DDoS Saldırılarının ve Kuyruk Yönetimi Algoritmalarının Hücresel Ağlar Üzerindeki Etkisi

Yıl 2024, Cilt: 7 Sayı: 1, 1 - 13, 29.03.2024

Muhammet Çakmak

https://doi.org/10.38016/jista.1225716

Öz

Günümüzün dijital ortamında Dağıtılmış Hizmet Reddi (DDoS) saldırıları, dünyanın her yerindeki kuruluşlar için büyük bir tehdit olarak öne çıkıyor. Bilinen teknolojinin giderek ilerlemesi ve mobil cihazların yaygınlaşmasıyla hücresel şebeke operatörleri, altyapılarını bu risklere karşı güçlendirme baskısıyla karşı karşıya kalıyor. Hücresel Uzun Vadeli Evrim (LTE) ağlarına yapılan DDoS saldırıları büyük hasara, yüksek paket kaybına ve yetersiz ağ performansına yol açabilir. LTE ağlarını etkileyen trafikteki dalgalanmaları yönetmek büyük önem taşıyor. Kuyruk yönetimi algoritmaları, LTE ağları içindeki Radyo Bağlantı Kontrolü (RLC) katmanındaki tıkanıklığın kontrolünü ele geçirmek için geçerli bir çözüm olarak ortaya çıkıyor. Bu algoritmalar proaktif olarak çalışır, veri aktarım hızlarını azaltarak ve potansiyel DDoS saldırılarına karşı savunmayı güçlendirerek tıkanıklığı öngörür ve azaltır. Bu yazıda, Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE) ve Packet Limited First In, First Out queue (pFIFO) gibi çeşitli kuyruk yönetimi yöntemlerini derinlemesine inceliyoruz. Bu kuyruk yönetimi algoritmalarına yönelik titiz değerlendirmemiz, hayati performans parametrelerini kapsayan çok yönlü bir değerlendirmeye dayanır. LTE ağının DDoS saldırılarına karşı dayanıklılığını ölçüyoruz; performansı uçtan uca gecikmeye, üretime, paket dağıtım hızına (PDF) ve adalet endeksi değerlerine göre ölçüyoruz. Bu değerlendirmenin potası, test ve analiz için güvenilir bir platform olan NS3 simülatöründen başkası değildir. Simülasyonlarımızın sonuçları aydınlatıcı bilgiler sağlıyor. CoDel, RED, PIE, pFIFO ve Drop-Tail algoritmaları art arda en iyi performans gösterenler olarak ortaya çıkıyor. Bu bulgular, gelişmiş kuyruk yönetimi algoritmalarının, LTE ağlarını DDoS saldırılarına karşı güçlendirme, sağlam savunmalar ve esnek ağ performansı sunma konusundaki kritik rolünün önemini göstermektedir.

