Intelligent Intrusion Detection System Based On Advanced Computing Research

The development of computer technology has changed human life, but the risk of viruses and the chance of a sharp increase. Design of security measures to guard against unauthorized access to earthquake information system resources and data, is the current host or seismic information network security field seismic system is a very important and urgent issue. The issue of network security is to carry out seismic studies to be solved an important issue. Intrusion detection technology is nearly20years, a pro-active network security technology to protect themselves from attack, it does not affect network performance, network detection, thus providing the attacks on the internal and external attacks and misuse of the operation of real-time protection.Some of the basic theory of intrusion detection system, the authors noted that the introduction of advanced machine learning and evolutionary computation method to realize the need for intrusion detection systems. Proposed intrusion detection method based on support vector machines non-equilibrium data, the intrusion detection method based on artificial immune danger theory as well as intrusion detection method based on immune dangerous cloning planning, done by a specific innovation as follows:(1) The proposed intrusion detection method based on support vector machines and unbalanced data. First introduced the problem of intrusion detection in non-equilibrium data, the non-equilibrium data for fast support vector machine classifier, and use it to achieve a new type of intrusion detection systems. The algorithm has the following advantages:(a) consider the impact of non-equilibrium data for the performance of the learning machine, non-equilibrium LSSVM with strong generalization ability of intrusion detection systems;(b) due LSSVM will learn in the process of inequality constraints into equality constraints and greatly reduces the complexity of the training process. Finally, the connection on the KDD Cup1999data set characteristics field classification, analysis and compare the rate of correct test results and to assess the detection efficiency. Results demonstrate its effectiveness.(2) The proposed intrusion detection method based on clustering algorithms and dangerous theory. Difficult to accurately distinguish between the problem of intrusion detection system of the traditional artificial immune mechanisms and non-self, the introduction of dangerous theory to achieve a more efficient intrusion detection. The algorithm has the following advantages:(a) the use of fuzzy C-means clustering algorithm for preprocessing to find the approximation of the data center location, the use of dangerous theory to find out the most appropriate number of clusters and good cluster centers, and significant savings in the intrusion detection system processing time,(b) avoid the traditional immune IDS system of self/not my set too large, the immune response to danger signals. According to the size of the judgment of the danger signal concentration is a intrusion. KDDCup1999data sets to verify its performance. Results demonstrate its effectiveness.(3) The proposed intrusion detection method based on immune dangerous cloning planning. With the growth of time, the immune dangerous intrusion detection algorithm autologous library will become very large, autologous tolerance time will increase exponentially. To further reduce the time complexity of immune danger of intrusion detection methods, raised the risk of an immune clone planning intrusion detection algorithm to speed up the convergence rate of Immune Algorithm. The algorithm has the following advantages:(a) the use of cloning operation instead of the traditional evolutionary operations of crossover, mutation and selection operations, the faster the speed of solving large-scale optimization problem solving,(b) be able to overcome the immune algorithm is easy to converge to a local minimum of defects. KDDCup1999data sets to verify its performance. Results demonstrate its effectiveness.