Detecting Incoming and Outgoing DDoS Attacks at the Edge Using a Single Set of Network Characteristics

Siaterlis, Christos; Maglaris, Vasilis

doi:10.1109/iscc.2005.50

Cited by 27 publications

(15 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By designing an intelligent user control system and train RBF neural network to detect attack. Siaterlis and Maglaris [2] give up the way of finding a single detection metric to reliably detect DDoS attacks. On the contrary they have attempted to make use of multiple metrics and they use Multi-Layer Perception (MLP) as a data classifier.…”

Section: Related Workmentioning

confidence: 99%

DDoS Attack Detection Using Sliding Window Method

Zhang¹,

Qian²,

Chi³

2018

dtcse

View full text Add to dashboard Cite

Abstract. DDoS (Distributed Denial of Service) attack, with its simple, difficult to detect, destructive features, causes a huge threat to the network security. In this paper, we construct a DDoS attack detection system and propose a DDoS detection algorithm based on sliding window. The effect of sliding window with different sizes on the accuracy of results is discussed and an optimal value is chosen as model parameter. Besides, we extract the characteristics of traffic and experiments show these features are meaningful to detect whether DDoS attack happens. Finally we choose some of the features and get a uniform standard of judgment by preprocessing, transforming these features and setting rules. The experimental result shows that the proposed algorithm can effectively detect DDoS attacks in real time, and the accuracy rate is 93%.

show abstract

Section: Related Workmentioning

confidence: 99%

DDoS Attack Detection Using Sliding Window Method

Zhang¹,

Qian²,

Chi³

2018

dtcse

View full text Add to dashboard Cite

show abstract

“…Siaterlis C. et al [14] have proposed a DoS detection method based on Multi-Layer Perceptron (MLP). The authors use multiple metrics to successfully detect flooding attacks and classify them as incoming or outgoing attacks.…”

Section: Related Workmentioning

confidence: 99%

DoS Detection Method based on Artificial Neural Networks

Idhammad¹,

Afdel²,

Belouch³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-DoS attack tools have become increasingly sophisticated challenging the existing detection systems to continually improve their performances. In this paper we present a victimend DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method consists of the following three major steps: (1) Collection of the incoming network traffic, (2) selection of relevant features for DoS detection using an unsupervised Correlation-based Feature Selection (CFS) method, (3) classification of the incoming network traffic into DoS traffic or normal traffic. Various experiments were conducted to evaluate the performance of the proposed method using two public datasets namely UNSW-NB15 and NSL-KDD. The obtained results are satisfactory when compared to the state-of-the-art DoS detection methods.

show abstract

“…A group of author [32]- [34] proposed a system of packetmarking and entropy in which each packet is marked on every router involved in communication in order to track the source of the packet. However, a number of techniques proposed by some authors used ANN or infrastructure to defend against DDOS attacks, where as a couple of them identified the source of the attack.…”

Section: Consequences Of Ddosmentioning

confidence: 99%

Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks

Aljumah¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Distributed Denial of Services (DDoS) is a ruthless attack that targets a node or a medium with its false packets to decline the network performance and its resources. Neural networks is a powerful tool to defend a network from this attack as in our proposed solution a mitigation process is invoked when an attack is detected by the detection system using the known patters which separate the legitimate traffic from malicious traffic that were given to artificial neural networks during its training process. In this research article, we have proposed a DDoS detection system using artificial neural networks that will flag (mark) malicious and genuine data traffic and will save network from losing performance. We have compared and evaluated our proposed system on the basis of precision, sensitivity and accuracy with the existing models of the related work.

show abstract

Detecting Incoming and Outgoing DDoS Attacks at the Edge Using a Single Set of Network Characteristics

Cited by 27 publications

References 9 publications

DDoS Attack Detection Using Sliding Window Method

DDoS Attack Detection Using Sliding Window Method

DoS Detection Method based on Artificial Neural Networks

Detection of Distributed Denial of Service Attacks Using Artificial Neural Networks

Contact Info

Product

Resources

About