Simulation of Denial of Service (DoS) Attack using Matlab and Xilinx

Mukhopadhayay, I; Polle, S; Naskar, Prabir Kumar

doi:10.9790/0661-1634119125

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…The better training algorithm was determined, enabling a DDoS attack to be identified by using the KDD dataset [29].…”

Section: Final Resultsmentioning

confidence: 99%

“…A wide range of recognized patterns and ML algorithms were used to develop the KDD1999 dataset, which was launched for the KDD Cup competition in 1999. The standard KDD1999 database, which contains connecting records of attacks and interference in a network, was used for testing and training [29].…”

Section: Collecting the Informationmentioning

confidence: 99%

“…Before the training and pre-processing, the KDD dataset was cleaned, and protocol, attack, and flag values were assigned [29].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Neural network training is a significant part of DDoS attack simulation. In this phase, an RNN was trained to analyze the predefined behavior of proposed algorithms and compare the results with better accuracy and shorter timing [29].…”

Section: Recursive Neural Networkmentioning

confidence: 99%

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Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Qamar

2022

ijcsm

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A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. A website may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannot handle real ones. A DDoS attack affects a large number of computers. Attackers employ a zombie network, which is a collection of infected machines on which the attacker has hidden the denial-of-service attacking application to carry out a DDoS attack. The MATLAB 2018a simulator was used in this study for training. Additionally, during design, the knowledge discovery dataset (KDD) was cleaned and the values of attacks were incorporated. A neural network model was subsequently developed, and the KDD was trained using a recursive artificial neural network. This network was developed using five distinct training algorithms: 1) Fletcher–Powell conjugate gradient, 2) Polak–Ribiére conjugate gradient of, 3) resilient backpropagation, 4) gradient conjugation with Powell/Beale restarts, and 5) gradient descent algorithm with variable learning rate. The artificial neural network toolset in MATLAB was used to investigate the detection of DDoS attacks. The conjugate gradient with Powell/Beale restart algorithm had a success rate of 99.9% and a training time of 00:53. This inquiry uses the KDD-CUP99 dataset. Has a better level of accuracy, according to the results

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“…The better training algorithm was determined, enabling a DDoS attack to be identified by using the KDD dataset [29].…”

Section: Final Resultsmentioning

confidence: 99%

Section: Collecting the Informationmentioning

confidence: 99%

“…Before the training and pre-processing, the KDD dataset was cleaned, and protocol, attack, and flag values were assigned [29].…”

Section: Data Pre-processingmentioning

confidence: 99%

Section: Recursive Neural Networkmentioning

confidence: 99%

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Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Qamar

2022

ijcsm

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“…Implementation of the confusion matrix that measures detection performance is the best option as this matrix has a wide range of measurement of detection. e matrix has also been utilised by many researchers [38][39][40][41][42][43][44][45].…”

Section: Critical Analysismentioning

confidence: 99%

Review of Recent Detection Methods for HTTP DDoS Attack

Jaafar

Abdullah

Ismail

2019

Journal of Computer Networks and Communications

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With increment in dependency on web technology, a commensurate increase has been noted in destructive attempts to disrupt the essential web technologies, hence leading to service failures. Web servers that run on Hypertext Transfer Protocol (HTTP) are exposed to denial-of-service (DoS) attacks. A sophisticated version of this attack known as distributed denial of service (DDOS) is among the most dangerous Internet attacks, with the ability to overwhelm a web server, thereby slowing it down and potentially taking it down completely. This paper reviewed 12 recent detection of DDoS attack at the application layer published between January 2014 and December 2018. A summary of each detection method is summarised in table view, along with in-depth critical analysis, for future studies to conduct research pertaining to detection of HTTP DDoS attack.

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Energy Efficient Intrusion Detection Scheme Based on Bayesian Energy Prediction in WSN

Shivaji¹,

Patil

2015

2015 Fifth International Conference on Advances in Computing and Communications (ICACC)

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Simulation of Denial of Service (DoS) Attack using Matlab and Xilinx

Cited by 5 publications

References 10 publications

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Review of Recent Detection Methods for HTTP DDoS Attack

Energy Efficient Intrusion Detection Scheme Based on Bayesian Energy Prediction in WSN

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