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Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

M. Hanefi Calp Resul Bütüner

https://doi.org/10.2339/politeknik.1340515

Öz

Today, the number and variety of cyber-attacks on all systems have increased with the widespread use of internet technology. Within these systems, Internet of Things (IoT)-based network devices are especially exposed to a lot of cyber-attacks and are vulnerable to these attacks. This adversely affects the operation of the devices in question, and the data is endangered due to security vulnerabilities. Therefore, in this study, a model that detects cyber-attacks to ensure security with machine learning (ML) algorithms were proposed by using the data obtained from the log records of an IoT-based system. For this, first, the dataset was created, and this dataset was preprocessed and prepared in accordance with the models. Then, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Logistic Regression (LR) algorithms were used to create the models. As a result, the best performance to detect cyber-attacks was obtained using the RF algorithm with a rate of 99.6%. Finally, the results obtained from all the models created were compared with other academic studies in the literature and it was seen that the proposed RF model produced very successful results compared to the others. Moreover, this study showed that RF was a promising method of attack detection.

Anahtar Kelimeler

Internet of things network devices security cyber-attack machine learning

Kaynakça

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Makine Öğrenimi Algoritmaları Kullanılarak IoT Tabanlı Ağ Cihazlarına Yönelik Siber Saldırıların Tespiti

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

M. Hanefi Calp Resul Bütüner

https://doi.org/10.2339/politeknik.1340515

Öz

Günümüzde internet teknolojisinin yaygınlaşmasıyla birlikte tüm sistemlere yönelik siber saldırıların sayısı ve çeşidi artmıştır. Bu sistemler içerisinde özellikle Nesnelerin İnterneti (IoT) tabanlı ağ cihazları çok sayıda siber saldırıya maruz kalmakta ve bu saldırılara karşı savunmasız kalmaktadır. Bu durum söz konusu cihazların çalışmasını olumsuz etkilemekte ve güvenlik açıkları nedeniyle veriler tehlikeye girmektedir. Bu nedenle bu çalışmada IoT tabanlı bir sistemin log kayıtlarından elde edilen veriler kullanılarak makine öğrenmesi (ML) algoritmaları ile güvenliği sağlamak için siber saldırıları tespit eden bir model önerilmiştir. Bunun için öncelikle veriseti oluşturulmuş ve bu veriseti ön işleme tabi tutularak modellere uygun olarak hazırlanmıştır. Ardından modelleri oluşturmak için Yapay Sinir Ağı (YSA), Rastgele Orman (RF), K-En Yakın Komşu (KNN), Naive Bayes (NB) ve Lojistik Regresyon (LR) algoritmaları kullanılmıştır. Sonuç olarak, siber saldırıları tespit etmede en iyi performans %99.6 ile RF algoritması kullanılarak elde edilmiştir. Son olarak oluşturulan tüm modellerden elde edilen sonuçlar literatürdeki diğer akademik çalışmalarla karşılaştırılmış ve önerilen RF modelinin diğerlerine göre oldukça başarılı sonuçlar ürettiği görülmüştür. Ayrıca, bu çalışma RF'nin gelecek vaat eden bir saldırı tespit yöntemi olduğunu göstermiştir.

Anahtar Kelimeler

Nesnelerin interneti ağ cihazları güvenlik siber saldırı makine öğrenimi

Kaynakça

  • [1] Scarfone, K., Mell P, “Guide to intrusion detection and prevention systems (IDPS)”, NIST, ABD, (2007).
  • [2] Ganapathy, S., Kulothungan K., Muthurajkumar S.,Vijayalakshmi M., Yogesh P. & Kannan A., “Intelligent feature selection and classification techniques for intrusion detection in networks: a survey”, EURASIP Journal on Wireless Communications and Networking, 1:273-289, (2013).
  • [3] Kolias, C., Kambourakis G. & Maragoudakis M, “Swarm Intelligence in Intrusion Detection: A Survey”, Computers and Security, 30 (8): 625-642, (2011).
  • [4] Behera, S., Pradhan, A., & Dash, R. “Deep neural network architecture for anomaly based intrusion detection system”. In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 270-274). IEEE, (2018, February).
  • [5] Aksu, D., & Aydin, M. A. “Detecting port scan attempts with comparative analysis of deep learning and support vector machine algorithms”. In 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) (pp. 77-80). IEEE, (2018, December).
  • [6] Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. “Deep learning approach for intelligent intrusion detection system”. IEEE Access, 7: 41525-41550, (2019).
  • [7] Hajisalem, V., Babaie, S., “A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection”, Computer Networks, 136: 37-50, (2018).
  • [8] Inayat, Z., Gani, A., Anuar, N. B., Khan, M. K. & Anwar, S., “Intrusion response systems: Foundations, design, and challenges”, Journal of Network and Computer Applications, 62: 53-74, (2016).
  • [9] Ashoor, A. S., Gore, S., “Difference between intrusion detection system (IDS) and intrusion prevention system (IPS)”, In International Conference on Network Security and Applications, 497-501, Berlin, Heidelberg, (2011).
  • [10] Jabez, J., Muthukumar, B., “Intrusion detection system (IDS): anomaly detection using outlier detection approach”, Procedia Computer Science, 48: 338-346, (2015).
  • [11] Quepons, I., “Vulnerability and Trust”, PhaenEx, 13, 2: 1-10, (2020).
  • [12] Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. & Vázquez, E., “Anomaly-based network intrusion detection: Techniques, systems and challenges”, Computers and Security, 28: 1-2, 18-28, (2009).
  • [13] Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, in ICISSP, Prague, Czech Republic, pp. 108-116, (2018).
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  • [15] Demir, F. “Investigation of performance of ML methods for cyber-attack detection””, Journal of Balikesir University Institute of Science, 23(2): 782-791, (2021).
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  • [19] Hatipoğlu, C., & Tunacan, T. Hatipoglu, C., & Tunacan, T. “Cyber Attacks and Detection Method in Turkey: A Literature Review”. BSEU Journal of Science, (2021).
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  • [22] Karimipour, H., Dehghantanha, A., Parizi, R. M., Choo, K. K. R., & Leung, H., “A deep and scalable unsupervised ML system for cyber-attack detection in large-scale smart grids”. IEEE Access, 7: 80778-80788, (2019).
  • [23] Kavousi-Fard, A., Su, W., & Jin, T. “A machine-learning-based cyber attack detection model for wireless sensor networks in microgrids”. IEEE Transactions on Industrial Informatics, 17(1): 650-658, (2020).
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  • [26] Smys, S. “DDOS attack detection in telecommunication network using ML”. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 1(01): 33-44, (2019).
  • [27] Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. “A review of intrusion detection systems using machine and deep learning in internet of things: challenges, solutions and future directions”. Electronics, 9(7): 1177, (2020).
  • [28] Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. “Cyberattacks detection in IoT-based smart city applications using ML techniques”. International Journal of environmental research and public health, 17(24): 9347, (2020).
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  • [30] Dutta, V., Choraś, M., Pawlicki, M., & Kozik, R. “A deep learning ensemble for network anomaly and cyber-attack detection”. Sensors, 20(16): 4583, (2020).
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  • [33] Savaş, T. & Savaş, S. “Tekdüzen Kaynak Bulucu Yoluyla Kimlik Avı Tespiti için Makine Öğrenmesi Algoritmalarının Özellik Tabanlı Performans Karşılaştırması”. Politeknik Dergisi , 25 (3): 1261-1270 . DOI: 10.2339/politeknik.1035286, (2022).
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Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme, Makine Öğrenme (Diğer), Bilgi Temsili ve Akıl Yürütme
Bölüm Araştırma Makalesi
Yazarlar

M. Hanefi Calp 0000-0001-7991-438X

Resul Bütüner 0000-0002-9778-2349

Erken Görünüm Tarihi 5 Şubat 2024
Yayımlanma Tarihi
Gönderilme Tarihi 10 Ağustos 2023
Yayımlandığı Sayı Yıl 2024 ERKEN GÖRÜNÜM

Kaynak Göster

APA Calp, M. H., & Bütüner, R. (2024). Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1340515
AMA Calp MH, Bütüner R. Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi. Published online 01 Şubat 2024:1-1. doi:10.2339/politeknik.1340515
Chicago Calp, M. Hanefi, ve Resul Bütüner. “Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms”. Politeknik Dergisi, Şubat (Şubat 2024), 1-1. https://doi.org/10.2339/politeknik.1340515.
EndNote Calp MH, Bütüner R (01 Şubat 2024) Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi 1–1.
IEEE M. H. Calp ve R. Bütüner, “Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms”, Politeknik Dergisi, ss. 1–1, Şubat 2024, doi: 10.2339/politeknik.1340515.
ISNAD Calp, M. Hanefi - Bütüner, Resul. “Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms”. Politeknik Dergisi. Şubat 2024. 1-1. https://doi.org/10.2339/politeknik.1340515.
JAMA Calp MH, Bütüner R. Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi. 2024;:1–1.
MLA Calp, M. Hanefi ve Resul Bütüner. “Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms”. Politeknik Dergisi, 2024, ss. 1-1, doi:10.2339/politeknik.1340515.
Vancouver Calp MH, Bütüner R. Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi. 2024:1-.
 
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