International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 16, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 1572KB)
Securing the Internet of Things: Evaluating Machine Learning Algorithms for Detecting IoT Cyberattacks Using CIC-IoT2023 Dataset
PDF (1572KB), PP.56-65
Views: 0 Downloads: 0
Author(s)
Index Terms
Internet of Things, Cybersecurity, Machine Learning, DDoS Attacks, CIC-IoT2023 Dataset
Abstract
An increase in cyber threats directed at interconnected devices has resulted from the proliferation of the Internet of Things (IoT), which necessitates the implementation of comprehensive defenses against evolving attack vectors. This research investigates the utilization of machine learning (ML) prediction models to identify and defend against cyber-attacks targeting IoT networks. Central emphasis is placed on the thorough examination of the CIC-IoT2023 dataset, an extensive collection comprising a wide range of Distributed Denial of Service (DDoS) assaults on diverse IoT devices. This ensures the utilization of a practical and comprehensive benchmark for assessment. This study develops and compares four distinct machine learning models Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF) to determine their effectiveness in detecting and preventing cyber threats to the Internet of Things (IoT). The comprehensive assessment incorporates a wide range of performance indicators, such as F1-score, accuracy, precision, and recall. Significantly, the results emphasize the superior performance of DT and RF, demonstrating exceptional accuracy rates of 0.9919 and 0.9916, correspondingly. The models demonstrate an outstanding capability to differentiate between benign and malicious packets, as supported by their high precision, recall, and F1 scores. The precision-recall curves and confusion matrices provide additional evidence that DT and RF are strong contenders in the field of IoT intrusion detection. Additionally, KNN demonstrates a noteworthy accuracy of 0.9380. On the other hand, LR demonstrates the least accuracy with a value of 0.8275, underscoring its inherent incapability to classify threats. In conjunction with the realistic and diverse characteristics of the CIC-IoT2023 dataset, the study's empirical assessments provide invaluable knowledge for determining the most effective machine learning algorithms and fortification strategies to protect IoT infrastructures. Furthermore, this study establishes ground-breaking suggestions for subsequent inquiries, urging the examination of unsupervised learning approaches and the incorporation of deep learning models to decipher complex patterns within IoT networks. These developments have the potential to strengthen cybersecurity protocols for Internet of Things (IoT) ecosystems, reduce the impact of emergent risks, and promote robust defense systems against ever-changing cyber challenges.
Cite This Paper
Akinul Islam Jony, Arjun Kumar Bose Arnob, "Securing the Internet of Things: Evaluating Machine Learning Algorithms for Detecting IoT Cyberattacks Using CIC-IoT2023 Dataset", International Journal of Information Technology and Computer Science(IJITCS), Vol.16, No.4, pp.56-65, 2024. DOI:10.5815/ijitcs.2024.04.04
Reference
[1]U. Tariq, I. Ahmed, A. K. Bashir, K. Shaukat, “A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review”. Sensors, Vol. 23, No. 8, 2023. DOI: https://doi.org/10.3390/s23084117
[2]X. Cheng, J. Zhang, B. Chen, “Cyber Situation Comprehension for IoT Systems based on APT Alerts and Logs Correlation”, Sensors, Vol.19, No.18, 2019. DOI: https://doi.org/10.3390/s19184045
[3]P. K. Sadhu, V. P. Yanambaka, A. Abdelgawad, “Internet of Things: Security and Solutions Survey”, Sensors, Vol. 22, No. 19, 2022. DOI: https://doi.org/10.3390/s22197433
