TFAD: TCP flooding attack detection in software-defined networking using proxy-based and machine learning-based mechanisms

Sudar, K. Muthamil; Deepalakshmi, P.; Singh, Ashish; Srinivasu, Parvathaneni Naga

doi:10.1007/s10586-022-03666-4

Cited by 18 publications

(5 citation statements)

References 25 publications

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“…Furthermore, TCP variants are susceptible to TCP Window Size attacks, where attackers manipulate TCP window size values to degrade network performance, disrupt communication, or exhaust available resources [156], [157]. By sending TCP packets with artificially inflated or reduced window size values, attackers can force the target's TCP stack to allocate excessive memory or processing resources, leading to performance degradation or denial of service [158].…”

Section: Common Attacks In Tcpmentioning

confidence: 99%

A survey of transmission control protocol variants

Lydiah Moraa Machora

2024

World J. Adv. Res. Rev.

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TCP (Transmission Control Protocol), is a reliable connection oriented end-to-end protocol. It contains within itself, mechanisms for ensuring reliability by requiring the receiver to acknowledge the segments that it receives. The network is not perfect and a small percentage of packets are lost enroute, either due to network error or due to the fact that there is congestion in the network and the routers are dropping packets. TCP ensures reliability by starting a timer whenever it sends a segment. If it does not receive an acknowledgement from the receiver within the ‘time-out’ interval then it retransmits the segment. In this paper a review of various TCP is carried out. There are a number of TCP variants for application in the management of network efficiency in terms of network congestion and transmission efficiency. These variants include: - TCP Tahoe, TCP Reno, TCP New Reno, TCP Vegas, TCP SACK, TCP FACK, TCP Asym, TCP RBP, Full TCP and TCP CUBIC. Therefore, the main objective of this paper is to study the tcp types on the network performance variances. All have different features and advantages but with maximal throughput as main objective, which are termed as the clones of TCP, have been incorporated into TCP/IP protocol for handling congestion efficiently in different network scenarios.

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Section: Common Attacks In Tcpmentioning

confidence: 99%

A survey of transmission control protocol variants

Lydiah Moraa Machora

2024

World J. Adv. Res. Rev.

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“…In light of the aforementioned limitations, future research should aim at developing more robust machine learning algorithms capable of real-time learning. Such evolution would enhance the system's responsiveness to immediate network conditions, thus improving reliability [62].…”

Section: Future Directionsmentioning

confidence: 99%

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Beissenova,

Zhidebayeva,

Kopzhassarova

et al. 2024

IJACSA

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In this research paper, we delve into the innovative realm of optimizing load balancing in Data Center Networks (DCNs) by leveraging the capabilities of Software-Defined Networking (SDN) and machine learning algorithms. Traditional DCN architectures face significant challenges in handling unpredictable traffic patterns, leading to bottlenecks, network congestion, and suboptimal utilization of resources. Our study proposes a novel framework that integrates the flexibility and programmability of SDN with the predictive and analytical prowess of machine learning. We employed a multi-layered methodology, initially constructing a virtualized environment to simulate real-world DCN traffic scenarios, followed by the implementation of SDN controllers to instill adaptiveness and programmability. Subsequently, we integrated machine learning models, training them on a substantial dataset encompassing diverse traffic patterns and network conditions. The crux of our approach was the application of these trained models to anticipate network congestion and dynamically adjust traffic flows, ensuring efficient load distribution among servers. A comparative analysis was conducted against prevailing load balancing methods, revealing our model's superiority in terms of latency reduction, enhanced throughput, and improved resource allocation. Furthermore, our research illuminates the potential for machine learning's self-learning mechanism to foresee and adapt to future network states or exigencies, marking a significant advancement from reactive to proactive network management. This convergence of SDN and machine learning, as demonstrated, ushers in a new era of intelligent, scalable, and highly reliable DCNs, demanding further exploration and investment for future-ready data centers.

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“…In comparison to our paper, which focuses on defending against packet injection attacks using deep learning, Sudar et al [23] explore the vulnerability of SDN to different types of attacks. While our paper aims to develop novel solutions for defending against packet injection attacks, the work of Sudar et al demonstrates the need for investigating the resilience of SDN against security attacks and suggests the importance of hardening the technology to provide a secure network environment.…”

Section: Packet Injection Attacks On Sdnmentioning

confidence: 99%