Anomaly Detection Using Hybrid Neuro Genetic Model

Abstract

With a steady increase in the population of Internet users, a plethora of network services have emerged on the global level. As an offshoot of this phenomenal rise in network services and their capabilities riding on the wave of internet, we are witnessing a massive risk of attacks on network security. Many security vulnerabilities are exposed and exploited by attackers, endangering the safety of massive amounts of data. To improve a network’s effectiveness, it’s critical to detect network traffic anomalies accurately and quickly. A new hybrid model that effectively detects anomalies in network services is proposed in this work. The genetic phase and NN phase represent the 2-phased approach making each one dependent on the other for weight assignment and prediction. The genetic phase generates optimal weights for classification of normal and anomaly patterns. The NN phase learns the input output relationship of network patterns employing GA in the training phase. Detection is accomplished using trained NN and it utilizes pre-processed KDD dataset containing normal and abnormal samples for training. The outcomes demonstrated that the suggested approach outperforms all other algorithms.