Biblio
With the development of computer technology and the popularization of network, network brings great convenience to colleagues and risks to people from all walks of life all over the world. The data in the network world is growing explosively. Various kinds of intrusions are emerging in an endless stream. The means of network intrusion are becoming more and more complex. The intrusions occur at any time and the security threats become more and more serious. Defense alone cannot meet the needs of system security. It is also necessary to monitor the behavior of users in the network at any time and detect new intrusions that may occur at any time. This will not only make people's normal network needs cannot be guaranteed, but also face great network risks. So that people not only rely on defensive means to protect network security, this paper explores block chain network intrusion detection system. Firstly, the characteristics of block chain are briefly introduced, and the challenges of block chain network intrusion security and privacy are proposed. Secondly, the intrusion detection system of WLAN is designed experimentally. Finally, the conclusion analysis of block chain network intrusion detection system is discussed.
In this paper we propose a new algorithm to detect Advanced Persistent Threats (APT's) that relies on a graph model of HTTP traffic. We also implement a complete detection system with a web interface that allows to interactively analyze the data. We perform a complete parameter study and experimental evaluation using data collected on a real network. The results show that the performance of our system is comparable to currently available antiviruses, although antiviruses use signatures to detect known malwares while our algorithm solely uses behavior analysis to detect new undocumented attacks.