Research on Principle Techniques for Network Intrusion Detection based on Data Mining and Analysis Approach

The security of software applications, from web-based applications to mobile services, is always at risk because of the open society of internet. With the increase in the number of network throughput and security threats, intrusion detection system has attracted much attention in recent years. In this paper, we undertake the research on the principle techniques for network intrusion detection based on data mining and analysis approach. We adopt the prior knowledge on Bayesian network which is a directed acyclic graph, each node represents a random variable and an edge said direct probabilistic dependencies between two connected nodes. Then, we use the traditional risk assessment model to measure the possibility of being hearted. The numeric analysis and experimental illustration indicates the effectiveness of our method compared with other popular adopted state-of-the-art methodologies. In the future, we plan to introduce the graph and complex network theory into our prototype system to enhance the performance.