A hybrid intelligent HIDS model using two-layer genetic algorithm and neural network

Host Intrusion detection systems (HIDS) are increasingly emerging techniques for information security on host based applications. These systems should be designed to prevent unauthorized access of system resources and data. Many intelligent learning techniques are currently being applied to the large volumes of data for the construction of an efficient host intrusion detection system. This paper represents a hybrid approach for modeling HIDS combines anomaly, misuse detection, based on two-layer Genetic algorithm and neural network which uses simple data mining techniques to process the web application traffics. Two-layer Genetic algorithm and neural network are applied respectively as anomaly and misuse detection. Suspicious intrusions can be traced back to its original source. The proposed model is able to detect critical vulnerabilities based on Open Web Application Security Project (OWASP).