Performance Trends of UDP and TCP in Cellular Networks Before 5G

Abstract:
This project aims to assess and compare the performance of UDP and TCP protocols in cellular network environments before the introduction of 5G technology (i.e. 3G and 4G) using the ns-3 simulation framework.
We will accomplish this through the procedure below:

References:
[1] R. Galazzo, "Timeline from 1G to 5G: A Brief History on Cell Phones," CENGN, 21 Sep. 2020, updated 24 Jan. 2022.
[2] S. A. Almowuena, M. A. Alghamdi, A. X. Liu, H. S. Alzahrani, and A. Al-Nasheri, "Performance evaluation of TCP, UDP and DCCP traffic over 4G network," 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015, pp. 1-6.
[3] Y. Zhang, N. Zhang, P. Yang, G. Guan, and X. Shen, "A Longitudinal Measurement Study of TCP Performance and Behavior in 3G/4G Networks Over High Speed Rails," IEEE Transactions on Mobile Computing, vol. 16, no. 2, pp. 511-524, Feb. 2017.
[4] A. Kumar, R. Kumar, and J. Akhtar, "Comparative studies on 3G,4G and 5G wireless technology,D" 2013 3rd IEEE International Advance Computing Conference (IACC), 2013, pp. 464-469.
[5] S. Nadel, "3G vs. 4G: What’s the Difference?" PCMag, 21 Mar. 2019.
[6] J. Davies, “5G vs 4G vs 3G: Comparing Generations of Mobile Network Technology,” WhistleOut, 25 Mar. 2019.

Determining the effects of DDOS attacks on Wifi and Ethernet

Abstract:
Distributed denial-of-service (DDoS) attacks are a great threat in the modern day and can have a major effect on people using the free internet since they can prevent individuals from accessing services and websites. Wifi and Ethernet connection are how a vast majority in how people access the internet and this project plans to analyze the effects of a DDoS attack on both of these communication systems. This study will do simulations using ns-3 software to see how both Wifi and Ethernet connection perform under throttle of a DDoS attack with changing variables of signal speed, strength and throughput. This study hopes to draw conclusions about the differences between Wifi and Ethernet while under the load of a DDoS attack and to determine the optimal variable settings to maximize bandwidth during these attacks.

References:
[1] I. Gupta and A. Kaur., "Comparative Throughput of WiFi & Ethernet LANs using OPNET MODELER," IJCST Vol. 1, Issue 2, Dec. 2010. [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1283e12c7a0797d64038271155c62a7c0d0716e0
[2] G. Hernandez-Oregon et al., "Performance evaluation and comparison of LiFi, WiFi and Ethernet networks in real environments," Research in Computing Science, Sept. 2022. [Online]. Available: https://www.researchgate.net/profile/Gerardo-Hernandez-Oregon/publication/374166176_Performance_evaluation_and_comparison_on_LiFi_WiFi_and_Ethernet_networks_in_real_environments/links/6511d8792c6cfe2cc20ff2ee/Performance-evaluation-and-comparison-on-LiFi-WiFi-and-Ethernet-networks-in-real-environments.pdf
[3] cloudflare. "What is a DDos attack?" cloudflare.com. https://www.cloudflare.com/learning/ddos/what-is-a-ddos-attack/
[4] S. Krishnan and D. Steinberg-Zwirek. "DDoS Protection: 8 Simple TacticsD" blackberry.com. https://blogs.blackberry.com/en/2022/11/ddos-attack-8-simple-prevention-and-mitigation-strategies
[5] R. Vishwakarma & A. K. Jain., "A survey of DDoS attacking techniques and defense mechanisms in the IoT network," Telecommunication Systems, Vol. 73, pp. 3-25. Jul. 2019 [Online]. Available: https://link.springer.com/article/10.1007/s11235-019-00599-z

Performance Evaluation as a Function of Position of 5.0 GHz IEEE 802.11ac in a Residential Environment

Abstract:
Wireless Local-area Networks were first introduced in 1997 with IEEE 802.11. The IEEE 802.11 protocol provided wireless link speeds of 2 Mbps. As time went on and bandwidth increased so did the requirements of the link speeds in wireless local-area networks. One of the latest implementations of the IEEE 802.11 protocol comes in the form of the IEEE 802.11ac for a 5GHz radio signal and has link speeds up to 6933 Mbps. The 5GHz signal has higher bandwidth capabilities than its 2.4 GHz IEEE 802.11n sibling but at the cost of a shorter range. In this report, we will explore the performance of the IEEE 802.11ac 5GHz signal as a function of position using the Network Simulator 3 environment. The final results will then be applied to form a general template on how to setup a wireless local-area network in a residential environment.

