An Enhanced Associative Ant Colony Optimization Technique-based Intrusion Detection System

There are several intrusion detection models that are presented till now as per my study. There are several approaches including data mining, neural network, naive basis, etc., that are applied for finding the intrusions. But there is still a need for betterment in this direction. Our paper focuses on the limitation faced in the traditional approach. In this paper, we suggest a hybrid framework based on association rule mining (ARM) and ant colony optimization (ACO). Combining the properties of association and ant colony may provide better classification in comparison with the previous methodology. In our approach, we consider the dataset of NSL-KDD. It is a dataset that does not include redundant record, and test sets are reasonable which is mentioned in [1]. Then, we consider equal proportion of 10,000 dataset from the whole dataset. We first divide it into two parts based on normal establishment and termination. Then, we consider the normal dataset, and for finding the intrusions, we calculate the support value based on the matching factor. Then, we apply ACO technique to check the global optimum value. If the value crosses the limit value, then the node will be added into the final attack category. Finally, based on the attack category of denial of service (DoS), user to root (U2R), remote to user (R2L), and probing (Probe), we find the final classification. Our results support better classification in comparison with the previous techniques used in several research papers as per our study.