TY - JOUR
T1 - DeepIoT.IDS
T2 - Hybrid deep learning for enhancing IoT network intrusion detection
AU - Maseer, Ziadoon K.
AU - Yusof, Robiah
AU - Mostafa, Salama A.
AU - Bahaman, Nazrulazhar
AU - Musa, Omar
AU - Al-Rimy, Bander Ali Saleh
N1 - Publisher Copyright:
© 2021 Tech Science Press. All rights reserved.
PY - 2021/8/24
Y1 - 2021/8/24
N2 - With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design a self-adaptive model for an intrusion detection system (IDS) to improve the detection of attacks. Consequently, in this paper, a novel hybrid weighted deep belief network (HW-DBN) algorithm is proposed for building an efficient and reliable IDS (DeepIoT.IDS) model to detect existing and novel cyberattacks. The HW-DBN algorithm integrates an improved Gaussian-Bernoulli restricted Boltzmann machine (Deep GB-RBM) feature learning operator with a weighted deep neural networks (WDNN) classifier. The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks, complex data patterns, noise values, and imbalanced classes. We have compared the performance of the DeepIoT.IDS model with three recent models. The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38% and 99.99% for web attack and bot attack scenarios, respectively. Furthermore, it can detect the occurrence of low-frequency attacks that are undetectable by other models.
AB - With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design a self-adaptive model for an intrusion detection system (IDS) to improve the detection of attacks. Consequently, in this paper, a novel hybrid weighted deep belief network (HW-DBN) algorithm is proposed for building an efficient and reliable IDS (DeepIoT.IDS) model to detect existing and novel cyberattacks. The HW-DBN algorithm integrates an improved Gaussian-Bernoulli restricted Boltzmann machine (Deep GB-RBM) feature learning operator with a weighted deep neural networks (WDNN) classifier. The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks, complex data patterns, noise values, and imbalanced classes. We have compared the performance of the DeepIoT.IDS model with three recent models. The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38% and 99.99% for web attack and bot attack scenarios, respectively. Furthermore, it can detect the occurrence of low-frequency attacks that are undetectable by other models.
KW - Cyberattacks
KW - Deep learning neural network
KW - Internet of things
KW - Intrusion detection system
KW - Supervised and unsupervised deep learning
UR - http://www.scopus.com/inward/record.url?scp=85113442013&partnerID=8YFLogxK
U2 - 10.32604/cmc.2021.016074
DO - 10.32604/cmc.2021.016074
M3 - Article
AN - SCOPUS:85113442013
SN - 1546-2218
VL - 69
SP - 3946
EP - 3967
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 3
ER -