Subject Areas : Machine learning
mehrnaz moudi
1
,
Arefeh Soleimani
2
,
AmirHossein Hojjati nia
3
1 -
2 - university of torbat-heydarieh
3 - university of torbat heydarieh
Keywords:
Abstract :
References:
[1] B. I. Farhan and A. D.Jasim, “A survey of intrusion detection using deep learning in internet of things,” ijcsm, pp. 83–93, 2022.
[2] U. Lakshmanna , N. Tariq, M. Asim, T. Baker, and A. Al-Shamma’a, “Machine learning and the Internet of Things security: Solutions and open challenges,” J. Parallel Distrib. Comput., vol. 162, pp. 89–104, 2022.
[3] K. Lakshmanna et al., “A review on Deep Learning techniques for IoT data,” Electronics (Basel), vol. 11, no. 10, p. 1604, 2022.
[4] M. A. Alsoufi et al., “Anomaly-based intrusion detection systems in IoT using deep learning: A systematic literature review,” Appl. Sci. (Basel), vol. 11, no. 18, p. 8383, 2021.
[5] A. Alsoufi , A. Muhammed, A. A. Abd Ghani, A. Abdullah, and F. Hakim, “An intrusion detection system for the Internet of Things based on machine learning: Review and challenges,” Symmetry (Basel), vol. 13, no. 6, p. 1011, 2021.
[6] T. Hossain, M. Ariful Islam, A. B. R. Khan, and M. Sadekur Rahman, “A robust and accurate IoT-based fire alarm system for residential use,” in Computer Networks, Big Data and IoT, Singapore: Springer Nature Singapore, 2022, pp. 479–492.
[7] B. Lahasan and H. Samma, “Optimized deep autoencoder model for internet of things intruder detection,” IEEE Access, vol. 10, pp. 8434–8448, 2022.
[8] S. Ullah et al., “A new intrusion detection system for the Internet of Things via deep convolutional neural network and feature engineering,” Sensors (Basel), vol. 22, no. 10, p. 3607, 2022.
[9] N. Tariq, M. Asim, Z. Maamar, M. Z. Farooqi, N. Faci, and T. Baker, “A Mobile Code-driven Trust Mechanism for detecting internal attacks in sensor node-powered IoT,” J. Parallel Distrib. Comput., vol. 134, pp. 198–206, 2019.
[10] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, 2017.
[11] P. Prasdika and B. Sugiantoro, “A review paper on big data and data mining concepts and techniques,” IJID (International Journal on Informatics for Development), vol. 7, no. 1, p. 33, 2018.
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[13] G. Kumar, K. Kumar, and M. Sachdeva, “The use of artificial intelligence based techniques for intrusion detection: a review,” Artif. Intell. Rev., vol. 34, no. 4, pp. 369–387, 2010.
[14] A. Patcha and J.-M. Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends,” Comput. netw., vol. 51, no. 12, pp. 3448–3470, 2007.
[15] D. Minh, H. X. Wang, Y. F. Li, and T. N. Nguyen, “Explainable artificial intelligence: a comprehensive review,” Artif. Intell. Rev., vol. 55, no. 5, pp. 3503–3568, 2022.
[16] P. Shukla, “ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things,” in 2017 Intelligent Systems Conference (IntelliSys), 2017.
[17] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.
[18] A. Paleyes, R.-G. Urma, and N. D. Lawrence, “Challenges in deploying machine learning: A survey of case studies,” ACM Comput. Surv., vol. 55, no. 6, pp. 1–29, 2023.
[19] S. Dong, P. Wang, and K. Abbas, “A survey on deep learning and its applications,” Comput. Sci. Rev., vol. 40, no. 100379, p. 100379, 2021.
[20] A. Thakkar and R. Lohiya, “A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 3211–3243, 2021.
[21] “Cybercrime Magazine - page one for the cybersecurity industry,” Cybercrime Magazine, 26-Dec-2018. [Online]. Available: https://cybersecurityventures.com/. [Accessed: 13-Dec-2022].
[22] S. B. Saad, A. Ksentini, and B. Brik, “A Trust architecture for the SLA management in 5G networks,” in ICC 2021 - IEEE International Conference on Communications, 2021.
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[24] A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey. .
[25] Y. Yue, S. Li, P. Legg, and F. Li, “Deep learning-based security behaviour analysis in IoT environments: A survey,” Secur. Commun. Netw., vol. 2021, pp. 1–13, 2021.
