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

Basit Öğe Kaydı
dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorMajidian, Zohre
dc.contributor.authorTaghipourEivazi, Shiva
dc.contributor.authorArasteh, Bahman
dc.contributor.authorBabai, Shahram
dc.date.accessioned2024-05-19T14:42:49Z
dc.date.available2024-05-19T14:42:49Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractContext: A 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. Problem: Most 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. Objectives: This 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. Methods: In 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 preprocessing, 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. Results: The 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.en_US
dc.identifier.doi10.1016/j.compeleceng.2023.108600
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.scopus2-s2.0-85146682845en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.compeleceng.2023.108600
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5287
dc.identifier.volume106en_US
dc.identifier.wosWOS:000924437300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectIntrusion Detection Methoden_US
dc.subjectDenial Of Service Attacksen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectAdaptive Neuro-Fuzzy Inference Systemen_US
dc.subjectError -Correcting Output Codesen_US
dc.titleAn intrusion detection method to detect denial of service attacks using error-correcting output codes and adaptive neuro-fuzzy inferenceen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu