Network security situation abnormal detection method based on hypothesis test

2016 ◽  
Vol 16 (3) ◽  
pp. 505-518 ◽  
Author(s):  
Zhicheng Wen ◽  
Pinjie He
2014 ◽  
Vol 530-531 ◽  
pp. 646-649
Author(s):  
Ling Qiu ◽  
Cai Ming Liu

To dynamically discover network attacks hidden in network data, an intelligent detection method for network security is proposed. Biological immune principles and mechanisms are adopted to judge whether network data contain illegal network packets. Signature library of network attacks and section library of attack signatures are constructed. They store attack signatures and signature sections, respectively. They are used to make the initial detection ability of proposed method. Detectors are defined to simulate immune cells. They evolve dynamically to adapt the network security. Signatures of network data are extracted from IP packets. Detectors match network data's signatures which mean some attacks. Warning information is formed and sent to network administrators according to recognized attacks.


2013 ◽  
Vol 6 (2) ◽  
pp. 329-335
Author(s):  
Rachna Kulhare ◽  
Dr. Divakar Singh

Network security has been one of the most important problems in Computer Network Management and Intrusion is the most publicized threats to security. In recent years, intrusion detection has emerged as an important field for network security. IDSs obtain better results when each class ofattacks is treated as a separate problem and handled by specialized algorithms. Now in days various model and method are available for intrusion detection. In this paper, we present a study of intrusion detection. Detection method to improve the detection rate & helping the users to develop secure information systems.


2013 ◽  
Vol 860-863 ◽  
pp. 2758-2761
Author(s):  
Yue Li ◽  
Ya Qin Fan ◽  
Duo Yang ◽  
Kai Yuan Zheng

Because the model of Botnet posed a threat to the network security, so this paper studies the semi distributed P2P network. Base on this, we simulate the propagation model of semi distributed P2P network and obtained a more conform to the actual status's new communication model. Through the analysis of the result, we prove the effectiveness of the honeypot detection method and flow detection method. Pseudo honeypot" detection technique model is based on the first two detection, we also simulate it and get a desired result. The conclusion has important significance for the study of network security.


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