References

[1]          Y. Cao, Z. Wang, H. Ding, J. Zhang, and B. Li, “An intrusion detection system based on stacked ensemble learning for IoT network,” Computers and Electrical Engineering, vol. 110, Sep. 2023, doi: 10.1016/j.compeleceng.2023.108836

[2]          Praveen, G. Pandian, D. F., C. "IntelliCare: Integrating IoT and Machine Learning for Remote Patient Monitoring in Healthcare: A Comprehensive Framework," Journal of Journal of Cognitive Human-Computer Interaction, vol. 7, no. 2, pp. 50-59, 2024. DOI: https://doi.org/10.54216/JCHCI.070205

[3]          M. A. Elsadig, “Detection of Denial-of-Service Attack in Wireless Sensor Networks: A Lightweight Machine Learning Approach,” IEEE Access, vol. 11, pp. 83537–83552, 2023, doi: 10.1109/ACCESS.2023.3303113.

[4]          N. Prazeres, R. L. de C. Costa, L. Santos, and C. Rabadão, “Engineering the application of machine learning in an IDS based on IoT traffic flow,” Intelligent Systems with Applications, vol. 17, Feb. 2023, doi: 10.1016/j.iswa.2023.200189

[5]          S. H. Khan et al., “A new deep boosted CNN and ensemble learning based IoT malware detection,” Comput    Secur, vol. 133, Oct. 2023, doi: 10.1016/j.cose.2023.103385.

[6]          R. Ahmad, I. Alsmadi, W. Alhamdani, L. Tawalbeh, A deep learning ensemble approach to detecting unknown network attacks, J. Inform. Secur. Appl. 67 (2022) 103196

[7]          X. H. Nguyen and K. H. Le, “Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model,” Internet of Things (Netherlands), vol. 23, Oct. 2023, doi: 10.1016/j.iot.2023.100851.

[8]           H. Nguyen and R. Kashef, “TS-IDS: Traffic-aware self-supervised learning for IoT Network Intrusion Detection,” Knowl Based Syst, vol. 279, Nov. 2023, doi: 10.1016/j.knosys.2023.110966

[9]          X. Sáez-de-Cámara, J. L. Flores, C. Arellano, A. Urbieta, and U. Zurutuza, “Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks,” Comput Secur, vol. 131, Aug. 2023, doi: 10.1016/j.cose.2023.103299

[10]       A., A. "Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting," Journal of International Journal of Advances in Applied Computational Intelligence, vol. 3, no. 2, pp. 38-47, 2023. DOI: https://doi.org/10.54216/IJAACI.030204

[11]       G. G. Gebremariam, J. Panda, and S. Indu, “Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models,” Alexandria Engineering Journal, vol. 82, pp. 82–100, Nov. 2023, doi: 10.1016/j.aej.2023.09.064

[12]       A. Khraisat and A. Alazab, “A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges,” Cybersecurity, vol. 4, no. 1, Dec. 2021, doi: 10.1186/s42400-021-00077-7.

[13]       R. K. Muna, M. I. Hossain, M. G. R. Alam, M. M. Hassan, M. Ianni, and G. Fortino, “Demystifying machine learning models of massive IoT attack detection with Explainable AI for sustainable and secure future smart cities,” Internet of Things (Netherlands), vol. 24, Dec. 2023, doi: 10.1016/j.iot.2023.100919.

[14]       A. Awajan, “A Novel Deep Learning-Based Intrusion Detection System for IoT Networks,” Computers, vol. 12, no. 2, Feb. 2023, doi: 10.3390/computers12020034

[15]       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, Apr. 2022, doi: 10.1016/j.compeleceng.2022.107810

[16]       H. Asgharzadeh, A. Ghaffari, M. Masdari, and F. Soleimanian Gharehchopogh, “Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm,” J Parallel Distrib Comput, vol. 175, pp. 1–21, May 2023, doi: 10.1016/j.jpdc.2022.12.009.

[17]       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

[18]       S. Latif et al., “Intrusion Detection Framework for the Internet of Things Using a Dense Random Neural Network,” IEEE Trans Industr Inform, vol. 18, no. 9, pp. 6435–6444, Sep. 2022, doi: 10.1109/TII.2021.3130248

[19]       P. Sanju, “Enhancing intrusion detection in IoT systems: A hybrid metaheuristics-deep learning approach with ensemble of recurrent neural networks,” Journal of Engineering Research, p. 100122, Jun. 2023, doi: 10.1016/j.jer.2023.100122.

[20]       S. Ullah et al., “A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering,” Sensors, vol. 22, no. 10, May 2022, doi: 10.3390/s22103607

[21]       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, Apr. 2023, doi: 10.1016/j.compeleceng.2023.108626.

