The Internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continually, developing flexible and adaptive security oriented approaches is a severe challenge. Intrusion detection is a significant focus of research in the security of computer systems and networks. The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools are currently developed. In this paper, we have focused on intrusion detection in computer networks by combination of fuzzy systems and Particle Swarm Optimization (PSO) algorithm. Fuzzy rules are desirable because of their interpretability by human experts. PSO algorithm is employed as meta-heuristic algorithm to optimize the obtained set of fuzzy rules. Results on intrusion detection dataset from KDD-Cup99 show that the proposed approach would be capable of classifying instances with high accuracy rate in addition to adequate interpretability of extracted rules.