Advanced Machine Learning Applications for Security, Privacy, and Reliability

Dear Colleagues,

Approaching the era of the Internet of Things (IoTs) has brought great convenience to the public through the interconnection of all things. Although our lives benefit from the emerging techniques, we face severe security, privacy, and reliability concerns. With the development of big data, there is a growing need for access control and privacy. Machine learning provides a promising solution to protect user data and detect known and unknown malicious attacks. Thus, advanced machine learning applications have been proposed to address the issues of security, privacy, and reliability in the IoTs. Additionally, machine learning in life-critical applications, such as autonomous driving, smart cities, healthcare, etc., security, privacy, and reliability should be the first and foremost concern.

This Special Issue aims to solicit innovative perspectives that focus on two fundamental questions: 1) How can advanced machine learning applications be exploited to address the issues of security, privacy, and reliability? 2) What security, privacy, and reliability concerns the advanced machine learning applications have incurred?

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Security and privacy in smart city;
• Advances in machine learning frameworks for intrusion detection;
• Adversarial attacks against deep neural networks;
• Reliability in computer vision systems;
• Advanced machine learning for industrial internet;
• Machine-learning-assisted side-channel attacks;
• Security protocols in cyber-physical systems;
• Trusted computing in machine learning;
• Advanced machine learning for hardware security.

I/We look forward to receiving your contributions.

Prof. Dr. Jiliang Zhang 
Dr. Zhaojun Lu
Dr. He Li
Dr. Zukun Lu
Guest Editors

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