Referencest:
[1] Ravindranath, N. S, Singh, I, Prasad, A, & Rao, V. S, "Performance Evaluation of IEEE 802.11 ac and 802.11 n using NS3," Indian Journal of Science and Technology, 9(26), 1-8, [Online]. Available: https://indjst.org/articles/performance-evaluation-of-ieee-80211ac-and-80211n-using-ns3
[2] Amewuda, A. B, Katsriku, F. A, & Abdulai, J. D, "Implementation and Evaluation of WLAN 802.11 ac for Residential Networks in NS-3. Journal of Computer Networks and Communications," Hindawi.com. 2018, 1-10. [Online]. Available: https://www.hindawi.com/journals/jcnc/2018/3518352/
[3] Rochim, A. F, and Sari, R. F, “Performance comparison of IEEE 802.11 n and IEEE 802.11 ac,” International Conference on Computer, Control, Informatics and its Applications (IC3INA) (pp. 54-59). IEEE. Researchgate.net. October 2016. [Online]. Available: https://www.researchgate.net/publication/314106220
[4] Francesco G. Lavacca, Pierpaolo Salvo, Ludovico Ferranti, Andrea Speranza and Luca Costantini, "Performance Evaluation of 5G Access Technologies and SDN Transport Network on an NS3 Simulator," Mdpi.com. September 2019. [Online]. Available: https://www.mdpi.com/2073-431X/9/2/43
[5] Razvan G. Lazar, Gheorghe Asachi, Iasi, Andreea V. Militaru; Constantin F. Caruntu and Cristian Patachia–Sultanoiu, "Performance analysis of 5G communication based on distance evaluation using the SIM8200EA-M2 module," IEEE.org. November 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9931884
[6] S. Narayan, C. Jayawardena, J. Wang, W. Ma and G. Geetu, "Performance test of IEEE 802.11ac wireless devices," 2015 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2015, pp. 1-6, doi: 10.1109/ICCCI.2015.7218076. keywords: {IEEE 802.11n Standard;Throughput;Delays;Jitter;Wireless communication;Performance evaluation;IEEE 802.11ac;IEEE 802.11n 2.4 GHz band;IEEE 802.11n 5.0 GHz band;throughput;jitter;delay;drop rate}. Availablehttps://ieeexplore.ieee.org/document/7218076
[7] Z. Lu, H. Lin, Z. Wang, W. Mu, C. Li and Z. Chen, "Tapered Fed Compact MIMO Antenna for 5G Communication," 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022, pp. 1-3, doi: 10.1109/ACES-China56081.2022.10065006. keywords: {Performance evaluation;Wireless communication;5G mobile communication;Impedance matching;Simulation;Microstrip antennas;Scattering parameters;Tapered fed;5G communication;MIMO;diversity performance}. Available: https://ieeexplore.ieee.org/document/10065006

A Simulation Study of DDOS Attacks on WiFi Networks

Abstract:
With the increase in the demand of WiFi networks, the vulnerability of these networks to malicious activities such as Distributed Denial of Service (DDoS) attacks becomes a critical concern. In this project, we utilize an ns-3 simulation to model three types of DDoS attacks being SYN floods, UDP floods, and Smurf floods on WiFi networks. The goal is to understand and compare the impact of each attack on the performance of the targeted network. The simulation setup lets us intentionally create and send attack traffic to the WiFi network. This allows us to watch and study different performance measures, like throughput, packet loss, and built-in security mechanisms, helping us understand how the network behaves during attacks. By comparing these metrics we gain insights into the unique behaviors and possible countermeasures for each type of DDoS attack. The simulation outcomes allows for the development of more efficient and effective algorithms, techniques, and procedures to counteract these attacks.

References:
[1] Cloudfare, "What is a distributed denial-of-service (ddos) attack? " cloudflare, https://www.cloudflare.com/en-ca/learning/ddos/what-is-a-ddos-attack/
[2] FORTINET, “What is a DDoS Attack? DDoS Meaning, Definition & Types,” Fortinet, 2023. https://www.fortinet.com/resources/cyberglossary/ddos-attack
[3] I. Kotenko and A. Ulanov, "Simulation of Internet DDoS Attacks and Defense," Lecture Notes in Computer Science, pp. 327–342, 2006, doi: https://doi.org/10.1007/11836810_24.
[4] L. Arockiam Lawrence and Vani L., "A Survey of Denial of Service Attacks and its Countermeasures on Wireless Network," International Journal on Computer Science and Engineering, vol. 2, 2010.
[5] "Simulation and analysis of DDoS attacks | IEEE Conference Publication | IEEE Xplore," ieeexplore.ieee.org. https://ieeexplore.ieee.org/abstract/document/6513885

