scholarly journals Bayes-based ARP attack detection algorithm for cloud centers

2016 ◽  
Vol 21 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Huan Ma ◽  
Hao Ding ◽  
Yang Yang ◽  
Zhenqiang Mi ◽  
James Yifei Yang ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Attack Detection

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

scholarly journals Event-triggered attack detection and identification in power-electronics-based DC microgrid

2021 ◽  
Author(s):  
Sicheng Gong
Keyword(s):  
Power Flow ◽  
Detection Algorithm ◽  
Power Line ◽  
Current Data ◽  
Attack Detection ◽  
Dc Microgrid ◽  
Detection Mechanism ◽  
Detection And Identification ◽  
Event Triggered ◽  
Identification Strategy

This paper proposes a novel event-triggered attack detection mechanism for converter-based DC microgrid system. Under a distributive network framework, each node collects its neighbours' relative data to regulate its own output for local stabilization. Without power line current data, hardly can an agent directly identify the source of unexpected power flow, especially under an organized attack composed of voltage variations and corresponding deceptive messages. In order to recognize traitors who broadcast wrong data, target at system distortion and even splitting, an efficient attack detection and identification strategy is mandatory. After the attack detector is triggered, each relative agent refuses to trust any received data directly before authentication. Through proposed two-step verification by comparing theoretical estimated signals with received ones, both self sensors and neighbour nodes would be inspected, and the attacker was difficult to hide himself. Through simulation on SIMULINK/PLECS and hardware experiments on dSpace Platform, the effectiveness of proposed detection algorithm has been proved.

scholarly journals Research on Pseudo-Node Detection Algorithm in Wireless Sensor Networks

2017 ◽  
Vol 13 (03) ◽  
pp. 113
Author(s):  
Wenjin Yu ◽  
Yong Li ◽  
Yuangeng Xu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Detection Algorithm ◽  
Comprehensive Analysis ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Font Size ◽  
Simulation Results ◽  
Sensor Security

<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">With the wide application of the wireless sensor network, the security of the sensor network is becoming increasingly important. In this paper, based on node ranging, a new intrusion node detection algorithm has been proposed for external pseudo-node detection in wireless sensor networks. The presence of the nodes under copying-attack and the pseudo-nodes in the network can be detected through inter-node ranging with appropriate use of various sensors of nodes themselves and comprehensive analysis of ranging results. Operating in a stand-alone or embedded manner, this method has remedied the defects in the traditional principle of attack detection. The simulation results show that the proposed method has excellent applicability in wireless sensor security detection.</span>

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

scholarly journals A Classification Detection Algorithm Based on Joint Entropy Vector against Application-Layer DDoS Attack

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yuntao Zhao ◽  
Wenbo Zhang ◽  
Yongxin Feng ◽  
Bo Yu
Keyword(s):  
Denial Of Service ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Training Data ◽  
Joint Entropy ◽  
Application Layer ◽  
Accurate Identification ◽  
Data Set ◽  
Ddos Attack ◽  
The Web

The application-layer distributed denial of service (AL-DDoS) attack makes a great threat against cyberspace security. The attack detection is an important part of the security protection, which provides effective support for defense system through the rapid and accurate identification of attacks. According to the attacker’s different URL of the Web service, the AL-DDoS attack is divided into three categories, including a random URL attack and a fixed and a traverse one. In order to realize identification of attacks, a mapping matrix of the joint entropy vector is constructed. By defining and computing the value of EUPI and jEIPU, a visual coordinate discrimination diagram of entropy vector is proposed, which also realizes data dimension reduction from N to two. In terms of boundary discrimination and the region where the entropy vectors fall in, the class of AL-DDoS attack can be distinguished. Through the study of training data set and classification, the results show that the novel algorithm can effectively distinguish the web server DDoS attack from normal burst traffic.

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