a new method for detection and prevention in computer networks based on ips and ids

Intrusion detection system is an important part in the organization s security structure. creating complete security in organizations computer systems requires ids and ips systems for inactive monitoring so that the system can detect intruders in the form of firewalls, antivirus and other security devices. this paper aimed to provide a new method for detection and prevention of ips and ids-based intrusion prevention. to solve this important challenge, we use powerful algorithms in the field of evolutionary computing such as genetic algorithm, colonial competition algorithm and particle swarm algorithm. in this paper, a backup vector machine has been used to classify existing data for intrusion detection systems in more than two classes. finally, a method was proposed that, in addition to classifying operations for data with more than two classes, it adjusts its parameters through meta-algorithms. the results of the simulation show that the detection accuracy rate in the proposed algorithm is 96%, which is better than other existing methods.