TY - JOUR

T1 - Towards SDN-Enabled, Intelligent IntrusionDetection System for Internet of Things (IoT)

AU - Muthanna, Mohammed Saleh Ali

AU - Alkanhel, Reem

AU - Muthanna, Ammar

AU - Rafiq, Ahsan

AU - Abdullah, Wadhah Ahmed Muthanna

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2022/2/22

Y1 - 2022/2/22

N2 - The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

AB - The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need to protect the IoT environment from such attacks and malware. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

KW - Deep learning

KW - IoT

KW - intrusion detection

KW - network security

KW - software-defined network

UR - http://www.scopus.com/inward/record.url?scp=85125319116&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2022.3153716

DO - 10.1109/ACCESS.2022.3153716

M3 - Article

VL - 10

SP - 22756

EP - 22768

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -