feb. 2024. doi:10.1007/s12083-024-01657-3.
116.
P. Mishra, V. Varadharajan, U. Tupakula, and E. S. Pilli, “A detailed investigation and analysis of using machine learning techniques for intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 686–728, 2019. doi:10.1109/comst.2018.2847722.
117.
M. Amiri-Zarandi, R. A. Dara, and E. Fraser, “A survey of machine learning-based solutions to protect privacy in the internet of things,” Computers & Security, vol. 96, p. 101921, Sep. 2020. doi:10.1016/j.cose.2020.101921.
118.
Q. Liu et al., “A survey on security threats and defensive techniques of Machine Learning: A data driven view,” IEEE Access, vol. 6, pp. 12103–12117, 2018. doi:10.1109/access.2018.2805680.
119.
K. A. P. da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. de Albuquerque, “Internet of things: A survey on machine learning-based intrusion detection approaches,” Computer Networks, vol. 151, pp. 147–157, Mar. 2019. doi:10.1016/j.comnet.2019.01.023.
120.
I. Idrissi, M. Azizi, and O. Moussaoui, “IOT security with deep learning-based intrusion detection systems: A systematic literature review,” 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), vol. 2019 july, pp. 1–10, Oct. 2020. doi:10.1109/icds50568.2020.9268713.
121.
S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of internet of things (IOT): A survey,” Journal of Network and Computer Applications, vol. 161, p. 102630, Jul. 2020. doi:10.1016/j.jnca.2020.102630.
122.
Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and Deep Learning Approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, Oct. 2020. doi:10.1002/ett.4150.
123.
N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network intrusion detection for IOT security based on Learning Techniques,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2671–2701, 2019. doi:10.1109/comst.2019.2896380.
124.
R. Vishwakarma and A. K. Jain, “A survey of ddos attacking techniques and defence mechanisms in the IOT network,” Telecommunication Systems, vol. 73, no. 1, pp. 3–25, Jul. 2019. doi:10.1007/s11235-019-00599-z.
125.
Q.-D. Ngo, H.-T. Nguyen, V.-H. Le, and D.-H. Nguyen, “A survey of IOT malware and detection methods based on static features,” ICT Express, vol. 6, no. 4, pp. 280–286, Dec. 2020. doi:10.1016/j.icte.2020.04.005.
126.
J. Sengupta, S. Ruj, and S. Das Bit, “A comprehensive survey on attacks, security issues and blockchain solutions for IOT and Iiot,” Journal of Network and Computer Applications, vol. 149, p. 102481, Jan. 2020. doi:10.1016/j.jnca.2019.102481.
127.
P. Williams, I. Dutta, H. Daoud, and M. Bayoumi, “Security aspects of internet of things – A survey,” 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), vol. 5, pp. 1–6, Jun. 2020. doi:10.1109/wf-iot48130.2020.9221429.
128.
M. Conti, A. Dehghantanha, K. Franke, and S. Watson, “Internet of things security and forensics: Challenges and opportunities,” Future Generation Computer Systems, vol. 78, pp. 544–546, Jan. 2018. doi:10.1016/j.future.2017.07.060 .
129.
T. J. Saleem and M. A. Chishti, “Deep learning for the internet of things: Potential benefits and use-cases,” Digital Communications and Networks, vol. 7, no. 4, pp. 526–542, Nov. 2021. doi:10.1016/j.dcan.2020.12.002 .
130.
X. Liang and Y. Kim, “A survey on security attacks and solutions in the IOT Network,” 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2021. doi:10.1109/ccwc51732.2021.9376174.
131.
V. Porkodi et al., “A survey on various machine learning models in IOT Applications,” 2020 International Conference on Computing and Information Technology (ICCIT-1441), vol. 44, pp. 1–4, Sep. 2020. doi:10.1109/iccit-144147971.2020.9213819.
132.
Y. Harbi, Z. Aliouat, A. Refoufi, and S. Harous, “Recent security trends in internet of things: A comprehensive survey,” IEEE Access, vol. 9, pp. 113292–113314, 2021. doi:10.1109/access.2021.3103725.
133.
H. Wu, H. Han, X. Wang, and S. Sun, “Research on artificial intelligence enhancing internet of things security: A survey,” IEEE Access, vol. 8, pp. 153826–153848, 2020. doi:10.1109/access.2020.3018170.
134.
P. Malhotra et al., “Internet of things: Evolution, concerns and security challenges,” Sensors, vol. 21, no. 5, p. 1809, Mar. 2021. doi:10.3390/s21051809 .
135.
