Detection of DDOS Attack using Deep Learning Model in Cloud Storage Application

Agrawal, Ankit; Khari, Manju; Singh, Rajiv

doi:10.1007/s11277-021-08271-z

Cited by 42 publications

(30 citation statements)

References 40 publications

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“…In addition, they plan to use another video steganography technology based on the payload method (DECM: Frequency Division Embedded Component Method), which can use two open devices VirtualDub and Stegano to implant significantly more privileges than existing tools information. They show that proposed model can be performed in the Telegram SNS courier, and compared proposed model and DECM with the current image steganography-based botnets and methods in terms of the effectiveness and imperceptibility [17]. Zahid Akhtar et al [18] proposed a concise overview of malware, followed by a summary of different inspection challenges.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Ismail

Mohmand

Hussain³

et al. 2022

IEEE Access

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Distributed network attacks are often referred to as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization's site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, THE UNWS-np-15 dataset from GitHub and python used as a simulator. After applying the machine learning models generated a confusion matrix for model performance identification. In the first classification, the results showed that both Precision (PR) and Recall (RE) are 89% for Random Forest Algorithm. The average Accuracy (AC) of model is 89% which is extremely good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are 90% for XGBoost. The average Accuracy (AC) of model is 90%. By comparing work to existing research work, the accuracy of defect determination improved as compare to existing research work which is 85% and 79%.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Ismail

Mohmand

Hussain³

et al. 2022

IEEE Access

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“…In this paper, the authors tried to implement an IDS-based security mechanism to automatically notify the administrator about any harmful activity [26]. For that purpose, a lightweight attack recognition system with a deep learning method is suggest to arrange simple and attack details.…”

Section: Ddos Detection By Deep Learningmentioning

confidence: 99%

Mitigating DDoS Attacks Using a Resource Sharing Network

Khan

Hossain

Shanto

et al. 2022

Proceedings of the 9th International Conference on Networking, Systems and Security

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Cloud computing is crucial to the internet just as air is crucial to breathing. Most users do not even come to think how the cloud architecture is one of the giant pillars on which the entire internet and all its related services depend. In recent times, cloud computing has gained noticeable popularity due to its ability to radically improve computing power through the application of virtual machines. In this era of the internet, however, security threats are increasing and it costs many businesses. The seemingly legitimate traffic of these application-level attacks renders the previous detection and mitigation methods ineffective. These cyber-attacks have grown ever so sophisticated and the detection and mitigation of these attacks have become one of the major concerns of security researchers and cloud service providers all around the globe. In our paper, we are trying to analyze existing research and implementation of the cloud architecture. This analysis will give us insight into the core mechanisms of the cloud architecture and DDoS attacks. Furthermore, we will apply this understanding of ours to design and build an universal mitigation technique for DDoS attacks. An universal solution that works for all scenarios, may take at least a few dozen developers working years on end to develop. Therefore, the goal of this paper will be to lay the foundation on which one day the universal solution may be created.

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“…DDoS saldırısı ve normal trafik esnasındaki entropi değişimine dayanan düşük ek hesaplama yüküne sahip bir savunma mekanizması sunulan çalışmada [26], yüksek doğrulukla tespit yapıldığı ve saldırı etkilerinin hafifletildiği ortaya konmuştur. DDoS saldırılarına maruz kalmış veri ve sağlıklı verinin ayırt edilmesi için derin öğrenme teknikleri kullanılan çalışmada [27], sadece saldırıya maruz kalmış verilerin ayıklanmasıyla sağlıklı verilerin bulut depolama alanlarında saklanması amaçlanmıştır. Bulut ortamında sistem yüklerinin, farklı yük dengeleme yaklaşımlarıy la yönetiminin incelendiği çalışmada [28], yoğun yük altında dinamik yük dengelemenin sisteme gelen isteklerin engellenmesi kapsamında daha iyi performans gösterdiği ortaya konmuştur.…”

Section: Dağıtık Hizmet Dışı Bırakma (Distributed Denial Of Service)unclassified

A Review on Challenges in Cloud Computing Security and Recent Researchs

GÜLBURUN

Dener

2022

Bilişim Teknolojileri Dergisi

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Bulut bilişim sistemleri, kişi ve organizasyonlar tarafından ihtiyaç duyulan farklı seviye ve nitelikteki bilg i sistemleri kaynaklarının, talebe bağlı olarak istenilen zaman ve miktarda ihtiyaç sahibine sunulabildiği, çoğunlukla internet üzerinden erişilebilir kılınan, özellikle değişken iş yüklerine sahip organizasyonların ihtiyaçlarına en iyi şekilde cevap verebilen sistemlerdir. Bulut bilişim sistemlerinin kullanımının artmasına paralel olarak hem bulut biliş im platformlarına hem de bu platformlardan servis alan organizasyonlara yönelik saldırılar artış göstermiştir. Çalışmad a, öncelikle, farklı kuruluşlar tarafından hazırlanan bulut bilişim güvenlik raporları incelenerek, bulut bilişim kapsamında karşılaşılan temel tehditler ortaya konmuştur. Daha sonra Web of Science veri tabanı temel alınarak bulut biliş im güvenliği kapsamında yapılan güncel çalışmalar incelenmiştir.

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Detection of DDOS Attack using Deep Learning Model in Cloud Storage Application

Cited by 42 publications

References 40 publications

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Mitigating DDoS Attacks Using a Resource Sharing Network

A Review on Challenges in Cloud Computing Security and Recent Researchs

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