Simulation of Starlink LEO Satellites with ns-3 and Optional DDoS Vulnerability Assessment

Abstract:
This project proposal focuses on the simulation of Starlink's Low Earth Orbit (LEO) satellite network utilizing the ns-3 network simulator. With the objective of evaluating performance across diverse scenarios, including potential vulnerabilities to Distributed Denial of Service (DDoS) attacks, this research addresses critical challenges in the optimization and security of satellite communication networks. By leveraging simulation-based methodologies, this study aims to provide valuable insights for enhancing the reliability and resilience of Starlink's satellite internet constellation prior to real-world deployment.

References:
[1] Pandian, C. (2023, May 22). Simulating leo satellite network using NS-3 Leo Module. Simulating LEO Satellite Network using ns-3 LEO Module. https://www.projectguideline.com/simulating-leo-satellite-network-using-ns-3-leo-module/
[2] T. Schubert, L. Wolf and U. Kulau, "ns-3-leo: Evaluation Tool for Satellite Swarm Communication Protocols | IEEE Journals & Magazine | IEEE Xplore," ieeexplore.ieee.org. https://ieeexplore.ieee.org/document/9693958?denied= (accessed Feb. 25, 2024).
[3] Tim Schubert, "Tim Schubert / ns-3 LEO Module · GitLab," GitLab. https://gitlab.ibr.cs.tu-bs.de/tschuber/ns-3-leo (accessed Feb. 25, 2024).
[4] Zhang Y, Wang Y, Hu Y, Lin Z, Zhai Y, Wang L, Zhao Q, Wen K, Kang L. Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack. Sensors. 2022; 22(19):7286. https://doi.org/10.3390/s22197286
[5] Z. Wang, G. Cui, P. Li, W. Wang, and Y. Zhang, "Design and implementation of ns3-based simulation system of LEO satellite constellation for IoTs," in Proc. of 2018 IEEE 4th International Conference on Computer and Communications, December 2018, Chengdu, China [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/8781066. [Accessed: 8 Feb. 2020].

Analysis of Network Traffic Anomalies Using Machine Learning

Abstract:
In an era of escalating cyber threats and rapidly evolving network landscapes, the demand for robust anomaly detection mechanisms within communication networks has intensified. This project delves into the analysis of network traffic anomalies utilizing machine learning algorithms, with a focused exploration on local area network (LAN) traffic, web traffic, and email traffic within SFU network infrastructure.

The project aims to implement a scalable framework for collecting and preprocessing network traffic data, leveraging packet capture tools and network monitoring software. Subsequently, a comprehensive evaluation of machine learning algorithms, including Random Forest and Support Vector Machines (SVM), will be conducted to ascertain their efficacy in detecting anomalous patterns within the targeted traffic sources. The project seeks to enhance SFU's network security posture through proactive identification and mitigation of potential threats, thereby contributing to the advancement of network security practices in educational institutions and beyond.

References:
1. A. Lakhina, M. Crovella, and C. Diot, "Mining Anomalies Using Traffic Feature Distributions," in Proceedings of the ACM SIGCOMM Internet Measurement Conference (IMC), 2004.
2. J. Zhang, Y. Liu, J. Wang, and C. Chen, "Deep Learning for Network Anomaly Detection: A Survey," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 4550-4563, Nov. 2018.
3. T. Chu, W. Si, S. Simoff, and Q. Nguyen, "A Machine Learning Classification Model Using Random Forest for Detecting DDoS Attacks," presented at the 2022 Int. Symp. Networks, Computers and Communications, Shenzhen, China, July 19-22, 2022.
4. M. H. Bhuyan, D. K. Bhattacharyya, and J. Kalita, "Network Anomaly Detection: Methods, Systems and Tools," in IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 303-336, First Quarter 2014.
5. C. M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.

Simulation and Techniques against DDoS Attacks

Abstract:
Denial of Service (DoS) attack disrupts normal traffic to a target, making it unavailable to legitimate users. There are numerous tactics on attacks such as volumetric attack. A Volumetric Attack floods a target with excessive traffic, overwhelming its capacity. This would lead to legitimate users being blocked from the server. There are three mitigation techniques to DoS attacks. These include authentication, confidentiality, integrity checks, access restrictions and firewalls. The authentication uses strong authentication to verify users and systems, the confidentiality encrypts data to protect it from being accessed, integrity checks implement checks to detect unauthorized changes, access restrictions limit access to critical systems and firewalls use firewalls to monitor and block malicious traffic.