R. Al-amri et al., “A review of machine learning and deep learning techniques for anomaly detection in IOT Data,” Applied Sciences, vol. 11, no. 12, p. 5320, Jun. 2021. doi:10.3390/app11125320.
136.
M. A. Alsoufi et al., “Anomaly-based intrusion detection systems in IOT using Deep Learning: A Systematic Literature Review,” Applied Sciences, vol. 11, no. 18, p. 8383, Sep. 2021. doi:10.3390/app11188383.
137.
D. S. Berman, A. L. Buczak, J. S. Chavis, and C. L. Corbett, “A survey of Deep Learning Methods for Cyber Security,” Information, vol. 10, no. 4, p. 122, Apr. 2019. doi:10.3390/info10040122.
138.
V. Gugueoth, S. Safavat, and S. Shetty, “Security of internet of things (IOT) using Federated Learning and deep learning — recent advancements, issues and prospects,” ICT Express, vol. 9, no. 5, pp. 941–960, Oct. 2023. doi:10.1016/j.icte.2023.03.006.
139.
J. Singh, M. Wazid, A. K. Das, V. Chamola, and M. Guizani, “Machine learning security attacks and defense approaches for emerging cyber physical applications: A comprehensive survey,” Computer Communications, vol. 192, pp. 316–331, Aug. 2022. doi:10.1016/j.comcom.2022.06.012.
140.
M. Tayyab et al., “A comprehensive review on Deep Learning Algorithms: Security and privacy issues,” Computers & Security, vol. 131, p. 103297, Aug. 2023. doi:10.1016/j.cose.2023.103297.
141.
U. Farooq, N. Tariq, M. Asim, T. Baker, and A. Al-Shamma’a, “Machine learning and the internet of things security: Solutions and open challenges,” Journal of Parallel and Distributed Computing, vol. 162, pp. 89–104, Apr. 2022. doi:10.1016/j.jpdc.2022.01.015.
142.
vinayakumar R et al., Deep Learning for Cyber Security Applications: A comprehensive survey, Oct. 2021. doi:10.36227/techrxiv.16748161.v1.
143.
J. Bian et al., “Machine learning in real-time internet of things (IOT) systems: A survey,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 8364–8386, Jun. 2022. doi:10.1109/jiot.2022.3161050.
144.
S. Bharati and P. Podder, “Machine and deep learning for IOT security and privacy: Applications, challenges, and Future Directions,” Security and Communication Networks, vol. 2022, pp. 1–41, Aug. 2022. doi:10.1155/2022/8951961.
145.
I. H. Sarker, A. I. Khan, Y. B. Abushark, and F. Alsolami, “Internet of things (IOT) security intelligence: A comprehensive overview, machine learning solutions and research directions,” Mobile Networks and Applications, vol. 28, no. 1, pp. 296–312, Mar. 2022. doi:10.1007/s11036-022-01937-3.
146.
C. Alex, G. Creado, W. Almobaideen, O. A. Alghanam, and M. Saadeh, “A comprehensive survey for IOT security datasets taxonomy, classification and Machine Learning Mechanisms,” Computers & Security, vol. 132, p. 103283, Sep. 2023. doi:10.1016/j.cose.2023.103283.
147.
Z. T. Pritee et al., “Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review,” Computers & Security, vol. 140, p. 103747, May 2024. doi:10.1016/j.cose.2024.103747.
148.
R. Priyadarshi, “Exploring machine learning solutions for overcoming challenges in IOT-based Wireless Sensor Network Routing: A comprehensive review,” Wireless Networks, vol. 30, no. 4, pp. 2647–2673, Feb. 2024. doi:10.1007/s11276-024-03697-2.
149.
S Fharis, “Securing the Dynamic Realm: A comprehensive review of ML algorithms in IOT-based home automation systems and beyond,” International Journal of Emerging Trends in Engineering Research, vol. 12, no. 1, pp. 1–7, Jan. 2024. doi:10.30534/ijeter/2024/011212024.
150.
S. A. Haifa Ali and J. Vakula Rani, “Attack detection in IOT using machine learning—A survey,” Engineering Cyber-Physical Systems and Critical Infrastructures, pp. 211–228, 2023. doi:10.1007/978-3-031-18497-0_16.
151.
B. Upadhyaya, S. Sun, and B. Sikdar, “Machine learning-based jamming detection in wireless IOT Networks,” 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), Aug. 2019. doi:10.1109/vts-apwcs.2019.8851633.
152.
G. Liu et al., “Softwarized IOT network immunity against eavesdropping with programmable data planes,” IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6578–6590, Apr. 2021. doi:10.1109/jiot.2020.3048842.
153.
