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

宣傳

Lessons Learned and Future Directions for Security, Resilience and Artificial Intelligence in Cyber and cyber attacks have been shown to cause safety violations, which can damage these systems and endanger human lives. The past decade has seen the proliferation of research efforts related to security and resilience in cyber physical systems, with an abundance of publications, workshops, and even

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nCore: Clean Slate Next-G Mobile Core Network Architecture for Scalability and Low Latencyde of the 3GPP specification. The design of nCore is based on distributed information-centric architecture with unique identifiers for network objects along with the concept of locator-ID separation. A clean slate flat network core architecture is presented with details of the design and key protoco

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Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networksitical assets on the battlefield before they can conduct reconnaissance or gain necessary access or credentials. We demonstrate the effectiveness of our game-theoretic design, which uses a meta-learning framework to enable zero-trust monitoring and decision-dominant defense against attackers in emer

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Thyroiditis

Understanding the Ineffectiveness of the Transfer Attack in Intrusion Detection Systemto generate different AEs based on specific surrogate models. To explore the transferability of AEs, we use different AEs to interact with different well-trained models, in order to find the key insights of transfer attacks in the network. We find that transfer attacks have some common properties wi

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Advanced ML/DL-Based Intrusion Detection Systems for Software-Defined Networksrusion detection methods for SDN. We will focus on specific network intrusion methods designed for SDN and evaluate their effectiveness and efficiency based on metrics such as accuracy, processing time, overhead, and false positive rate. Through real testbed evaluations, we aim to identify the most

aptitude

Deep Learning for Robust and Secure Wireless Communications of wireless communication systems..In this chapter, we present three Deep Learning-based solutions for achieving robust and secure wireless communications. The first is a real-time system capable of detecting, classifying, and spectro-temporally localizing wireless collisions and emissions across a

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Universal Targeted Adversarial Attacks Against mmWave-Based Human Activity Recognitionl investigate both white-box and black-box adversarial attacks on these mmWave-based HAR systems. Our strategy encompasses the two prevalent mmWave-based HAR models—voxel-based and heatmap-based, thereby enhancing the reach of our attack. Our evaluations underscore the effectiveness of our designed

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Adversarial Online Reinforcement Learning Under Limited Defender Resourceso change policies. Thus, in addition to the standard metric of losses, switching costs, which capture the costs for changing policies, are regarded as a critical metric in RL. This chapter will introduce the state-of-the-art results on both bandits and RL with switching costs, their importance on ne

背帶

On the Robustness of Image-Based Malware Detection Against Adversarial Attacks Method (FGSM) attacks for white-box settings. To this end, we implement a Convolutional Neural Network (CNN) to classify the image representations of Windows Portable Executable (PE) malware with a detection accuracy of 95.05%. Then, we evaluate its robustness along with MalConv, a state-of-the-art