The low-rate denial of service attack based comparative study of active queue management scheme

Patel, Sanjeev; Sharma, Abhinav

doi:10.1109/ic3.2017.8284360

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Furthermore, different active queue management techniques such as random early detection, deficit round-robin, fair queuing, drop-tail, stochastic fair queue, and random exponential marking can be employed for packet flow control between various source nodes and destination nodes. This can be achieved through the management of the intermediate routers' buffers [28].…”

Section: Average Queue Occupancymentioning

confidence: 99%

Network-on-Chip Topologies: Potentials, Technical Challenges, Recent Advances and Research Direction

Alimi¹,

Patel²,

Aboderin³

et al. 2022

Network-on-Chip - Architecture, Optimization, and Design Explorations

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Integration technology advancement has impacted the System-on-Chip (SoC) in which heterogeneous cores are supported on a single chip. Based on the huge amount of supported heterogeneous cores, efficient communication between the associated processors has to be considered at all levels of the system design to ensure global interconnection. This can be achieved through a design-friendly, flexible, scalable, and high-performance interconnection architecture. It is noteworthy that the interconnections between multiple cores on a chip present a considerable influence on the performance and communication of the chip design regarding the throughput, end-to-end delay, and packets loss ratio. Although hierarchical architectures have addressed the majority of the associated challenges of the traditional interconnection techniques, the main limiting factor is scalability. Network-on-Chip (NoC) has been presented as a scalable and well-structured alternative solution that is capable of addressing communication issues in the on-chip systems. In this context, several NoC topologies have been presented to support various routing techniques and attend to different chip architectural requirements. This book chapter reviews some of the existing NoC topologies and their associated characteristics. Also, application mapping algorithms and some key challenges of NoC are considered.

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Section: Average Queue Occupancymentioning

confidence: 99%

Network-on-Chip Topologies: Potentials, Technical Challenges, Recent Advances and Research Direction

Alimi¹,

Patel²,

Aboderin³

et al. 2022

Network-on-Chip - Architecture, Optimization, and Design Explorations

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show abstract

“…As the most representative AQM algorithm, RED [11] has been extended by many researchers [15][16][17][18][19][20][21]. Because RED manages the average queue size in a router and controls the queuing delay, it is applicable to and efficiently at not only lockout, global synchronization, full queue, and deadlock but also denial-of-service attacks [22]. RED has a higher throughput fairness than Tail-Drop [23,24].…”

Section: Introductionmentioning

confidence: 99%

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

Kato

Asahara

et al. 2023

NOLTA

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This paper proposed a packet drop probability function with an adjustable nonlinearity parameter in random early detection (RED) for active queue management of a router to control network congestion. We investigated the effect of nonlinearity on the average queue size, average throughput, average retransmission rate, average round-trip time, and fairness index for two widely used loss-based congestion control algorithms: Reno and CUBIC. Simulations were performed with Tail-Drop and the original RED to clarify the effect of nonlinearity under different traffic loads. The results showed that the RED with a nonlinear function did not aggravate the network performance statistics because achieved high throughput while maintaining a low-queuing delay, such as the original RED with a linear function under the extremely heavy traffic condition. Under the light and the heavy traffic conditions, increasing the bending degree of the nonlinear function accomplished high throughput by preventing excessive discarding of packets.

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“…After that, many machine learning techniques are explored for their outcomes analysis [5][6][7][8][9]. A novel machine learning-based model for content rating has been built by the researchers of [11] whereas the Internet traffic is examined in [12]. Secondly, Payload-based techniques were developed, during which packages of the related networks are analysed and protocols are recognised based on the study.…”

Section: Introductionmentioning

confidence: 99%

Utilizing machine learning techniques to classify network traffic

Butool

Yazdani

Ahmedulla

et al. 2022

ijhs

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With in community of internet, it may be essential to recognize whatever programs are flowing via the networks in order to execute particular activities. Network traffic categorization is primarily used by Internet service providers (ISPs) to determine the qualities needed to construct a connection, which in turn influences the cable network current effectiveness. Stream, bandwidth, and machine-learning methods were all used to categorise internet protocol, and each has its own benefits and drawbacks. Because of its widespread use across disciplines as well as the increasing awareness between many investigators of its [5] methodology when especially in comparison to everyone else, the Machine Learning method is popular these times. Nave Bayes as well as K-nearest algorithm results are then compared in this research whenever applied to a networking given dataset taken through live stream feeds using Ethernet program. Python's sklearn module and the pandas and numpy arrays modules are utilised as assist modules to create a machine learning algorithm. Our findings show that K closest approach is more efficient than Nave Bayes, Decision Tree, and Support Vector Machine algorithms.

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The low-rate denial of service attack based comparative study of active queue management scheme

Cited by 7 publications

References 9 publications

Network-on-Chip Topologies: Potentials, Technical Challenges, Recent Advances and Research Direction

Network-on-Chip Topologies: Potentials, Technical Challenges, Recent Advances and Research Direction

Effects on random early detection of the packet drop probability function with an adjustable nonlinearity

Utilizing machine learning techniques to classify network traffic

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