[4]S. Kumar, P. Tiwari, M. Zymbler, “Internet of Things is a revolutionary approach for future technology enhancement: a review”, Journal of Big Data, Vol.6, No.1, pp.1-21, 2019. DOI: https://doi.org/10.1186/s40537-019-0268-2
[5]J. P. A. Yaacoub, H. N. Noura, O. Salman, A. Chehab, “Robotics cyber security: Vulnerabilities, attacks, countermeasures, and recommendations”, International Journal of Information Security, Vol.21, pp.115-158, 2022. DOI: https://doi.org/10.1007/s10207-021-00545-8
[6]Check Point Research, “The Tipping Point: Exploring the Surge in IoT Cyberattacks Globally”, 2023. Retrieved on October 12, 2023, from https://blog.checkpoint.com/security/the-tipping-point-exploring-the-surge-in-iot-cyberattacks-plaguing-the-education-sector/
[7]K. Tsiknas, D. Taketzis, K. Demertzis, C. Skianis, “Cyber threats to industrial IoT: a survey on attacks and countermeasures”, IoT, Vol. 2, No. 1, pp. 163-186, 2021. DOI: https://doi.org/10.3390/iot2010009
[8]M. Abdullahi, Y. Baashar, H. Alhussian, A. Alwadain, N. Aziz, L. F. Capretz, S. J. Abdulkadir, “Detecting cybersecurity attacks in the internet of things using artificial intelligence methods: A systematic literature review”, Electronics, Vol. 11, No. 2, 2022. DOI: https://doi.org/10.3390/electronics11020198
[9]Ani Petrosyan, “Annual number of IoT attacks global 2022”, 2023. Retrieved on October 12, 2023 https://www.statista.com/statistics/1377569/worldwide-annual-internet-of-things-attacks/
[10]M. Ahsan, K. E. Nygard, R. Gomes, M. M. Chowdhury, N. Rifat, J. F. Connolly, “Cybersecurity threats and their mitigation approaches using Machine Learning—A Review”, Journal of Cybersecurity and Privacy, Vol. 2, No. 3, pp. 527-555, 2022.
[11]Matthew Urwin, “Machine Learning in Cybersecurity: How It Works and Companies to Know”, 2023. Retrieved on October 12, 2023 https://builtin.com/artificial-intelligence/machine-learning-cybersecurity
[12]J. Singh, J. Singh, “A survey on machine learning-based malware detection in executable files”, Journal of Systems Architecture, Vol. 112, 2021.
[13]N. Vadivelan, K. Bhargavi, S. Kodati, M. Nalini, “Detection of cyber-attacks using machine learning. In AIP Conference Proceedings.” AIP Publishing. Vol. 2405, No. 1, 2022.
[14]E. C. P. Neto, S. Dadkhah, R. Ferreira, A. Zohourian, R. Lu, A. A. Ghorbani, “CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment”, Sensors, Vol. 23, No. 13, 2023. DOI: https://doi.org/10.3390/s23135941
[15]U. Inayat, M. F. Zia, S. Mahmood, H. M. Khalid, M. Benbouzid, “Learning-based methods for cyber-attack detection in IoT systems: A survey on methods, analysis, and future prospects”, Electronics, Vol. 11, No. 9, 2022.
[16]E. Adi, A. Anwar, Z. Baig, S. Zeadally, “Machine learning and data analytics for the IoT”, Neural computing and applications, Vol. 32, pp. 16205-16233, 2020.
[17]C. Malathi, I. N. Padmaja, “Identification of cyber-attacks using machine learning in smart IoT networks”, Materials Today: Proceedings, Vol. 80, pp. 2518-2523, 2023.
[18]O. A. Alkhudaydi, M. Krichen, A. D. Alghamdi, “A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things. Information”, Vol. 14, No. 10, pp. 550, 2023.
[19]J. G. Almaraz-Rivera, J. A. Perez-Diaz, 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, 2022.
[20]J. Kim, M. Shim, S. Hong, Y. Shin, E. Choi, E. “Intelligent detection of IoT botnets using machine learning and deep learning”, Applied Sciences, Vol. 10, No. 19, 2023.
[21]S. Dalal, U. K. Lilhore, N. Foujdar, S. Simaiya, M. Ayadi, N. A. Almujally, A. Ksibi, “Next-generation cyber-attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree”, Journal of Cloud Computing, Vol. 12, No. 1, pp. 1-20, 2023.
[22]O. Jullian, B. Otero, E. Rodriguez, N. Gutierrez, H. Antona, R. Canal, “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, pp. 33, 2023.