This paper presents a novel energy aware centralized dynamic clustering routing framework for large-scale Wireless Sensor Network (WSN). The main advantage of the proposed method is pluggability of clustering algorithms in the framework. It uses some clustering algorithms that some of their usages are new in this field. The clustering algorithms are K-means, FCM, UPC, GA, IGA and FGKA that run at base station used to identify cluster of sensors. Six clustering algorithms are evaluated in the framework and results of them are compared in three models named unicast, multicast and broadcast.

By ever increase in using computer network and internet, using Intrusion Detection Systems (IDS) has been more important. Main problems of IDS are the number of generated alerts, alert failure as well as identifying the attack type of alerts. In this paper a system is proposed that uses Adaptive Neuro-Fuzzy Inference System to classify IDS alerts reducing false positive alerts and also identifying attack types of true positive ones. By the experimental results on DARPA KDD cup 98, the system can classify alerts, leading a reduction of false positive alerts considerably and identifying attack types of alerts in low slice of time.