Research And Mplementation Of Clustering Algorithm In Mobile Phone Virusintrusion Detection

With the development of ICT and the applications of user demand, mobile phones turns can from the traditional "da ge da" as only receive calls gradually to intelligent direction. Smart phone supports a separate operating system, where the system can install and use third-party software, make the phone a change t hat can only provide simple voice and text message service before, starting with integrated short-range wireless transmission, multimedia messaging, mobile Internet, mobile office, audio and video entertainment and simple image processing functions, to become a mobile PC. With the user enjoying a lot easier and fun of life bridged by Intelligent, the viruses and vandalism for smart phones is also increasing, which caused great negative impact to the user’s communication security and user experience. The anti-virus technology is lagging behind mobile phone in the virus updates, so we urgently need anti-virus computer experience and accumulation of anti-virus used in the field of mobile phones based on this. The research and implementation against intrusion detection technology of mobile phone viruses is put forward.This paper describes the characteristics and operation principle of mobile phone viruses in detail and analysis feasibility of the clustering algorithm in the application of intrusion detection. Then proposing incremental hierarchical clustering algorithm based on Study of abnormal incremental data the characteristics of the virus, which is a cohesive hierarchical clustering algorithm, using the designated representative point of each cluster to represent the actual data. It uses a shrinkage factor to control the data distribution of representative points at the same time. This method is effective to represent irregular data distributions, and have a good adaptability to anomaly point data at the same time. The innovation of this algorithm is that it is able to use incremental abnormal data to correction modeling data on the pre-cluster, So that the algorithm has the ability of self-learning. It will add the latest virus signature data to the virus signature database, based on the effective using of pre-modeling results. This approach is also effective to solve the shortcoming of that once all step has been done, he data in the clustering of clusters can’t be changed.This article designed and developed intrusion detection system against mobile phone viruses according to "based on abnormal data studying Incremental hierarchical clustering algorithm" and the characteristics of mobile phone virus. System models vast amounts of data obtained through the network, get the normal virus signature data, and then detect the network data using these signatures. System test results show that:the system can effectively derive rules from classless data, achieve better detection purposes by testing using the rules.