[22]       H. C. Altunay and Z. Albayrak, “A hybrid CNN + LSTMbased intrusion detection system for industrial IoT networks,” Engineering Science and Technology, an International Journal, vol. 38, Feb. 2023, doi: 10.1016/j.jestch.2022.101322.

[23]       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 (Netherlands), vol. 24, Dec. 2023, doi: 10.1016/j.iot.2023.100936.

[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. MDPI AG, Jun. 01, 2021. doi: 10.3390/s21113654

[25]       B. Bala and S. Behal, “AI techniques for IoT-based DDoS attack detection: Taxonomies, comprehensive review and research challenges,” Computer Science Review, vol. 52. Elsevier Ireland Ltd, May 01, 2024. doi: 10.1016/j.cosrev.2024.100631.

[26]       M. M. Inuwa and R. Das, “A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks,” Internet of Things, p. 101162, Jul. 2024, doi: 10.1016/j.iot.2024.101162.

[27]       A. Nazir et al., “A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem,” Ain Shams Engineering Journal, 2024, doi: 10.1016/j.asej.2024.102777.

[28]       H. Y. Alshaeaa and Z. M. Ghadhban, “Developing a hybrid feature selection method to detect botnet attacks in IoT devices,” Kuwait Journal of Science, vol. 51, no. 3, Jul. 2024, doi: 10.1016/j.kjs.2024.100222.

[29]       A. F. Otoom, W. Eleisah, and E. E. Abdallah, “Deep Learning for Accurate Detection of Brute Force attacks on IoT Networks,” in Procedia Computer Science, Elsevier B.V., 2023, pp. 291–298. doi: 10.1016/j.procs.2023.03.038.

[30]       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, Apr. 2023, doi: 10.1016/j.compeleceng.2023.108626.

[31]       C. Kumar and M. S. A. Ansari, “An explainable nature-inspired cyber attack detection system in Software-Defined IoT applications,” Expert Syst Appl, vol. 250, Sep. 2024, doi: 10.1016/j.eswa.2024.123853.

[32]       M. Osman, J. He, N. Zhu, and F. M. M. Mokbal, “An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach,” Ad Hoc Networks, vol. 152, Jan. 2024, doi: 10.1016/j.adhoc.2023.103331.

[33]       P. Malini and D. K. R. Kavitha, “An efficient deep learning mechanisms for IoT/Non-IoT devices classification and attack detection in SDN-enabled smart environment,” Comput Secur, vol. 141, Jun. 2024, doi: 10.1016/j.cose.2024.103818.

[34]       M. S. Ahmad and S. M. Shah, “A lightweight mini-batch federated learning approach for attack detection in IoT,” Internet of Things (Netherlands), vol. 25, Apr. 2024, doi: 10.1016/j.iot.2024.101088.

[35]       J. Bhayo, S. A. Shah, S. Hameed, A. Ahmed, J. Nasir, and D. Draheim, “Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks,” Eng Appl Artif Intell, vol. 123, Aug. 2023, doi: 10.1016/j.engappai.2023.106432.

[36]       C. Rookard and A. Khojandi, “RRIoT: Recurrent reinforcement learning for cyber threat detection on IoT devices,” Comput Secur, vol. 140, May 2024, doi: 10.1016/j.cose.2024.103786.

[37]       C. Ni and S. C. Li, “Machine learning enabled Industrial IoT Security: Challenges, Trends and Solutions,” Journal of Industrial Information Integration, vol. 38. Elsevier B.V., Mar. 01, 2024. doi: 10.1016/j.jii.2023.100549.

[38]       F. Alwahedi, A. Aldhaheri, M. A. Ferrag, A. Battah, and N. Tihanyi, “Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models,” Internet of Things and Cyber-Physical Systems, vol. 4. KeAi Communications Co., pp. 167–185, Jan. 01, 2024. doi: 10.1016/j.iotcps.2023.12.003.

[39]       T. Gaber, A. El-Ghamry, and A. E. Hassanien, “Injection attack detection using machine learning for smart IoT applications,” Physical Communication, vol. 52, Jun. 2022, doi: 10.1016/j.phycom.2022.101685.

[40]       A. Nazir et al., “Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 10. King Saud bin Abdulaziz University, Dec. 01, 2023. doi: 10.1016/j.jksuci.2023.101820.

[41]       Y.-Y. Song, L. Ying, Decision tree methods: applications for classification and prediction, Shanghai Arch. Psychiatry 27 (2) (2015) 130.

[42]       IBM, What is machine learning and machine learning techniques, 2023, https: //www.ibm.com/topics/machine-learning, [Online Accessed : 25 Nov 2023].