[26] S. Bharati and P. Podder, “Machine and deep learning for IoT security and privacy: Applications, challenges, and future directions,” Secur. Commun. Netw., vol. 2022, pp. 1–41, 2022.
[27] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IoT security: Current solutions and future challenges,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1686–1721, 2020.
[28] Security challenges and solutions in the internet of Nordic and Baltic Journal of Information and Communications Technologies.
[29] J. Wurm, K. Hoang, O. Arias, A.-R. Sadeghi, and Y. Jin, “Security analysis on consumer and industrial IoT devices,” in 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 2016.
[30] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Commun. Surv. Tutor., vol. 20, no. 4, pp. 2923–2960, 2018.
[31] J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer, vol. 27, no. 2, pp. 83–85, 2005.
[32] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.
[33] Z. M. Fadlullah et al., “State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems,” IEEE Commun. Surv. Tutor., vol. 19, no. 4, pp. 2432–2455, 2017.
[34] H. Li, K. Ota, and M. Dong, “Learning IoT in edge: Deep learning for the internet of things with edge computing,” IEEE Netw., vol. 32, no. 1, pp. 96–101, 2018.
[35] R. V. Mendonça, J. C. Silva, R. L. Rosa, M. Saadi, D. Z. Rodriguez, and A. Farouk, “A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithm,” Expert Syst., 2021.
[36] S. K. Gupta, M. Tripathi, and J. Grover, “Hybrid optimization and deep learning based intrusion detection system,” Comput. Electr. Eng., vol. 100, no. 107876, p. 107876, 2022.
[37] A. Basati and M. M. Faghih, “DFE: efficient IoT network intrusion detection using deep feature extraction,” Neural Comput. Appl., vol. 34, no. 18, pp. 15175–15195, 2022.
[38] A. Sagu, N. S. Gill, P. Gulia, J. M. Chatterjee, and I. Priyadarshini, “A hybrid deep learning model with self-improved optimization algorithm for detection of security attacks in IoT environment,” Future internet, vol. 14, no. 10, p. 301, 2022.
[39] I. Idrissi, O. Moussaoui, and M. Azizi, “A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for IoT,” Int. J. Comput. Digit. Syst., vol. 11, no. 1, pp. 209–216, 2022.
[40] Y. Masoudi-Sobhanzadeh and S. Emami-Moghaddam, “A real-time IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier,” Comput. netw., vol. 217, no. 109365, p. 109365, 2022.
[41] Article Lightweight Internet of Things Botnet Detection Using One-Class Classification. .
[42] I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui, and H. El Fadili, “Toward a deep learning-based intrusion detection system for IoT against botnet attacks,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 10, no. 1, p. 110, 2021.
[43] H. Alkahtani and T. H. H. Aldhyani, “Intrusion detection system to advance Internet of Things infrastructure-based deep learning algorithms,” Complexity, vol. 2021, pp. 1–18, 2021.
[44] L. Liu, P. Wang, J. Lin, and L. Liu, “Intrusion detection of imbalanced network traffic based on machine learning and deep learning,” IEEE Access, vol. 9, pp. 7550–7563, 2021.
[45] J. Ashraf, A. D. Bakhshi, N. Moustafa, H. Khurshid, A. Javed, and A. Beheshti, “Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4507–4518, 2021.
[46] B. B. Borisenko, S. D. Erokhin, A. S. Fadeev, and I. D. Martishin, “Intrusion detection using multilayer perceptron and neural networks with long short-term memory,” in 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO, 2021.
[47] T. H. Hai and L. H. Nam, “A practical comparison of deep learning methods for network intrusion detection,” in 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2021.
[48] P. Ts and P. Shrinivasacharya, “Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security,” Global Transitions Proceedings, vol. 2, no. 2, pp. 448–454, 2021.
[49] H. Jia, J. Liu, M. Zhang, X. He, and W. Sun, “Network intrusion detection based on IE-DBN model,” Comput. Commun., vol. 178, pp. 131–140, 2021.
[50] R. Biswas and S. Roy, “Botnet traffic identification using neural networks,” Multimed. Tools Appl., vol. 80, no. 16, pp. 24147–24171, 2021.
[51] F. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, “Intrusion detection systems using long short-term memory (LSTM),” J. Big Data, vol. 8, no. 1, 2021.
