A Survey of DDOS Attacks Using Machine Learning Techniques

Arshi, Maliheh Alsadat; Nasreen,; Madhavi, K. Reddy

doi:10.1051/e3sconf/202018401052

Cited by 25 publications

(15 citation statements)

References 7 publications

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“…Resilient backpropagation addresses this problem by calculating only the direction in which partial derivatives are used to change weights, resulting in less memory usage. [35].…”

Section: Resilient Backpropagation Training Algorithm Resultsmentioning

confidence: 99%

“…The direction of the gradient to the ministry must be determined in predefined intervals in the combined gradient algorithms. This strategy only shows the restart when the gradient output does not change minimally [35,36]. The recognition ( 19) is used to determine when the search direction must be reset to the sound level of the slope.…”

Section: Conjugate Gradient With Powell/beale Restart Training Algorithmmentioning

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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“…Resilient backpropagation addresses this problem by calculating only the direction in which partial derivatives are used to change weights, resulting in less memory usage. [35].…”

Section: Resilient Backpropagation Training Algorithm Resultsmentioning

confidence: 99%

Section: Conjugate Gradient With Powell/beale Restart Training Algorithmmentioning

confidence: 99%

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Qamar

2022

ijcsm

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“…Em (Arshi et al, 2020), os autores utilizaram técnicas de Aprendizado de Máquina para identificação de ataques DDoS. Os tipos de ataques empregados no estudo foram: UDP Flood, ICMP (PING) Flood, Smurf Attack e HTTP Flood, e os algoritmos utilizados para classificação foram o Naive Bayes, SVM, Árvores de Decisão (Decision Tree), Rede Neural e K-means.…”

Section: Trabalhos Relacionadosunclassified

Técnicas De Ensemble Learning Para Sistema De Detecção De Intrusão No Contexto Da Cibersegurança

2022

Proceedings of the Ibero American Conferences on Applied Computing 2022 and WWW/Internet 2022

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Recentemente, houve um interesse em melhorar os recursos disponíveis em técnicas de Intrusion Detection System (IDS). Neste sentido, diversos estudos relacionados à cibersegurança demonstram que as invasões de ambiente e o sequestro de informações, são cada vez mais recorrentes e complexas. A criticidade dos negócios envolvendo operações em ambiente utilizando recursos computacionais, não permitem a vulnerabilidade da informação. A cibersegurança tomou uma dimensão dentro do universo da tecnologia indispensável nas corporações, a prevenção de riscos de invasões ao ambiente é tratada diariamente por equipes de Segurança. Assim, o objetivo principal do estudo foi investigar a técnica de Ensemble Learning utilizando o método Stacking, apoiado pelos algoritmos Support Vector Machine (SVM) e k-Nearest Neighbour (kNN) visando uma otimização dos resultados para detecção de ataques DDoS. Para isso, utilizou-se o conceito de Intrusion Detection System com aplicação da ferramenta de Data Mining e Machine Learning Orange, para obter melhores resultados.

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“…The work presented in [11] surveys the recent developed DDoS detection approaches using the machine learning models. Authors of [12,13,14] proposed a DDoS detection system using Naïve Bayes model.…”

Section: Related Workmentioning

confidence: 99%

Detecting Distributed Denial of Service Attacks using Machine Learning Models

Alghoson¹,

Abbass²

2021

IJACSA

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The Software Defined Networking (SDN) is a vital technology which includes decoupling the control and data planes in the network. The advantages of the separation of the control and data planes including: a dynamic, manageable, flexible, and powerful platform. In addition, a centralized network platform offers situations that challenge security, for instance the Distributed Denial of Service (DDoS) attack on the centralized controller. DDoS attack is a well-known malicious attack attempts to disrupt the normal traffic of targeted server, network, or service, by overwhelming the target's infrastructure with a flood of Internet traffic. This paper involves investigating several machine learning models and employ them with the DDoS detection system. This paper investigates the issue of enhancing the DDoS attacks detection accuracy using a wellknown DDoS named as CICDDoS2019 dataset. In addition, the DDoS dataset has been preprocessed using two main approaches to obtain the most relevant features. Four different machine learning models have been selected to work with the DDoS dataset. According to the results obtained from real experiments, the Random Forest machine learning model offered the best detection accuracy with (99.9974%), with an enhancement over the recent developed DDoS detection systems. Keywords-Cybersecurity; distributed denial of service (DDoS); machine learning (ML); Canadian institute cybersecuritydistributed denial of service (CICDDoS2019) dataset

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A Survey of DDOS Attacks Using Machine Learning Techniques

Cited by 25 publications

References 7 publications

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Gradient Techniques To Predict Distributed Denial-Of-Service Attack

Técnicas De Ensemble Learning Para Sistema De Detecção De Intrusão No Contexto Da Cibersegurança

Detecting Distributed Denial of Service Attacks using Machine Learning Models

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