Titlebook: Network Security Empowered by Artificial Intelligence; Yingying Chen,Jie Wu,Xiaogang Wang Book 2024 The Editor(s) (if applicable) and The

松驰

choroid

Deep Learning for Robust and Secure Wireless Communicationsanging from information access to social networking. However, the emergence of numerous wireless applications is driving the demand for spectrum to unprecedented levels. Simultaneously, wireless systems are becoming increasingly software-driven, reducing the barrier for wireless threats such as inte

纹章

Wallow

扩音器

Localizing Spectrum Offenders Using Crowdsourcing over large areas in order to provide protection to spectrum users. This chapter explores various recent RSS-based localization techniques which use crowdsourced measurements, including path loss models, fingerprinting, and machine learning-based approaches. Our focus is on utilizing convolutional n

归功于

咽下

Security and Privacy of Augmented Reality Systemsem increasingly popular among mass consumers, in industry, and even in military training. In order to support immersive and realistic user experience, AR systems rely on real-time sensing through various types of sensors to understand the physical environment, make intelligent decisions, and render

使出神

Securing Augmented Reality Applicationsalthcare. However, these applications face numerous security challenges, such as data privacy, authentication, and authorization. In this chapter, we explore the use of Artificial Intelligence and Machine Learning techniques to enhance the security of AR applications. We discuss the different securi

genesis

On the Robustness of Image-Based Malware Detection Against Adversarial Attacksnstrated in various network-security-oriented applications such as intrusion detection, cyber threat intelligence, vulnerability discovery, and malware detection. Nevertheless, recent research studies have shown that crafted adversarial samples can be used to evade malware detection models. Even tho

甜得发腻

The Cost of Privacy: A Comprehensive Analysis of the Security Issues in Federated Learningn extensive model trained in a broader dataset without ever sharing their private data directly. FL combines multiple local models into a global model, thereby diminishing the need for individual participants to have large datasets. This decentralized nature of FL makes it more susceptible to advers