An Entropy-SVM Based Interest Flooding Attack Detection Method in ICN

2018 ◽  
Author(s):  
Ting Zhi ◽  
Ying Liu ◽  
Zhiwei Yan
Keyword(s):  
Detection Method ◽  
Attack Detection ◽  
Flooding Attack

Interest Flooding Attack Detection Method in NDN Networks

2021 ◽  
Author(s):  
V. Mounika ◽  
N. Raghavendra Sai ◽  
Vasantha Bhavani ◽  
P S V S Sridhar
Keyword(s):  
Detection Method ◽  
Attack Detection ◽  
Flooding Attack

A Method Based on BP Neural Network to Detect SIP DOS Flooding Attack

2012 ◽  
Vol 263-266 ◽  
pp. 2773-2778
Author(s):  
Hai Xiu Cheng ◽  
Bi Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Detection Method ◽  
Attack Detection ◽  
Ip Multimedia Subsystem ◽  
Network Characteristics ◽  
Flooding Attack ◽  
Flooding Attacks ◽  
The Neural Network ◽  
Simulation Results

In order to detect the SIP flooding attacks in IMS (IP Multimedia Subsystem) network, the paper proposes a flooding attack detection method based on BP neural network, and analyses causes of SIP flooding attack, and thus determine the BP the input of the neural network characteristics. The simulation results show that this method has better detection performance.

scholarly journals A Novel Shilling Attack Detection Method

2014 ◽  
Vol 31 ◽  
pp. 165-174 ◽  
Author(s):  
Alper Bilge ◽  
Zeynep Ozdemir ◽  
Huseyin Polat
Keyword(s):  
Detection Method ◽  
Attack Detection ◽  
Shilling Attack ◽  
Shilling Attack Detection

scholarly journals Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jieren Cheng ◽  
Chen Zhang ◽  
Xiangyan Tang ◽  
Victor S. Sheng ◽  
Zhe Dong ◽  
...  
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Multiple Kernel Learning ◽  
Attack Detection ◽  
Kernel Learning ◽  
Economic Losses ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Multiple Kernel ◽  
Ddos Attack Detection

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

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