A. Shafique, A. Mehmood, and M. Elhadef, “Detecting signal spoofing attack in uavs using machine learning models,” IEEE Access, vol. 9, pp. 93803–93815, 2021. doi:10.1109/access.2021.3089847.
154.
D. Marabissi, L. Mucchi, and A. Stomaci, “IOT nodes authentication and ID spoofing detection based on joint use of Physical Layer Security and machine learning,” Future Internet, vol. 14, no. 2, p. 61, Feb. 2022. doi:10.3390/fi14020061.
155.
A. Sharma et al., “An efficient hybrid deep learning model for denial of service detection in Cyber Physical Systems,” IEEE Transactions on Network Science and Engineering, vol. 10, no. 5, pp. 2419–2428, Sep. 2023. doi:10.1109/tnse.2023.3273301.
156.
Y. Chen et al., “Physical layer authentication for industrial control based on convolutional denoising autoencoder,” IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15633–15641, May 2024. doi:10.1109/jiot.2023.3347603.
157.
S. Alangari, “An unsupervised machine learning algorithm for attack and anomaly detection in IOT Sensors,” Wireless Personal Communications, Feb. 2024. doi:10.1007/s11277-023-10811-8.
158.
H. Mrabet, S. Belguith, A. Alhomoud, and A. Jemai, “A survey of IOT security based on a layered architecture of sensing and data analysis,” Sensors, vol. 20, no. 13, p. 3625, Jun. 2020. doi:10.3390/s20133625.
159.
W. Ding, M. Abdel-Basset, and R. Mohamed, “Deepak-IOT: An effective deep learning model for cyberattack detection in IOT networks,” Information Sciences, vol. 634, pp. 157–171, Jul. 2023. doi:10.1016/j.ins.2023.03.052.
160.
B. Sharma, L. Sharma, C. Lal, and S. Roy, “Anomaly based network intrusion detection for IOT attacks using Deep Learning Technique,” Computers and Electrical Engineering, vol. 107, p. 108626, Apr. 2023. doi:10.1016/j.compeleceng.2023.108626.
161.
T. S. Chu, W. Si, S. Simoff, and Q. V. Nguyen, “A machine learning classification model using random forest for detecting ddos attacks,” 2022 International Symposium on Networks, Computers and Communications (ISNCC), vol. 18, pp. 1–7, Jul. 2022. doi:10.1109/isncc55209.2022.9851797.
162.
M. Liu and L. Yang, “IOT network traffic analysis with Deep Learning,” 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), vol. 23, pp. 184–189, Mar. 2024. doi:10.1109/percomworkshops59983.2024.10502498.
163.
E. Altulaihan, M. A. Almaiah, and A. Aljughaiman, “Anomaly detection ids for detecting DOS attacks in IOT networks based on machine learning algorithms,” Sensors, vol. 24, no. 2, p. 713, Jan. 2024. doi:10.3390/s24020713.
164.
A. Awajan, “A novel deep learning-based Intrusion Detection System for IOT Networks,” Computers, vol. 12, no. 2, p. 34, Feb. 2023. doi:10.3390/computers12020034.
165.
F. T. Zahra, Y. S. Bostanci, and M. Soyturk, “LSTM-based jamming detection and forecasting model using transport and application layer parameters in Wi-Fi based IOT Systems,” IEEE Access, vol. 12, pp. 32944–32958, 2024. doi:10.1109/access.2024.3371673.
166.
W. I. Khedr, A. E. Gouda, and E. R. Mohamed, “FMDADM: A multi-layer ddos attack detection and mitigation framework using Machine Learning for stateful SDN-based IOT Networks,” IEEE Access, vol. 11, pp. 28934–28954, 2023. doi:10.1109/access.2023.3260256.
167.
S. M. Almeghlef, A. A.-M. AL-Ghamdi, M. S. Ramzan, and M. Ragab, “Application layer-based denial-of-service attacks detection against IOT-Coap,” Electronics, vol. 12, no. 12, p. 2563, Jun. 2023. doi:10.3390/electronics12122563.
168.
G. De La Torre Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting internet of things attacks using distributed deep learning,” Journal of Network and Computer Applications, vol. 163, p. 102662, Aug. 2020. doi:10.1016/j.jnca.2020.102662.
169.
Z. Ahmad et al., “Anomaly detection using Deep Neural Network for IOT Architecture,” Applied Sciences, vol. 11, no. 15, p. 7050, Jul. 2021. doi:10.3390/app11157050.
170.