[52] M. S. ElSayed, N.-A. Le-Khac, M. A. Albahar, and A. Jurcut, “A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique,” J. Netw. Comput. Appl., vol. 191, no. 103160, p. 103160, 2021.
[53] C. Joshi, R. K. Ranjan, and V. Bharti, “A Fuzzy Logic based feature engineering approach for Botnet detection using ANN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6872–6882, 2022.
[54] K. Sethi, Y. V. Madhav, R. Kumar, and P. Bera, “Attention based multi-agent intrusion detection systems using reinforcement learning,” J. Inf. Secur. Appl., vol. 61, no. 102923, p. 102923, 2021.
[55] F. Hussain et al., “A framework for malicious traffic detection in IoT healthcare environment,” Sensors (Basel), vol. 21, no. 9, 2021.
[56] I. Vaccari, S. Narteni, M. Aiello, M. Mongelli, and E. Cambiaso, “Exploiting internet of things protocols for malicious data exfiltration activities,” IEEE Access, vol. 9, pp. 104261–104280, 2021.
[57] Y. Imrana, Y. Xiang, L. Ali, and Z. Abdul-Rauf, “A bidirectional LSTM deep learning approach for intrusion detection,” Expert Syst. Appl., vol. 185, no. 115524, p. 115524, 2021.
[58] I. A. Khan, N. Moustafa, D. Pi, W. Haider, B. Li, and A. Jolfaei, “An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 12, pp. 25469–25478, 2022.
[59] G. De La Torre Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting Internet of Things attacks using distributed deep learning,” J. Netw. Comput. Appl., vol. 163, no. 102662, p. 102662, 2020.
[60] S. Latif, Z. Idrees, Z. Zou, and J. Ahmad, “DRaNN: A deep random neural network model for intrusion detection in industrial IoT,” in 2020 International Conference on UK-China Emerging Technologies (UCET), 2020.
[61] M. Roopak, G. Y. Tian, and J. Chambers, “An intrusion detection system against DDoS attacks in IoT networks,” in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020.
[62] D. S. Smys, D. A. Basar, and D. H. Wang, “Hybrid intrusion detection system for internet of things (IoT),” December 2020, vol. 2, no. 4, pp. 190–199, 2020.
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[1] B. I. Farhan and A. D.Jasim, “A survey of intrusion detection using deep learning in internet of things,” ijcsm, pp. 83–93, 2022.
[2] U. Lakshmanna , N. Tariq, M. Asim, T. Baker, and A. Al-Shamma’a, “Machine learning and the Internet of Things security: Solutions and open challenges,” J. Parallel Distrib. Comput., vol. 162, pp. 89–104, 2022.
[3] K. Lakshmanna et al., “A review on Deep Learning techniques for IoT data,” Electronics (Basel), vol. 11, no. 10, p. 1604, 2022.
[4] M. A. Alsoufi et al., “Anomaly-based intrusion detection systems in IoT using deep learning: A systematic literature review,” Appl. Sci. (Basel), vol. 11, no. 18, p. 8383, 2021.
[5] A. Alsoufi , A. Muhammed, A. A. Abd Ghani, A. Abdullah, and F. Hakim, “An intrusion detection system for the Internet of Things based on machine learning: Review and challenges,” Symmetry (Basel), vol. 13, no. 6, p. 1011, 2021.
[6] T. Hossain, M. Ariful Islam, A. B. R. Khan, and M. Sadekur Rahman, “A robust and accurate IoT-based fire alarm system for residential use,” in Computer Networks, Big Data and IoT, Singapore: Springer Nature Singapore, 2022, pp. 479–492.
[7] B. Lahasan and H. Samma, “Optimized deep autoencoder model for internet of things intruder detection,” IEEE Access, vol. 10, pp. 8434–8448, 2022.
[8] S. Ullah et al., “A new intrusion detection system for the Internet of Things via deep convolutional neural network and feature engineering,” Sensors (Basel), vol. 22, no. 10, p. 3607, 2022.
[9] N. Tariq, M. Asim, Z. Maamar, M. Z. Farooqi, N. Faci, and T. Baker, “A Mobile Code-driven Trust Mechanism for detecting internal attacks in sensor node-powered IoT,” J. Parallel Distrib. Comput., vol. 134, pp. 198–206, 2019.
[10] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, 2017.
[11] P. Prasdika and B. Sugiantoro, “A review paper on big data and data mining concepts and techniques,” IJID (International Journal on Informatics for Development), vol. 7, no. 1, p. 33, 2018.
