Intrusion Detection based on Machine Learning Techniques in IoT Datasets

Objective & Hypothesis:Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are types of attacks that aim system crash and cause damage, and Port Scanning attacks are types of attacks that aim to collect data from the system.

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Key Findings & Data:In this study, DoS, DDoS and Scanning attacks on “Bot-IoT” and “ToN_IoT” datasets are classified using Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Gradient Boosting (GB), Linear Discriminant Analysis (LDA) and Extra Trees (ET) machine learning algorithms.

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Conclusion & Impact:The experimental results show that the GB classifier can achieve the best classification with an F1-score of 99.9944%.

🔬Design:Modeling & Simulation

📍Scope:Comprehensive Sample

⚙️Variables:Saldırı Tespit Sistemi · DoS · DDoS

Abstract

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are types of attacks that aim system crash and cause damage, and Port Scanning attacks are types of attacks that aim to collect data from the system. In this study, DoS, DDoS and Scanning attacks on “Bot-IoT” and “ToN_IoT” datasets are classified using Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive-Bayes (NB), Gradient Boosting (GB), Linear Discriminant Analysis (LDA) and Extra Trees (ET) machine learning algorithms. The experimental results show that the GB classifier can achieve the best classification with an F1-score of 99.9944%.

Özet

Servis Hizmet Reddi ve Dağıtık Servis Hizmet Reddi saldırıları sistemleri çökertmeyi ve hasar vermeyi amaçlarken, Port Tarama saldırısı ise sistemden veri toplamayı amaçlayan siber saldırı türlerindendir. Bu çalışmada, Rastgele Orman, Karar Ağacı, Destek Vektör Makinesi, K-En Yakın Komşu, Naive-Bayes, Gradyan Artırma, Doğrusal Diskriminant Analizi ve Ekstra Ağaçlar makine öğrenmesi algoritmaları kullanılarak, “Bot-IoT” ve “ToN_IoT” veri kümeleri üzerinde DoS, DDoS ve Scanning saldırıları sınıflandırılmıştır. Yapılan deneyler, Gradyan Artırma sınıflandırıcı ile %99.9944 F1-skorla en iyi sınıflandırma gerçekleştirildiğini göstermiştir.

Keywords

Saldırı Tespit Sistemi DoS DDoS makine öğrenmesi Intrusion Detection System machine learning

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Cite This Article

Pehlivanoğlu, M. K., Kuyucu, A., Kaya, R., Aydın, R. (2023). Intrusion Detection based on Machine Learning Techniques in IoT Datasets. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 52, 19–26. https://doi.org/10.31590/ejosat.1184984

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References (12)

Booij, T. M., Chiscop, I., Meeuwissen, E., Moustafa, N., & Hartog, F. T. H. D. (2022, January 1). ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets. IEEE Internet of Things Journal, 9(1), 485–496. https://doi.org/10.1109/jiot.2021.3085194
Falcao, X. A., & Papa, J. P. (2022, February 7). Optimum-Path Forest: Theory, Algorithms, and Applications (1st ed.). Academic Press, 68.
Ioannou, C.; Vassiliou, V. Network Attack Classification in IoT Using Support Vector Machines. J. Sens. Actuator Netw. 2021, 10, 58. https://doi.org/10.3390/jsan10030058
Islam, U., Muhammad, A., Mansoor, R., Hossain, M. S., Ahmad, I., Eldin, E. T., Khan, J. A., Rehman, A. U., & Shafiq, M. (2022, July 8). Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models. Sustainability, 14(14), 8374. https://doi.org/10.3390/su14148374
Koroniotis, N. (2018, November 2). Towards the Development of Realistic Botnet Dataset in the. arXiv.org. Retrieved September 6, 2022, from https://arxiv.org/abs/1811.00701
Kozik, R., Pawlicki, M. & Choraś, M. A new method of hybrid time window embedding with transformer-based traffic data classification in IoT-networked environment. Pattern Anal Applic 24, 1441–1449 (2021). https://doi.org/10.1007/s10044-021-00980-2
M. Erfani et al., "A feature exploration approach for IoT attack type classification," 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2021, pp. 582-588, doi: 10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00101.
Nascita, A., Cerasuolo, F., Monda, D. D., Garcia, J. T. A., Montieri, A., & Pescape, A. (2022, May 2). Machine and Deep Learning Approaches for IoT Attack Classification. IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). https://doi.org/10.1109/infocomwkshps54753.2022.9797971
Sahu, A. K., Sharma, S., Tanveer, M., & Raja, R. (2021, August). Internet of Things attack detection using hybrid Deep Learning Model. Computer Communications, 176, 146–154. https://doi.org/10.1016/j.comcom.2021.05.024
Ullah, I., & Mahmoud, Q. H. (2021). Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks. IEEE Access, 9, 103906–103926. https://doi.org/10.1109/access.2021.3094024
Wozniak, M., Silka, J., Wieczorek, M., & Alrashoud, M. (2021, August). Recurrent Neural Network Model for IoT and Networking Malware Threat Detection. IEEE Transactions on Industrial Informatics, 17(8), 5583–5594. https://doi.org/10.1109/tii.2020.3021689
Koroniotis, N. (2018, November 2). Towards the Development of Realistic Botnet Dataset in the. arXiv.org. Retrieved September 6, 2022, from https://arxiv.org/abs/1811.00701