Titlebook: Artificial Intelligence Security and Privacy; First International Jaideep Vaidya,Moncef Gabbouj,Jin Li Conference proceedings 2024 The Edi

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,Protecting Bilateral Privacy in?Machine Learning-as-a-Service: A Differential Privacy Based Defenseo query requests and model responses, both the client and server sides in MLaaS are privacy-protected. Experimental results also demonstrate the effectiveness of the proposed solution in ensuring accuracy and providing privacy protection for both the clients and servers in MLaaS.

HALO

,An Embedded Cost Learning Framework Based on?Cumulative Gradient Rewards,the steganography problem. In this framework, the reward function assigns distortion values to each pixel of the image and relates the security performance of steganography. Based on the conducted experiments, an enhanced steganographic embedding scheme can ultimately be achieved.

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https://doi.org/10.1007/978-3-031-02260-9ues to generate the transformed dataset of test set and shapelet; finally, combine with KNN classifier for network traffic anomaly detection. In this paper, multi-classification experiments are conducted on one available dataset, NSL-KDD with 99.18. accuracy, and the experimental results are analyzed for model solvability.

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,Converging Blockchain and?Deep Learning in?UAV Network Defense Strategy: Ensuring Data Security Durce UAV safety during operations and provide robust protection against cyber threats. A series of experimental tests were conducted, simulating various UAV network attack scenarios. The results of these experiments unequivocally demonstrate the feasibility and effectiveness of the blockchain-driven UAV network service architecture.