[12] “داده کاوی چیست؟ - کاربردهای Data Mining - سحاب,” سحاب, 27-Jul-2021. [Online]. Available: https://www.sahab.ir/insights/what-is-data-mining/. [Accessed: 13-Dec-2022].
[13] G. Kumar, K. Kumar, and M. Sachdeva, “The use of artificial intelligence based techniques for intrusion detection: a review,” Artif. Intell. Rev., vol. 34, no. 4, pp. 369–387, 2010.
[14] A. Patcha and J.-M. Park, “An overview of anomaly detection techniques: Existing solutions and latest technological trends,” Comput. netw., vol. 51, no. 12, pp. 3448–3470, 2007.
[15] D. Minh, H. X. Wang, Y. F. Li, and T. N. Nguyen, “Explainable artificial intelligence: a comprehensive review,” Artif. Intell. Rev., vol. 55, no. 5, pp. 3503–3568, 2022.
[16] P. Shukla, “ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things,” in 2017 Intelligent Systems Conference (IntelliSys), 2017.
[17] C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.
[18] A. Paleyes, R.-G. Urma, and N. D. Lawrence, “Challenges in deploying machine learning: A survey of case studies,” ACM Comput. Surv., vol. 55, no. 6, pp. 1–29, 2023.
[19] S. Dong, P. Wang, and K. Abbas, “A survey on deep learning and its applications,” Comput. Sci. Rev., vol. 40, no. 100379, p. 100379, 2021.
[20] A. Thakkar and R. Lohiya, “A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 3211–3243, 2021.
[21] “Cybercrime Magazine - page one for the cybersecurity industry,” Cybercrime Magazine, 26-Dec-2018. [Online]. Available: https://cybersecurityventures.com/. [Accessed: 13-Dec-2022].
[22] S. B. Saad, A. Ksentini, and B. Brik, “A Trust architecture for the SLA management in 5G networks,” in ICC 2021 - IEEE International Conference on Communications, 2021.
[23] A. Thakkar and R. Lohiya, “Role of swarm and evolutionary algorithms for intrusion detection system: A survey,” Swarm Evol. Comput., vol. 53, no. 100631, p. 100631, 2020.
[24] A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey. .
[25] Y. Yue, S. Li, P. Legg, and F. Li, “Deep learning-based security behaviour analysis in IoT environments: A survey,” Secur. Commun. Netw., vol. 2021, pp. 1–13, 2021.
[26] S. Bharati and P. Podder, “Machine and deep learning for IoT security and privacy: Applications, challenges, and future directions,” Secur. Commun. Netw., vol. 2022, pp. 1–41, 2022.
[27] F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine learning in IoT security: Current solutions and future challenges,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1686–1721, 2020.
[28] Security challenges and solutions in the internet of Nordic and Baltic Journal of Information and Communications Technologies.
[29] J. Wurm, K. Hoang, O. Arias, A.-R. Sadeghi, and Y. Jin, “Security analysis on consumer and industrial IoT devices,” in 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 2016.
[30] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep learning for IoT big data and streaming analytics: A survey,” IEEE Commun. Surv. Tutor., vol. 20, no. 4, pp. 2923–2960, 2018.
[31] J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer, vol. 27, no. 2, pp. 83–85, 2005.
[32] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.
[33] Z. M. Fadlullah et al., “State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems,” IEEE Commun. Surv. Tutor., vol. 19, no. 4, pp. 2432–2455, 2017.
[34] H. Li, K. Ota, and M. Dong, “Learning IoT in edge: Deep learning for the internet of things with edge computing,” IEEE Netw., vol. 32, no. 1, pp. 96–101, 2018.
[35] R. V. Mendonça, J. C. Silva, R. L. Rosa, M. Saadi, D. Z. Rodriguez, and A. Farouk, “A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithm,” Expert Syst., 2021.
[36] S. K. Gupta, M. Tripathi, and J. Grover, “Hybrid optimization and deep learning based intrusion detection system,” Comput. Electr. Eng., vol. 100, no. 107876, p. 107876, 2022.
[37] A. Basati and M. M. Faghih, “DFE: efficient IoT network intrusion detection using deep feature extraction,” Neural Comput. Appl., vol. 34, no. 18, pp. 15175–15195, 2022.
[38] A. Sagu, N. S. Gill, P. Gulia, J. M. Chatterjee, and I. Priyadarshini, “A hybrid deep learning model with self-improved optimization algorithm for detection of security attacks in IoT environment,” Future internet, vol. 14, no. 10, p. 301, 2022.
