Paper Title
Intrusion Detection Using Surrogate Model on Tag3p

Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms help to detect these attacks. In this paper, a rule evolution approach using Surrogate model on Tree Adjoining Grammar Guided Genetic Programming (SuTAG3P) for generating new rules. A testing dataset proposed by DARPA is used to evaluate these new rules. The proof of concept implementation shows that a rule generated by SuTAG3P has a low false positive rate (FPR), a low false negative rate and a high true positive rate of detecting attacks. Index Terms— attack detection; intrusion detection system; tag3p, genetic programming, surrogate model.