Subject Areas : Machine learning
Heshamt Asadi
1
,
Mahmood Alborzi
2
,
Hessam Zandhessami
3
1 -
2 -
3 -
Keywords:
Abstract :
[1] J. Asharf, N. Moustafa, H. Khurshid, E. Debie, W. Haider, and A. Wahab, “A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions,” Electronics (Basel), vol. 9, no. 7, p. 1177, Jul. 2020, doi: 10.3390/electronics9071177.
[2] N. Mishra and S. Pandya, “Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review,” IEEE Access, vol. 9, pp. 59353–59377, 2021, doi: 10.1109/ACCESS.2021.3073408.
[3] S. A. Bakhsh, M. A. Khan, F. Ahmed, M. S. Alshehri, H. Ali, and J. Ahmad, “Enhancing IoT network security through deep learning-powered Intrusion Detection System,” Internet of Things, vol. 24, p. 100936, Dec. 2023, doi: 10.1016/j.iot.2023.100936.
[4] 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.
[5] M. Macas, C. Wu, and W. Fuertes, “A survey on deep learning for cybersecurity: Progress, challenges, and opportunities,” Computer Networks, vol. 212, p. 109032, Jul. 2022, doi: 10.1016/j.comnet.2022.109032.
[6] A. S. Dina, A. B. Siddique, and D. Manivannan, “A deep learning approach for intrusion detection in Internet of Things using focal loss function,” Internet of Things, vol. 22, p. 100699, Jul. 2023, doi: 10.1016/j.iot.2023.100699.
[7] 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.
[8] 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,” Comput Secur, vol. 132, p. 103283, Sep. 2023, doi: 10.1016/j.cose.2023.103283.
[9] 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.
[10] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul Model Pract Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031.
[11] A. Tchernykh et al., “Scalable Data Storage Design for Nonstationary IoT Environment With Adaptive Security and Reliability,” IEEE Internet Things J, vol. 7, no. 10, pp. 10171–10188, Oct. 2020, doi: 10.1109/JIOT.2020.2981276.
[12] Y. Li, Y. Zuo, H. Song, and Z. Lv, “Deep Learning in Security of Internet of Things,” IEEE Internet Things J, vol. 9, no. 22, pp. 22133–22146, Nov. 2022, doi: 10.1109/JIOT.2021.3106898.
[13] S. M. Kasongo, “A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework,” Comput Commun, vol. 199, pp. 113–125, Feb. 2023, doi: 10.1016/j.comcom.2022.12.010.
[14] B. Madhu, M. Venu Gopala Chari, R. Vankdothu, A. K. Silivery, and V. Aerranagula, “Intrusion detection models for IOT networks via deep learning approaches,” Measurement: Sensors, vol. 25, p. 100641, Feb. 2023, doi: 10.1016/j.measen.2022.100641.
[15] R. Zhao et al., “A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things,” IEEE Internet Things J, vol. 9, no. 12, pp. 9960–9972, 2022, doi: 10.1109/JIOT.2021.3119055.
[16] S. U. Jan, S. Ahmed, V. Shakhov, and I. Koo, “Toward a Lightweight Intrusion Detection System for the Internet of Things,” IEEE Access, vol. 7, pp. 42450–42471, 2019, doi: 10.1109/ACCESS.2019.2907965.
[17] A. Heidari and M. A. Jabraeil Jamali, “Internet of Things intrusion detection systems: a comprehensive review and future directions,” Cluster Comput, vol. 26, no. 6, pp. 3753–3780, Dec. 2023, doi: 10.1007/s10586-022-03776-z.
[18] D. Musleh, M. Alotaibi, F. Alhaidari, A. Rahman, and R. M. Mohammad, “Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT,” Journal of Sensor and Actuator Networks, vol. 12, no. 2, p. 29, Mar. 2023, doi: 10.3390/jsan12020029.
[19] A. Kumar, K. Abhishek, M. R. Ghalib, A. Shankar, and X. Cheng, “Intrusion detection and prevention system for an IoT environment,” Digital Communications and Networks, vol. 8, no. 4, pp. 540–551, Aug. 2022, doi: 10.1016/j.dcan.2022.05.027.
[20] S. Alosaimi and S. M. Almutairi, “An Intrusion Detection System Using BoT-IoT,” Applied Sciences, vol. 13, no. 9, p. 5427, Apr. 2023, doi: 10.3390/app13095427.
[21] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simul Model Pract Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031.
[22] T. Saba, A. Rehman, T. Sadad, H. Kolivand, and S. A. Bahaj, “Anomaly-based intrusion detection system for IoT networks through deep learning model,” Computers and Electrical Engineering, vol. 99, p. 107810, Apr. 2022, doi: 10.1016/j.compeleceng.2022.107810.
