1. Home
  2. Vol 5, No 3 (2024)
  3. Aljabri

IoT Attacks Detection Using Supervised Machine Learning Techniques

Malak Aljabri, Afrah Shaahid, Fatima Alnasser, Asalah Saleh, Dorieh Alomari, Menna Aboulnour, Walla Al-Eidarous, Areej Althubaity

Abstract


In recent times, the growing significance of Internet of Things (IoT) devices in people's lives is undeniable, driven by their myriad benefits. However, these devices confront cybersecurity threats akin to traditional network devices, as they depend on networks for connectivity and synchronization. Artificial Intelligence (AI) techniques, specifically Machine Learning (ML) and Deep Learning (DL), have demonstrated notable reliability in the field of cyberattack detection. This study focuses on detecting Flood and Brute Force cyberattacks using Machine Learning (ML) and Deep Learning (DL) models. The primary emphasis lies in identifying traffic features that significantly detect these types of attacks. The experimental study incorporates eight models: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Gradient Boosting (GB), Naïve Bayes (NB), and Artificial Neural Network (ANN). Two sets of experiments were conducted, with the first set involving six features and the subsequent set, after feature selection, focusing on a reduced set of three features. The evaluation of the proposed model's efficiency and performance relied on metrics such as Accuracy, Precision, Recall, and F1-score. Remarkably, all proposed models exhibited high performance in both sets of experiments. However, the Gradient Boosting (GB) classifier suppressed others, attaining an impressive accuracy level of 95.94% and 95.28% in the sets with six features and three features, respectively.

 

Doi: 10.28991/HIJ-2024-05-03-01

Full Text: PDF


Keywords


Supervised; IoT Security; Cyberattacks; IoT Attacks.

References


Statista. (2024). IoT connected devices worldwide 2019-2030. Statistics, Hamburg, Germany. Available online: https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide (accessed on May 2024).

Bonaccorsi, M., Betti, S., Rateni, G., Esposito, D., Brischetto, A., Marseglia, M., Dario, P., & Cavallo, F. (2017). ‘HighChest’: An augmented freezer designed for smart food management and promotion of eco-efficient behaviour. Sensors (Switzerland), 17(6), 1357. doi:10.3390/s17061357.

Aljabri, M., Aljameel, S. S., Mohammad, R. M. A., Almotiri, S. H., Mirza, S., Anis, F. M., Aboulnour, M., Alomari, D. M., Alhamed, D. H., & Altamimi, H. S. (2021). Intelligent techniques for detecting network attacks: Review and research directions. Sensors, 21(21). doi:10.3390/s21217070.

Arnaldo, I., Cuesta-Infante, A., Arun, A., Lam, M., Bassias, C., & Veeramachaneni, K. (2017). Learning representations for log data in cybersecurity. In S. D. Shlomi & Lodha (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Springer International Publishing, Vol. 10332 LNCS, 250–268. doi:10.1007/978-3-319-60080-2_19.

Hammood, B. A. K., & Sadiq, A. T. (2023). Ensemble machine learning approach for IoT intrusion detection systems. Iraqi Journal for Computers and Informatics, 49(2), 93-99.

Loganathan, G., Samarabandu, J., & Wang, X. (2018). Sequence to Sequence Pattern Learning Algorithm for Real-Time Anomaly Detection in Network Traffic. Canadian Conference on Electrical and Computer Engineering, 2018-May, 1–4. doi:10.1109/CCECE.2018.8447597.

Lambert II, G. M. (2017). Security analytics: Using deep learning to detect cyber-attacks. UNF Graduate Theses and Dissertations University of North Florida, Florida, United States. Available online: https://digitalcommons.unf.edu/etd/728/ (accessed on May 2024).

Stevanovic, M., & Pedersen, J. M. (2016). Detecting bots using multi-level traffic analysis. International Journal on Cyber Situational Awareness, 1(1), 182–209. doi:10.22619/ijcsa.2016.100109.

Alzahrani, R. A., & Aljabri, M. (2023). AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions. Journal of Sensor and Actuator Networks, 12(1). doi:10.3390/jsan12010004.

Aljabri, M., Zagrouba, R., Shaahid, A., Alnasser, F., Saleh, A., & Alomari, D. M. (2023). Machine learning-based social media bot detection: a comprehensive literature review. Social Network Analysis and Mining, 13(1), 20. doi:10.1007/s13278-022-01020-5.

Aljabri, M., Alahmadi, A. A., Mohammad, R. M. A., Alhaidari, F., Aboulnour, M., Alomari, D. M., & Mirza, S. (2023). Machine Learning-Based Detection for Unauthorized Access to IoT Devices. Journal of Sensor and Actuator Networks, 12(2), 27. doi:10.3390/jsan12020027.

Kumari, P., & Jain, A. K. (2023). A comprehensive study of DDoS attacks over IoT network and their countermeasures. Computers and Security, 127, 103096. doi:10.1016/j.cose.2023.103096.

Anwer, M., Umer, M., Khan, S. M., & Waseemullah. (2021). Attack Detection in IoT using Machine Learning. Engineering, Technology and Applied Science Research, 11(3), 7273–7278. doi:10.48084/etasr.4202.

