Design of Intrusion Detection Model Based on FP-Growth and Dynamic Rule Generation with Clustering

Intrusion Detection is the process used to identify intrusions. If we think of the current scenario then several new intrusion that cannot be prevented by the previous algorithm, IDS is introduced to detect possible violations of a security policy by monitoring system activities and response in all times for betterment. If we detect the attack type in a particular communication environment, a response can be initiated to prevent or minimize the damage to the system. So it is a crucial concern. In our framework we present an efficient framework for intrusion detection which is based on Association Rule Mining (ARM) and K-Means Clustering. K- Means clustering is use for separation of similar elements and after that association rule mining is used for better detection. Detection Rate (DR), False Positive Rate (FPR) and False Negative Rate (FNR) are used to measure performance and analysis experimental results.