Intrusion Detection System Based on Combination of Optimized Genetic and Firefly Algorithms in Cloud Computing Structure

摘要

Attackers or hackers are always looking to attack networks. Optimizing and securing system settings prevents hackers from accessing networks to a great extent. Intrusion Detection Systems (IDS), firewalls, and Honey Pot (Honey Pot) are technologies that can prevent hacking attacks on the networks. IDS or Intrusion Detection System analyzes all activities on the network and uses the information available on its database in order to determine if the activity is allowed or considered unauthorized. It also determines whether this activity can harm your network or not and eventually notify such activities by sending alarms or alerts to the system administrator. The main purpose of intrusion detection system is to classify data and network traffic. Thus, the detection of penetration in these systems is essentially a classification operation, so if the classification operation can be improved, the performance of intrusion detection system could get increased. For this reason, we have used the ECOC algorithm to improve classification performance by categorizing general problem into trivial classes. Improvement means that by breaking down the problem into smaller classes and assigning a separate classifier to each class, the power and accuracy of the classification operation increases, thereby overall system performance would improve. Other important factor which enhance diagnostic performance is the use of appropriate features in training and testing classifications. For this reason, we used firefly and genetic algorithms to select the proper features of each classification in each level. The main goal of this research is to provide an intrusion detection system with better penetration detection and performance. Based on the results obtained from the system diagnosis, our proposed system has been able to increase the detection rate up to 5% in comparison with other intrusion detection systems.