J. G. Almaraz-Rivera, J. A. Perez-Diaz, and J. A. Cantoral-Ceballos, “Transport and application layer ddos attacks detection to IOT devices by using machine learning and Deep Learning Models,” Sensors, vol. 22, no. 9, p. 3367, Apr. 2022. doi:10.3390/s22093367.
171.
A. K. Pathak, S. Saguna, K. Mitra, and C. Ahlund, “Anomaly detection using machine learning to discover sensor tampering in IOT Systems,” ICC 2021 - IEEE International Conference on Communications, vol. 20, pp. 1–6, Jun. 2021. doi:10.1109/icc42927.2021.9500825.
172.
D. Marabissi, L. Mucchi, and A. Stomaci, “IOT nodes authentication and ID spoofing detection based on joint use of Physical Layer Security and machine learning,” Future Internet, vol. 14, no. 2, p. 61, Feb. 2022. doi:10.3390/fi14020061.
173.
B. Urooj, M. A. Shah, C. Maple, M. K. Abbasi, and S. Riasat, “Malware detection: A framework for reverse engineered Android applications through Machine Learning Algorithms,” IEEE Access, vol. 10, pp. 89031–89050, 2022. doi:10.1109/access.2022.3149053.
174.
J. Sakhnini, H. Karimipour, A. Dehghantanha, and R. M. Parizi, “Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach,” Physical Communication, vol. 47, p. 101394, Aug. 2021. doi:10.1016/j.phycom.2021.101394.
175.
M. Piva, G. Maselli, and F. Restuccia, “The tags are Alright,” Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Jul. 2021. doi:10.1145/3466772.3467033.
176.
M. Jacovic, X. R. Rey, G. Mainland, and K. R. Dandekar, “Mitigating RF jamming attacks at the physical layer with machine learning,” IET Communications, vol. 17, no. 1, pp. 12–28, Oct. 2022. doi:10.1049/cmu2.12461.
177.
M. S. Akhtar and T. Feng, “Detection of malware by Deep Learning as CNN-LSTM machine learning techniques in Real time,” Symmetry, vol. 14, no. 11, p. 2308, Nov. 2022. doi:10.3390/sym14112308.
178.
T. Gaber, A. El-Ghamry, and A. E. Hassanien, “Injection attack detection using machine learning for smart IOT Applications,” Physical Communication, vol. 52, p. 101685, Jun. 2022. doi:10.1016/j.phycom.2022.101685.
179.
Q. Abu Al-Haija, M. Alohaly, and A. Odeh, “A lightweight double-stage scheme to identify malicious DNS over HTTPS traffic using a hybrid learning approach,” Sensors, vol. 23, no. 7, p. 3489, Mar. 2023. doi:10.3390/s23073489.
180.
G. M and P. H B, “Semantic Query-featured Ensemble learning model for SQL-injection attack detection in IOT-ecosystems,” IEEE Transactions on Reliability, vol. 71, no. 2, pp. 1057–1074, Jun. 2022. doi:10.1109/tr.2021.3124331.
181.
P. Chaudhary, B. B. Gupta, and A. K. Singh, “Securing heterogeneous embedded devices against XSS attack in intelligent IOT System,” Computers & Security, vol. 118, p. 102710, Jul. 2022. doi:10.1016/j.cose.2022.102710.
182.
J. Wang and J. Liu, “Deep learning for securing software-defined industrial internet of things: Attacks and countermeasures,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 11179–11189, Jul. 2022. doi:10.1109/jiot.2021.3126633.
183.
H. B. ul Haq and M. Saqlain, “An implementation of effective machine learning approaches to perform sybil attack detection (SAD) in IOT Network,” Theoretical and Applied Computational Intelligence, vol. 1, no. 1, pp. 1–14, Oct. 2023. doi:10.31181/taci1120232.
184.
M. Albishari, M. Li, R. Zhang, and E. Almosharea, “Deep learning-based early stage detection (DL-ESD) for routing attacks in internet of things networks,” The Journal of Supercomputing, vol. 79, no. 3, pp. 2626–2653, Aug. 2022. doi:10.1007/s11227-022-04753-4.
185.
F. Mofidi, S. G. Hounsinou, and G. Bloom, “L-ids: A multi-layered approach to ransomware detection in IOT,” 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), vol. 13291, pp. 0387–0396, Jan. 2024. doi:10.1109/ccwc60891.2024.10427870.
186.
N. Koroniotis, N. Moustafa, E. Sitnikova, and B. Turnbull, “Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IOT dataset,” Future Generation Computer Systems, vol. 100, pp. 779–796, Nov. 2019. doi:10.1016/j.future.2019.05.041.
187.
N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 Network Data Set),” 2015 Military Communications and Information Systems Conference (MilCIS), Nov. 2015. doi:10.1109/milcis.2015.7348942.
188.
N. Moustafa, “A new distributed architecture for evaluating AI-based security systems at the edge: Network ton_iot datasets,” Sustainable Cities and Society, vol. 72, p. 102994, Sep. 2021. doi:10.1016/j.scs.2021.102994.
189.
F. Jeelani, D. S. Rai, A. Maithani, and S. Gupta, “The detection of IOT botnet using machine learning on IOT-23 Dataset,” 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Feb. 2022. doi:10.1109/iciptm54933.2022.9754187.
190.
H. Hindy et al., “Machine learning based IOT intrusion detection system: An MQTT case study (MQTT-IOT-IDS2020 dataset),” Lecture Notes in Networks and Systems, pp. 73–84, 2021. doi:10.1007/978-3-030-64758-2_6.
191.
I. Sharafaldin, A. Habibi Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” Proceedings of the 4th International Conference on Information Systems Security and Privacy, 2018. doi:10.5220/0006639801080116.
192.
S. García, M. Grill, J. Stiborek, and A. Zunino, “An empirical comparison of botnet detection methods,” Computers & Security, vol. 45, pp. 100–123, Sep. 2014. doi:10.1016/j.cose.2014.05.011.
193.
M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, “NetFlow datasets for Machine Learning-based network intrusion detection systems,” Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 117–135, 2021. doi:10.1007/978-3-030-72802-1_9.
194.
Y. Meidan et al., “N-Baiot—network-based detection of IOT botnet attacks using deep autoencoders,” IEEE Pervasive Computing, vol. 17, no. 3, pp. 12–22, Jul. 2018. doi:10.1109/mprv.2018.03367731.
195.
F. De Keersmaeker, Y. Cao, G. K. Ndonda, and R. Sadre, “A survey of public IOT datasets for Network Security Research,” IEEE Communications Surveys & Tutorials, vol. 25, no. 3, pp. 1808–1840, 2023. doi:10.1109/comst.2023.3288942.
196.
S. Hanif, T. Ilyas, and M. Zeeshan, “Intrusion detection in IOT using artificial neural networks on UNSW-15 dataset,” 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), Oct. 2019. doi:10.1109/honet.2019.8908122.
197.
B. Susilo and R. F. Sari, “Intrusion detection in IOT networks using Deep Learning algorithm,” Information, vol. 11, no. 5, p. 279, May 2020. doi:10.3390/info11050279.
198.
I. Vaccari, G. Chiola, M. Aiello, M. Mongelli, and E. Cambiaso, “MQTTset, a new dataset for machine learning techniques on Mqtt,” Sensors, vol. 20, no. 22, p. 6578, Nov. 2020. doi:10.3390/s20226578.
199.
M. Sarhan, S. Layeghy, N. Moustafa, M. Gallagher, and M. Portmann, “Feature extraction for machine learning-based intrusion detection in IOT networks,” Digital Communications and Networks, vol. 10, no. 1, pp. 205–216, Feb. 2024. doi:10.1016/j.dcan.2022.08.012.
200.
H. Nguyen and R. Kashef, “TS-ids: Traffic-aware self-supervised learning for IOT network intrusion detection,” Knowledge-Based Systems, vol. 279, p. 110966, Nov. 2023. doi:10.1016/j.knosys.2023.110966.
201.
A. Arshad et al., “A novel ensemble method for enhancing internet of things device security against botnet attacks,” Decision Analytics Journal, vol. 8, p. 100307, Sep. 2023. doi:10.1016/j.dajour.2023.100307.
202.
N. Abdalgawad, A. Sajun, Y. Kaddoura, I. A. Zualkernan, and F. Aloul, “Generative deep learning to detect cyberattacks for the IOT-23 dataset,” IEEE Access, vol. 10, pp. 6430–6441, 2022. doi:10.1109/access.2021.3140015.
203.
C. Okur, A. Orman, and M. Dener, “DDOS intrusion detection with machine learning models: N-Baiot Data Set,” Engineering Cyber-Physical Systems and Critical Infrastructures, pp. 607–619, 2023. doi:10.1007/978-3-031-31956-3_51.
204.
Z. K. Maseer et al., “DeepIoT.IDS: Hybrid deep learning for enhancing IOT network intrusion detection,” Computers, Materials & Continua, vol. 69, no. 3, pp. 3945–3966, 2021. doi:10.32604/cmc.2021.016074.
205.
M. Ali et al., “Hybrid machine learning model for efficient botnet attack detection in IOT environment,” IEEE Access, vol. 12, pp. 40682–40699, 2024. doi:10.1109/access.2024.3376400.