UDP Flooding Attack Detection Using Information Metric Measure

Boro, Debojit; Basumatary, Himant; Goswami, Tribeni; Bhattacharyya, Dhruba K.

doi:10.1007/978-981-10-0129-1_16

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Boro et al [47] considered two detection features for UDP flooding attacks targeted to a particular destination: (i) the count of total destination port changes for non-spoofed source addresses with random destination ports, and (ii) the count of source addresses changes for randomly spoofed source addresses. A KD-Tree that each node represents a unique source address is used to record information of incoming traffic.…”

Section: A Detection Methods Against Network/transport Layer Ddos Flmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

100

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Section: A Detection Methods Against Network/transport Layer Ddos Flmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

100

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“…UDP does not require the source and destination to build the three-way handshaking like TCP. Moreover, it is not crucial for an end-to-end connection [26]. The minor of the authentication mechanism and end-to-end connections make UDP vulnerable to attacks.…”

Section: Udp Flooding Attackmentioning

confidence: 99%

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

Park

2021

Electronics

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Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.

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“…Nam et al [27] combined KNN with SOM to deal with DDoS flooding. Because Linear KNN has high time consuming [28], K-Dimensional tree (KD tree) stores training points in a tree structure for fast query of KNN [29], [30]. But, KD tree has to build an index chain for all training entities [31], if the training entity changes, it will affect detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

Efficient DDoS Detection Based on K-FKNN in Software Defined Networks

Sun

Feng³

et al. 2019

IEEE Access

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Software Defined Networking (SDN) centrally manages the network data layer to improve the programmability and flexibility of networks by the controller. Because of centralized control, SDN is vulnerable to Distributed Denial of Service (DDoS) attacks. In order to protect the security of SDN, a method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed for DDoS detection in SDN, and the modular detection system is presented in the controller. The detailed experiments are conducted to evaluate the system performance. The results of the experiments show that K-FKNN improves the detection accuracy and efficiency of K-Nearest Neighbors (KNN), and has high precision and stability of DDoS detection in SDN. INDEX TERMS Software defined networks, network security, DDoS detection, K-nearest neighbors.

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UDP Flooding Attack Detection Using Information Metric Measure

Cited by 7 publications

References 5 publications

Security Data Collection and Data Analytics in the Internet: A Survey

Security Data Collection and Data Analytics in the Internet: A Survey

MAN-EDoS: A Multihead Attention Network for the Detection of Economic Denial of Sustainability Attacks

Efficient DDoS Detection Based on K-FKNN in Software Defined Networks

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