[39] I. Idrissi, O. Moussaoui, and M. Azizi, “A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for IoT,” Int. J. Comput. Digit. Syst., vol. 11, no. 1, pp. 209–216, 2022.
[40] Y. Masoudi-Sobhanzadeh and S. Emami-Moghaddam, “A real-time IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier,” Comput. netw., vol. 217, no. 109365, p. 109365, 2022.
[41] Article Lightweight Internet of Things Botnet Detection Using One-Class Classification. .
[42] I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui, and H. El Fadili, “Toward a deep learning-based intrusion detection system for IoT against botnet attacks,” IAES Int. J. Artif. Intell. (IJ-AI), vol. 10, no. 1, p. 110, 2021.
[43] H. Alkahtani and T. H. H. Aldhyani, “Intrusion detection system to advance Internet of Things infrastructure-based deep learning algorithms,” Complexity, vol. 2021, pp. 1–18, 2021.
[44] L. Liu, P. Wang, J. Lin, and L. Liu, “Intrusion detection of imbalanced network traffic based on machine learning and deep learning,” IEEE Access, vol. 9, pp. 7550–7563, 2021.
[45] J. Ashraf, A. D. Bakhshi, N. Moustafa, H. Khurshid, A. Javed, and A. Beheshti, “Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4507–4518, 2021.
[46] B. B. Borisenko, S. D. Erokhin, A. S. Fadeev, and I. D. Martishin, “Intrusion detection using multilayer perceptron and neural networks with long short-term memory,” in 2021 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO, 2021.
[47] T. H. Hai and L. H. Nam, “A practical comparison of deep learning methods for network intrusion detection,” in 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2021.
[48] P. Ts and P. Shrinivasacharya, “Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security,” Global Transitions Proceedings, vol. 2, no. 2, pp. 448–454, 2021.
[49] H. Jia, J. Liu, M. Zhang, X. He, and W. Sun, “Network intrusion detection based on IE-DBN model,” Comput. Commun., vol. 178, pp. 131–140, 2021.
[50] R. Biswas and S. Roy, “Botnet traffic identification using neural networks,” Multimed. Tools Appl., vol. 80, no. 16, pp. 24147–24171, 2021.
[51] F. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, “Intrusion detection systems using long short-term memory (LSTM),” J. Big Data, vol. 8, no. 1, 2021.
[52] M. S. ElSayed, N.-A. Le-Khac, M. A. Albahar, and A. Jurcut, “A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique,” J. Netw. Comput. Appl., vol. 191, no. 103160, p. 103160, 2021.
[53] C. Joshi, R. K. Ranjan, and V. Bharti, “A Fuzzy Logic based feature engineering approach for Botnet detection using ANN,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 9, pp. 6872–6882, 2022.
[54] K. Sethi, Y. V. Madhav, R. Kumar, and P. Bera, “Attention based multi-agent intrusion detection systems using reinforcement learning,” J. Inf. Secur. Appl., vol. 61, no. 102923, p. 102923, 2021.
[55] F. Hussain et al., “A framework for malicious traffic detection in IoT healthcare environment,” Sensors (Basel), vol. 21, no. 9, 2021.
[56] I. Vaccari, S. Narteni, M. Aiello, M. Mongelli, and E. Cambiaso, “Exploiting internet of things protocols for malicious data exfiltration activities,” IEEE Access, vol. 9, pp. 104261–104280, 2021.
[57] Y. Imrana, Y. Xiang, L. Ali, and Z. Abdul-Rauf, “A bidirectional LSTM deep learning approach for intrusion detection,” Expert Syst. Appl., vol. 185, no. 115524, p. 115524, 2021.
[58] I. A. Khan, N. Moustafa, D. Pi, W. Haider, B. Li, and A. Jolfaei, “An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 12, pp. 25469–25478, 2022.
[59] G. De La Torre Parra, P. Rad, K.-K. R. Choo, and N. Beebe, “Detecting Internet of Things attacks using distributed deep learning,” J. Netw. Comput. Appl., vol. 163, no. 102662, p. 102662, 2020.
[60] S. Latif, Z. Idrees, Z. Zou, and J. Ahmad, “DRaNN: A deep random neural network model for intrusion detection in industrial IoT,” in 2020 International Conference on UK-China Emerging Technologies (UCET), 2020.
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