دورية أكاديمية

Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment

التفاصيل البيبلوغرافية
العنوان: Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment
المؤلفون: Matheus P. Novaes, Luiz F. Carvalho, Jaime Lloret, Mario Lemes Proenca
المصدر: IEEE Access, Vol 8, Pp 83765-83781 (2020)
بيانات النشر: IEEE, 2020.
سنة النشر: 2020
المجموعة: LCC:Electrical engineering. Electronics. Nuclear engineering
مصطلحات موضوعية: DDoS, deep learning, fuzzy, LSTM, portscan, SDN, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
الوصف: Computer networks become complex and dynamic structures. As a result of this fact, the configuration and the managing of this whole structure is a challenging activity. Software-Defined Networks(SDN) is a new network paradigm that, through an abstraction of network plans, seeks to separate the control plane and data plane, and tends as an objective to overcome the limitations in terms of network infrastructure configuration. As in the traditional network environment, the SDN environment is also liable to security vulnerabilities. This work presents a system of detection and mitigation of Distributed Denial of Service (DDoS) attacks and Portscan attacks in SDN environments (LSTM-FUZZY). The LSTM-FUZZY system presented in this work has three distinct phases: characterization, anomaly detection, and mitigation. The system was tested in two scenarios. In the first scenario, we applied IP flows collected from the SDN Floodlight controllers through emulation on Mininet. On the other hand, in the second scenario, the CICDDoS 2019 dataset was applied. The results gained show that the efficiency of the system to assist in network management, detect and mitigate the occurrence of the attacks.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2169-3536
Relation: https://ieeexplore.ieee.org/document/9085352/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2020.2992044
URL الوصول: https://doaj.org/article/2a6cf57949494f6bb7d1d3ecabfa5623
رقم الأكسشن: edsdoj.2a6cf57949494f6bb7d1d3ecabfa5623
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:21693536
DOI:10.1109/ACCESS.2020.2992044