An intrusion detection method to detect denial of service attacks using error-correcting output codes and adaptive neuro-fuzzy inference

Abstract

ContextA wide range of network technologies and equipment used in network infrastructure are vulnerable to Denial of Service (DoS) attacks. Therefore, the identification of these attacks is of particular importance in security systems. ProblemMost of the previously presented solutions use a single machine learning model to detect DoS attacks; but it seems that improving the detection accuracy and reliability in the intrusion detection system will be possible by using the combination of learning models. ObjectivesThis research, is an effort to improve the accuracy of DoS attacks detection, compared to previous methods. Also, overcoming the challenge of large number of classes in intrusion detection task using ECOC based hybrid classifiers is one of the main objectives of the research. MethodsIn this paper, a novel method to detect DoS attacks in computer networks is proposed. The proposed method performs the intrusion detection process in three phases named as pre-processing, feature extraction and classification. Principal Component Analysis (PCA) is used for extracting features, while a combination of Error Correcting Output Codes (ECOC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for classification. In this classification model, Particle Swarm Optimization (PSO) algorithm has been used to optimize the structure of ANFIS. ResultsThe performance of the proposed method has been evaluated using the NSLKDD database. Using a 10-fold cross validation experiment, the proposed IDS showed a sensitivity of 99.82%. The results also show that the proposed method can detect the types of DoS attacks with an average accuracy of 98.9%, which shows a significant improvement compared to the previous methods.