TY - JOUR
T1 - Multi-Level Deep Neural Network for Distributed Denial-of-Service Attack Detection and Classification in Software-Defined Networking Supported Internet of Things Networks
AU - Abid, Yawar Abbas
AU - Wu, Jinsong
AU - Xu, Guangquan
AU - Fu, Shihui
AU - Waqas, Muhammad
PY - 2024
Y1 - 2024
N2 - With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.
AB - With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.
KW - CIC-DDoS 2019
KW - classification
KW - Computer crime
KW - convolutional neural network (CNN)
KW - Convolutional neural networks
KW - Deep learning
KW - Denial-of-service attack
KW - distributed denial of service (DDoS)
KW - Internet of Things
KW - long-short-term memory (LSTM)
KW - recurrent neural network (RNN)
KW - Security
KW - software-defined networking (SDN)
KW - Telecommunication traffic
UR - http://www.scopus.com/inward/record.url?scp=85187996336&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2024.3376578
DO - 10.1109/JIOT.2024.3376578
M3 - Article
AN - SCOPUS:85187996336
SN - 2327-4662
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
ER -