Anahtar Kelimeler

DDoS atakları, LTE ağı, Ns-3 simülasyonu

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

Öz

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

Anahtar Kelimeler

DDoS attacks, LTE network, Ns-3 simulation

Kaynakça

• Albayrak, Z., Çakmak, M., 2018. A Review: Active Queue Management Algorithms in Mobile Communication. International Conference on Cyber Security and Computer Science 180–184.
• Ali, S.M., Çakmak, M., Albayrak, Z., 2022. Security Classification of Smart Devices Connected to LTE Network, in: Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-030-94191-8_91
• Amer, H., Al-Kashoash, H., Khami, M.J., Mayfield, M., Mihaylova, L., 2020. Non-cooperative game based congestion control for data rate optimization in vehicular ad hoc networks. Ad Hoc Networks 107. https://doi.org/10.1016/j.adhoc.2020.102181
• Ashfaq, M.F., Malik, M., Fatima, U., Shahzad, M.K., 2022. Classification of IoT based DDoS Attack using Machine Learning Techniques, in: 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, pp. 1–6. https://doi.org/10.1109/IMCOM53663.2022.9721740
• Bisoy, S.K., Pattnaik, P.K., 2016. Design of feedback controller for TCP/AQM networks. Engineering Science and Technology, an International Journal 20. https://doi.org/http://dx.doi.org/10.1016/j.jestch.2016.10.002
• Çakmak, M., Albayrak, Z., 2022. AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks. Mobile Networks and Applications 27. https://doi.org/10.1007/s11036-022-02011-8
• Çakmak, M., Albayrak, Z., 2020. Performance Analysis of Queue Management Algorithms Between Remote-Host and PG-W in LTE Networks. Academic Platform Journal of Engineering and Science 456–463. https://doi.org/10.21541/apjes.662677
• Çakmak, M., Albayrak, Z., Torun, C., 2021. Performance comparison of queue management algorithms in lte networks using NS-3 simulator. Tehnicki Vjesnik 28. https://doi.org/10.17559/TV-20200411071703
• F. M. Suaib Akhter, A., F. M. Shahen Shah, A., Ahmed, M., Moustafa, N., Çavuşoğlu, U., Zengin, A., 2021. A Secured Message Transmission Protocol for Vehicular Ad Hoc Networks. Computers, Materials & Continua 68, 229–246. https://doi.org/10.32604/cmc.2021.015447
• Gomez, C.A., Wang, X., Shami, A., 2021. Federated intelligence for active queue management in inter-domain congestion. IEEE Access 9, 10674–10685. https://doi.org/10.1109/ACCESS.2021.3050174
• Gómez, G., Pérez, Q., Lorca, J., García, R., 2014. Quality of service drivers in LTE and LTE-A networks. Wirel Pers Commun 75, 1079–1097. https://doi.org/10.1007/s11277-013-1409-0
• Gong, Y., Cao, J., Fu, Y., Guo, M., 2019. A DDoS attack detection model for LTE-A network. Journal of Cyber Security 4. https://doi.org/10.19363/J.cnki.cn10-1380/tn.2019.01.03
• Israr, A., Yang, Q., Li, W., Zomaya, A.Y., 2021. Renewable energy powered sustainable 5G network infrastructure: Opportunities, challenges and perspectives. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2020.102910
• Jevtić, M., Zogović, N., Dimić, G., 2009. Evaluation of Wireless Sensor Network Simulators. Proceedings of the 17th Telecommunications Forum TELFOR 2009 Belgrade Serbia.
• Jyothi, K.K., Chaudhari, S., 2020. Optimized neural network model for attack detection in LTE network. Computers & Electrical Engineering 88, 106879. https://doi.org/10.1016/j.compeleceng.2020.106879
• Krishnan, P., Duttagupta, S., Achuthan, K., 2019. SDNFV Based Threat Monitoring and Security Framework for Multi-Access Edge Computing Infrastructure. Mobile Networks and Applications 24. https://doi.org/10.1007/s11036-019-01389-2
• Lin, Y., Li, L., Ren, P., Wang, Y., Szeto, W.Y., 2021. From aircraft tracking data to network delay model: A data-driven approach considering en-route congestion. Transp Res Part C Emerg Technol 131. https://doi.org/10.1016/j.trc.2021.103329
• Low, S.H., Paganini, F., Jiantao Wang, Adlakha, S., Doyle, J.C., n.d. Dynamics of TCP/RED and a scalable control, in: Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, pp. 239–248. https://doi.org/10.1109/INFCOM.2002.1019265
• Mousavi, H., Amiri, I.S., Mostafavi, M.A., Choon, C.Y., 2019. LTE physical layer: Performance analysis and evaluation. Applied Computing and Informatics 15. https://doi.org/10.1016/j.aci.2017.09.008
• Mousavi, H., Amiri, I.S., Mostafavi, M.A., Choon, C.Y., 2017. LTE physical layer: Performance analysis and evaluation. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2017.09.008
• N.D., A., A., R., 2019. Avoiding queue overflow and reducing queuing delay at eNodeB in LTE networks using congestion feedback mechanism. Comput Commun 146, 131–143. https://doi.org/10.1016/j.comcom.2019.07.015
• Oughton, E.J., Comini, N., Foster, V., Hall, J.W., 2022. Policy choices can help keep 4G and 5G universal broadband affordable. Technol Forecast Soc Change 176. https://doi.org/10.1016/j.techfore.2021.121409
• Pan, R., Natarajan, P., Piglione, C., Prabhu, M.S., Subramanian, V., Baker, F., VerSteeg, B., 2013. PIE: A lightweight control scheme to address the bufferbloat problem. IEEE International Conference on High Performance Switching and Routing, HPSR 148–155. https://doi.org/10.1109/HPSR.2013.6602305
• Paul, A., Kawakami, H., Tachibana, A., Hasegawa, T., 2017. Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network. Technologies (Basel) 5, 59. https://doi.org/10.3390/technologies5030059
• Praveen, K. v., Prathap, P.M.J., 2021. Energy Efficient Congestion Aware Resource Allocation and Routing Protocol for IoT Network using Hybrid Optimization Techniques. Wirel Pers Commun 117. https://doi.org/10.1007/s11277-020-07917-8
• Raghuvanshi, D.M., Annappa, B., Tahiliani, M.P., 2013. On the effectiveness of CoDel for active queue management. International Conference on Advanced Computing and Communication Technologies, ACCT 107–114. https://doi.org/10.1109/ACCT.2013.27
• Said, A.A., Çakmak, M., Albayrak, Z., 2022. Performance of Ad-Hoc Networks Using Smart Technology Under DDoS Attacks, in: Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-3-030-94191-8_92
• Swain, S.K., Nanda, P.K., 2021. Adaptive queue management and traffic class priority based fairness rate control in wireless sensor networks. IEEE Access 9. https://doi.org/10.1109/ACCESS.2021.3102033
• Szyguła, J., Domański, A., Domańska, J., Marek, D., Filus, K., Mendla, S., 2021. Supervised learning of neural networks for active queue management in the internet. Sensors 21. https://doi.org/10.3390/s21154979
• Verma, H., Chauhan, N., Chand, N., Awasthi, L.K., 2022. Buffer-loss estimation to address congestion in 6LoWPAN based resource-restricted ‘Internet of Healthcare Things’ network. Comput Commun 181, 236–256. https://doi.org/10.1016/j.comcom.2021.10.016
• Wang, T., Wang, M., 2020. Hyperchaotic image encryption algorithm based on bit-level permutation and DNA encoding. Opt Laser Technol 132, 106355. https://doi.org/10.1016/j.optlastec.2020.106355
• Weingärtner, E., vom Lehn, H., Wehrle, K., 2009. A performance comparison of recent network simulators, in: IEEE International Conference on Communications. https://doi.org/10.1109/ICC.2009.5198657
• Wu, Z., Zhu, M., Li, Q., Xue, L., Yang, J., Chen, Z., Cao, Y., Cui, Y., 2022. Design of power monitoring system for new energy grid-connected operation based on LoRa and 4G technology. Energy Reports 8, 95–105. https://doi.org/10.1016/j.egyr.2022.10.038
• Zenitani, K., 2023. From attack graph analysis to attack function analysis. Inf Sci (N Y) 119703. https://doi.org/10.1016/j.ins.2023.119703
• Zidic, D., Mastelic, T., Nizetic Kosovic, I., Cagalj, M., Lorincz, J., 2023. Analyses of ping-pong handovers in real 4G telecommunication networks. Computer Networks 227. https://doi.org/10.1016/j.comnet.2023.109699