Using the ns3, it is possible to simulate volumetric attack and it is done by various people. The links below contain ns3 code to simulate the attack. The last link contains the ns3 code with the ability to detect DoS attacks.

References:
[1] Simulation on ns3 - first technique
[2] Simulation on ns3 - second technique
[3] Low rate Dos attack simulation
[4] Five different simulations
[5] Dos detection with ns3

Ddos Volumetric Attack on Online Gaming Services

Abstract:
The Internet is built as a best-effort service that relies primarily on trust between the end users. This architecture has widely evolved however the underlying foundational infrastructure of the Internet has remained the same. Security was an after-thought in building the internet as a result has left the use of internet services highly susceptible to attacks. A denial of service (Dos) attack blocks a user from accessing an internet service by flooding the network, victim, and/or the servers. When the attack comes from multiple sources it is referred to as a distributed denial of service (DDoS) attack. Our research project analyzes the effect of volumetric flood ddos attacks on online gaming servers.

References:
[1] Oleg Antipov, "3 entry points for ddos attacks in gaming services: DDoS-Guard," DDoS, https://ddos-guard.net/en/blog/ddos-attacks-in-gaming-services (accessed Feb. 22, 2024).
[2] S. Upadhyay, "DDOS simulation in NS-3 [C++]," Medium, https://infosecwriteups.com/ddos-simulation-in-ns-3-c-12f031a7b38c (accessed Feb. 22, 2024).
[3] E. Law, "What is a ddos attack?," Game Rant, https://gamerant.com/ddos-attack-explained-prevention/ (accessed Feb. 22, 2024).
[4] Keunsoo Lee et al., "DDoS attack detection method using cluster analysis," Expert Systems with Applications, https://www.sciencedirect.com/science/article/pii/S0957417407000395 (accessed Feb. 22, 2024).
[5] Furqan Rustam, et al., "Denial of service attack classification using machine classification using machine learning_with_multi-features," Research Gate, https://www.researchgate.net/publication/365618619_Denial_of_Service_Attack_Classifi cation_Using_Machine_Learning_with_Multi-Features (accessed Feb. 23, 2024)

Exploring the resilience and limitations of Bluetooth networks using ns-3

Abstract:
Our project aims to comprehensively investigate the resilience and limitations of Bluetooth networks through the utilization of ns-3 simulation, and potentially, Wireshark analysis. The study focuses on evaluating the impact of video transmission on Bluetooth networks, assessing latency factors, determining the effective range of Bluetooth communication, and leveraging existing implemented code to streamline the simulation process. By examining past projects, the objective is to identify relevant implementations on Bluetooth networks, reducing the need for entirely scratch-built solutions.

The simulation framework, ns-3, has been selected due to its robust capabilities in modeling network protocols and its ability to simulate large-scale scenarios realistically. The choice of scenarios and metrics will be based on a systematic approach, considering factors such as network topology, mobility, and interference patterns. This project aims to contribute insights into the performance characteristics of Bluetooth networks under various conditions, providing valuable information for optimizing Bluetooth communication in real-world applications. The decision-making process behind simulation choices, scenarios, and metrics will be thoroughly explained to justify the methodological approach.

References:
[1] L. Tian, S. Latre, and J. Famaey, "An IEEE 802.11ah simulation module for NS-3," 2016, doi: 10.13140/RG.2.1.1378.8244.
[2] "BlueCoDE: Bluetooth coordination in dense environment for better coexistence | IEEE Conference Publication | IEEE Xplore." Accessed: Feb. 25, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8117534
[3] Z. Hosseinkhani and M. Nabi, "BMSim: An Event-Driven Simulator for Performance Evaluation of Bluetooth Mesh Networks," IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2139–2151, Jan. 2024, doi: 10.1109/JIOT.2023.3291551.
[4] M. Chotalia and S. Gajjar, "Performance Comparison of IEEE 802.11ax, 802.11ac and 802.11n Using Network Simulator NS3," in Computing Science, Communication and Security, N. Chaubey, S. M. Thampi, N. Z. Jhanjhi, S. Parikh, and K. Amin, Eds., in Communications in Computer and Information Science. Cham: Springer Nature Switzerland, 2023, pp. 191–203. doi: 10.1007/978-3-031-40564-8_14.
[5] H. Mari, "Simulation models for the performance analysis of bluetooth low energy in multi-device environment," masterThesis, 2017. Accessed: Feb. 25, 2024. [Online]. Available: http://elib.uni-stuttgart.de/handle/11682/9560