An Edge-Driven Security Framework for Intelligent Internet of Things

被引:19
作者
Dai, Minghui [1 ]
Su, Zhou [2 ]
Li, Ruidong [3 ]
Wang, Yuntao [4 ]
Ni, Jianbing [5 ]
Fang, Dongfeng [6 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China
[2] Shanghai Univ, Shanghai, Peoples R China
[3] Natl Inst Informat & Commun Technol, Tokyo, Japan
[4] Xi An Jiao Tong Univ, Sch Cyber Sci & Engn, Xian, Peoples R China
[5] Queens Univ, Dept Elect & Comp Engn, Kingston, ON, Canada
[6] Calif Polytech State Univ San Luis Obispo, Dept Comp Sci & Software Engn, San Luis Obispo, CA USA
来源
IEEE NETWORK | 2020年 / 34卷 / 05期
关键词
Internet of Things; Cloud computing; Security; Edge computing; Servers; Logistics; Image edge detection; FOG; CHALLENGES;
D O I
10.1109/MNET.011.2000068
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The rise of the intelligent Internet of Things (IoT) promotes the development of emerging technologies and industries with the integration of edge computing and cloud computing. This integrated paradigm becomes a promising prospect to continuously expand the service capabilities of IoT. However, edge-driven intelligent IoT systems raises serious security issues as the systems are prone to be compromised by malicious attacks. In this article, an edge-driven security framework is investigated for intelligent IoT systems. First, we introduce the architecture of edge-driven intelligent IoT, and present typical edge-driven intelligent IoT applications. Second, we point out the security threats in edge-driven intelligent IoT in terms of attack behaviors of adversaries. Third, we develop a case study of edge-driven intelligent IoT from the security perspective. Finally, we discuss future research directions in this emerging area.
引用
收藏
页码:39 / 45
页数:7
相关论文
共 15 条
[1]   Dense Moving Fog for Intelligent IoT: Key Challenges and Opportunities [J].
Andreev, Sergey ;
Petrov, Vitaly ;
Huang, Kaibin ;
Lema, Maria A. ;
Dohler, Mischa .
IEEE COMMUNICATIONS MAGAZINE, 2019, 57 (05) :34-41
[2]   Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing [J].
Diro, Abebe Abeshu ;
Chilamkurti, Naveen .
IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (02) :169-175
[3]   Securing Collaborative Deep Learning in Industrial Applications Within Adversarial Scenarios [J].
Esposito, Christian ;
Su, Xin ;
Aljawarneh, Shadi A. ;
Choi, Chang .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (11) :4972-4981
[4]   Modeling, Analysis, and Mitigation of Dynamic Botnet Formation in Wireless IoT Networks [J].
Farooq, Muhammad Junaid ;
Zhu, Quanyan .
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2019, 14 (09) :2412-2426
[5]   Smart-Edge-CoCaCo: AI-Enabled Smart Edge with Joint Computation, Caching, and Communication in Heterogeneous IoT [J].
Hao, Yixue ;
Miao, Yiming ;
Hu, Long ;
Hossain, M. Shamim ;
Muhammad, Ghulam ;
Amin, Syed Umar .
IEEE NETWORK, 2019, 33 (02) :58-64
[6]   Security in the Internet of Things Supported by Mobile Edge Computing [J].
He, Daojing ;
Chan, Sammy ;
Guizani, Mohsen .
IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (08) :56-61
[7]   Challenges and Solutions in Fog Computing Orchestration [J].
Jiang, Yuxuan ;
Huang, Zhe ;
Tsang, Danny H. K. .
IEEE NETWORK, 2018, 32 (03) :122-129
[8]   MHCP: Multimedia Hybrid Cloud Computing Protocol and Architecture for Mobile Devices [J].
Jimenez, Jose M. ;
Diaz, Juan R. ;
Lloret, Jaime ;
Romero, Oscar .
IEEE NETWORK, 2019, 33 (01) :106-112
[9]  
Li W., IEEE T NETW SCI ENG, DOI [10.1109/TNSE.2020.3014380, DOI 10.1109/TNSE.2020.3014380]
[10]   Defending Malicious Check-In Using Big Data Analysis of Indoor Positioning System: An Access Point Selection Approach [J].
Li, Weiwei ;
Su, Zhou ;
Zhang, Kuan ;
Benslimane, Abderrahim ;
Fang, Dongfeng .
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2020, 7 (04) :2642-2655