Tomer, V., & Sharma, S. (2022). Detecting IoT attacks Using an Ensemble Machine Learning Model. Future Internet, 14(4), 102. doi:10.3390/fi14040102.

Alsamiri, J., & Alsubhi, K. (2019). Internet of things cyber-attacks detection using machine learning. International Journal of Advanced Computer Science and Applications, 10(12), 627–634. doi:10.14569/ijacsa.2019.0101280.

Lashkari, A. H. (2024). CICFlowmeter-V4.0: GitHub. Available online: https://github.com/ahlashkari/CICFlowMeter (accessed on May 2024).

Htwe, C. S., Thant, Y. M., & Thwin, M. M. S. (2020). Botnets attack detection using machine learning approach for IoT environment. Journal of Physics: Conference Series, 1646(1), 012101. doi:10.1088/1742-6596/1646/1/012101.

Gaber, T., El-Ghamry, A., & Hassanien, A. E. (2022). Injection attack detection using machine learning for smart IoT applications. Physical Communication, 52. doi:10.1016/j.phycom.2022.101685.

Aysa, M. H., Ibrahim, A. A., & Mohammed, A. H. (2020). IoT ddos Attack Detection Using Machine Learning. 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings, 1–7, Istanbul, Turkey. doi:10.1109/ISMSIT50672.2020.9254703.

Krishnan, S., Neyaz, A., & Liu, Q. (2021). IoT Network Attack Detection using Supervised Machine Learning. International Journal of Artificial Intelligence and Expert Systems, 10(2), 18–32.

Saran, N., & Kesswani, N. (2022). A comparative study of supervised Machine Learning classifiers for Intrusion Detection in Internet of Things. Procedia Computer Science, 218, 2049–2057. doi:10.1016/j.procs.2023.01.181.

Altulaihan, E., Almaiah, M. A., & Aljughaiman, A. (2024). Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms. Sensors, 24(2), 713. doi:10.3390/s24020713.

Pecori, R., Tayebi, A., Vannucci, A., & Veltri, L. (2020). IoT Attack Detection with Deep Learning Analysis. Proceedings of the International Joint Conference on Neural Networks, 1–8. doi:10.1109/IJCNN48605.2020.9207171.

Alkahtani, H., & Aldhyani, T. H. H. (2021). Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications. Security and Communication Networks, 2021. doi:10.1155/2021/3806459.

Al-Zubidi, A. F., Farhan, A. K., & Towfek, S. M. (2024). Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model. Journal of Intelligent Systems, 33(1), 20230195. doi:10.1515/jisys-2023-0195.

Islam, N., Farhin, F., Sultana, I., Kaiser, S., Rahman, S., Mahmud, M., Hosen, S., & Cho, G. H. (2021). Towards Machine Learning Based Intrusion Detection in IoT Networks. Computers, Materials and Continua, 69(2), 1801–1821. doi:10.32604/cmc.2021.018466.

Karamollaoğlu, H., Doğru, İ. A., & Yücedağ, İ. (2024). An Efficient Deep Learning-based Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques. Information Technology and Control, 53(1), 243–261. doi:10.5755/j01.itc.53.1.34933.

Dadkhah, S., Mahdikhani, H., Danso, P. K., Zohourian, A., Truong, K. A., & Ghorbani, A. A. (2022). Towards the Development of a Realistic Multidimensional IoT Profiling Dataset. 2022 19th Annual International Conference on Privacy, Security and Trust, PST 2022, 1–11. doi:10.1109/PST55820.2022.9851966.

Alpaydin, E. (2020). Introduction to Machine Learning (Adaptive Computation and Machine Learning Series). Natural Language Engineering: The MIT Press, 14(01), 133–137. doi:10.1017/s1351324906004438.

Chiroma, H., Noor, A. S. M., Abdulkareem, S., Abubakar, A. I., Hermawan, A., Qin, H., Hamza, M. F., & Herawan, T. (2017). Neural networks optimization through genetic algorithm searches: A review. Applied Mathematics and Information Sciences, 11(6), 1543–1564. doi:10.18576/amis/110602.

Bahad, P., & Saxena, P. (2020). Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics. International Conference on Intelligent Computing and Smart Communication 2019, 235–244. doi:10.1007/978-981-15-0633-8_22.

Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. O'Reilly Media, California, United States.

Hilbe, J. M. (2016). Practical guide to logistic regression. Practical Guide to Logistic Regression. CRC Press, Florida, United States. doi:10.18637/jss.v071.b03.

Talekar, B. (2020). A Detailed Review on Decision Tree and Random Forest. Bioscience Biotechnology Research Communications, 13(14), 245–248. doi:10.21786/bbrc/13.14/57.


Full Text: PDF

DOI: 10.28991/HIJ-2024-05-03-01

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Malak Aljabri, Afrah Shaahid, Fatima Alnasser, Asalah Saleh, Dorieh Alomari, Menna Aboulnour, Walla Al-Eidarous, Rachid Zagrouba