[43]       H. El Alami, K. Hall, and D. B. Rawat, “Comparative Study of Machine Learning Techniques for Detecting GPS Spoofing Attacks on Mission Critical Military IoT Devices,” in 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 512–517. doi: 10.1109/ICCWorkshops57953.2023.10283613

[44]       A. Sharma, H. Babbar, and A. K. Vats, “Detection of Attacks in Smart Healthcare deploying Machine Learning Algorithms,” in 2023 4th International Conference for Emerging Technology, INCET 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/INCET57972.2023.10170367

[45]       A. Sharma and H. Babbar, “LUFlow: Attack Detection in the Internet of Things Using Machine Learning Approaches,” in 2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICDCECE57866.2023.10150813

[46]       P. A. A. Resende and A. C. Drummond, “A survey of random forest based methods for intrusion detection systems,” ACM Computing Sur veys (CSUR), vol. 51, no. 3, pp. 1–36, 2018.

[47]       A. Sharma and H. Babbar, “Machine Learning-Based Anomaly Detection in the Internet of Things,” in 2023 3rd Asian Conference on Innovation in Technology, ASIANCON 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ASIANCON58793.2023.10270100.

[48]       A. Khamoun, R. M. Ziani, O. Salem, and A. Mehaoua, “Using Genetic Algorithms to Detect Intrusions for IoT Systems,” in 2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 1–6. doi: 10.1109/Healthcom56612.2023.10472377

[49]       M. Hasan, M. Milon Islam, M. Ishrak Islam Zarif, and M. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” 2019, doi: 10.1016/j.iot.2019.10.

[50]       O. Embarak, M. Algrnaodi, “Deep Learning Fusion for Attack Detection in Internet of Things Communications”, Journal of Fusion: Practice and Applications, vol. 9, no. 2, pp. 27-47, 2023. doi: https://doi.org/10.54216/FPA.090203.

[51]       E. Kazia, “Machine learning for False Information Detection in Social Internet of Things”, Journal of Fusion: Practice and Applications, vol. 10, no. 1, pp. 38-77, 2023. doi :  https://doi.org/10.54216/FPA.100103.

[52]       Mahmoud Zaher, Nabil M. Eldakhly, “Secured Intrusion Detection in Adhoc Networks”, International Journal of Wireless and Ad Hoc Communication, Vol. 5, No. 2, pp. 30-49. 2023. doi   :  https://doi.org/10.54216/IJWAC.050203

[53]       Mahmoud A. Zaher , Nabil M. Eldakhly, “Cyber Attack Detection in Wireless Adhoc Network using Artificial Intelligence”, International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 2 , pp. 18-33. 2023. doi :  https://doi.org/10.54216/IJWAC.060202

[54]       S.P. Samyuktha , Dr.P. Kavitha , V.A Kshaya , P. Shalini , R. Ramya, “A Survey on Cyber Security Meets Artificial Intelligence: AI– Driven Cyber Security”, Journal of Cognitive Human-Computer Interaction, Vol. 2, No. 2, pp. 50-55, 2022. doi :  https://doi.org/10.54216/JCHCI.020202

[55]       Gande Akhila , Hemachandran K , Juan R Jaramillo, “Indian Premier League Using Different Aspects of Machine Learning Algorithms”, Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 1, pp. 01-07, 2022. doi   :  https://doi.org/10.54216/JCHCI.010101

[56]       Mahmoud A. Zaher , Nabil M. Eldakhly, “Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification for Cybersecurity”, Journal of Cybersecurity and Information Management, Vol. 9, No. 2, pp. 20-30, 2022. doi :  https://doi.org/10.54216/JCIM.090202

[57]       Nagamalla, V. karkee, J. Kumar, R. "Integrating Predictive Big Data Analytics with Behavioral Machine Learning Models for Proactive Threat Intelligence in Industrial IoT Cybersecurity," Journal of International Journal of Wireless and Ad Hoc Communication, vol. 7, no. 2, pp. 08-24, 2023. DOI: https://doi.org/10.54216/IJWAC.070201

[58]       Ossama H. Embarak, Raed Abu Zitar,  “Securing Wireless Sensor Networks Against DoS attacks in Industrial 4.0”,  Journal of Intelligent Systems and Internet of Things,  Vol. 8,  No. 1,  pp. 66-74, 2023. doi  :  https://doi.org/10.54216/JISIoT.080106

[59]       Reem Atassi, Aditi Sharma,  “Intelligent Traffic Management using IoT and Machine Learning”,  Journal of Intelligent Systems and Internet of Things,  Vol. 8,  No. 2,  pp. 08-19, 2023. doi  :  https://doi.org/10.54216/JISIoT.080201

[60]       Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy, “Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration”, International Journal of Advances in Applied Computational Intelligence, Vol. 4 ,No. 2,pp. 15-25, 2023. doi :  https://doi.org/10.54216/IJAACI.040202

[61]    Alshaimaa A. Tantawy, “Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting”, International Journal of Advances in Applied Computational Intelligence, Vol. 3 , No. 2 , pp. 38-47, 2023, doi   :  https://doi.org/10.54216/IJAACI.030204