Security Engineering with Machine Learning for Adversarial Resiliency in Mobile Cyber Physical Systems

被引：2
|
作者：
Olowononi, Felix O. [1 ]
Rawat, Danda B. [1 ]
Garuba, Moses [1 ]
Kamhoua, Charles [2 ]
机构：
[1] Howard Univ, Dept Elect Engn & Comp Sci, Data Sci & Cybersecur Ctr, Washington, DC 20059 USA
[2] US Army Res Lab, Adelphi, MD USA
来源：
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS | 2019年 / 11006卷
基金：
美国国家科学基金会;
关键词：
Vehicular Cyber Physical Systems; Machine learning; VANET; Security; False data injection; Resiliency; Adversarial; Bayesian model; COMMUNICATION;
D O I：
10.1117/12.2519372
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS.
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收藏
页数：7
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