Abstract: The security of networked computers plays a strategic role in modern computer systems. The most important reason is the difficulties in obtaining adequate attack data for the supervised classifiers to model the attack patterns and the data acquisition task is always time-consuming and greatly relies on the domain experts. The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality and integrity of critical information for both individuals and enterprises. This task is so complicated because the determination of normal and abnormal behaviors in computer networks is hard as the boundaries cannot be well defined. One of the difficulties in such a prediction process is the generation of false alarms in many anomaly based intrusion detection systems. This study proposes a Local Search guided Differential Evolution (LSDE) search algorithm to generate fuzzy rules capable of detecting intrusive behaviors. In the presented algorithm the global population is divided into subpopulations, each assigned to a distinct processor. Each subpopulation consists of the same class fuzzy rules. These rules evolve independently in the proposed parallel manner. A series of experimental results on the well-known KDD Cup 1999 data set demonstrate that the proposed method is more robust and effective than the state-of-the-art previous intrusion detection methods as well as can be further optimized as discussed in this study for real applications of intrusion detection system.