CLASSIFICATION OF THE DDOS ATTACK OVER FLASH CROWD WITH DNN USING WORLD CUP 1998 AND CAIDA 2007 DATASETS

2021 ◽  
Vol 15 (3) ◽  
pp. 33
Author(s):  
CH. SEKHAR ◽  
K. VENKATA RAO ◽  
M.H.M KRISHNA PRASAD ◽  
◽  
◽  
...  
Keyword(s):  
World Cup ◽  
Ddos Attack ◽  
Flash Crowd

scholarly journals Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

2020 ◽  
Vol 14 (1) ◽  
pp. 17-34
Author(s):  
Maryam Ghanbari ◽  
Witold Kinsner
Keyword(s):  
Smart Grid ◽  
Input Data ◽  
Denial Of Service ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Infrastructure Services ◽  
Distinguishing Features

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy.

Distributed Denial of Service Attacks and Defense in Cloud Computing

2021 ◽  
pp. 522-540
Author(s):  
Gopal Singh Kushwah ◽  
Virender Ranga
Keyword(s):  
Cloud Computing ◽  
Denial Of Service ◽  
High Availability ◽  
Distributed Denial Of Service ◽  
Security Issue ◽  
Network Resources ◽  
Ddos Attack ◽  
On Demand ◽  
Learning Machine

Cloud computing has now become a part of many businesses. It provides on-demand resources to its users based on pay-as-you-use policy, across the globe. The high availability feature of this technology is affected by distributed denial of service (DDoS) attack, which is a major security issue. In this attack, cloud or network resources are exhausted, resulting in a denial of service for legitimate users. In this chapter, a classification of various types of DDoS attacks has been presented, and techniques for defending these attacks in cloud computing have been discussed. A discussion on challenges and open issues in this area is also given. Finally, a conceptual model based on extreme learning machine has been proposed to defend these attacks.

scholarly journals Detection of DDoS Attack in TCP protocol using Hybrid Machine Learning Techniques

2020 ◽  
pp. 253-258
Author(s):  
Vinod Desai ◽  
Aravind Pradhani ◽  
Sheetal Majukar
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Fast Detection ◽  
Ddos Attack ◽  
Tcp Protocol ◽  
Learning Techniques ◽  
Flash Crowd ◽  
Attack Scenario ◽  
Learning Technique ◽  
Network Status

Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself.

DDoS Attacks and Their Types

2016 ◽  
pp. 197-205 ◽  
Author(s):  
Dileep Kumar
Keyword(s):  
Large Scale ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Network Resource ◽  
Financial Trading ◽  
Ddos Attack ◽  
Site Access ◽  
Gain Access

Billions of people rely on internet to discover and share ideas with the world. However, the websites are vulnerable to deliver the attacks, preventing people to access them. The recent study of global surveys showed that DDoS Attacks evolved in strategy and tactics. A Distributed Denial of Service (DDoS) attack is a new emerging bigger threat that target organization's business critical services such as e-commerce transactions, financial trading, email or web site access. A DDoS attack is a large-scale, coordinated attack on the availability of services of a victim system or network resource, launched indirectly through many compromised computers on the Internet. To create attacks, attackers first discover vulnerable sites or hosts on the network. Then vulnerable hosts are exploited by attackers who use their vulnerability to gain access to these hosts. This chapter deals with the introduction, architecture and classification of DDoS Attacks.

scholarly journals Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

2022 ◽  
pp. 1078-1096
Author(s):  
Maryam Ghanbari ◽  
Witold Kinsner
Keyword(s):  
Smart Grid ◽  
Input Data ◽  
Denial Of Service ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Infrastructure Services ◽  
Distinguishing Features

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy.

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