***:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

作     者：Ziadoon K.Maseer Robiah Yusof Salama A.Mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy 

作者机构：Faculty of Information and Communication TechnologyUniversiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous SystemsFaculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and TechnologyUNITAR International UniversitySelangor47301Malaysia School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3945-3966页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was partially funded by the Industry Grant Scheme from Jaycorp Berhad in cooperation with UNITAR International University.The authors would like to thank INSFORNET the Center for Advanced Computing Technology(C-ACT)at Universiti Teknikal Malaysia Melaka(UTeM) and the Center of Intelligent and Autonomous Systems(CIAS)at Universiti Tun Hussein Onn Malaysia(UTHM)for supporting this work 

主　　题：Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning 

摘      要：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 ***,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of ***,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 ***,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of ***,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(***)model to detect existing and novel *** HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)*** CICIDS2017 dataset is selected to evaluate the *** model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced *** have compared the performance of the *** model with three recent *** results show the *** 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,***,it can detect the occurrence of low-frequency attacks that are undetectable by other models.