Security is critical in the Cyber-Physical Systems (CPS) model for smart healthcare networks, and it will likely have a significant impact on the industry, medical and healthcare; and farming-related substructures shortly. Due to an increase in the frequency of security and privacy attacks in present times in healthcare networks, this article addressed a fundamental component of intrusion detection systems (IDS) based on the important parameter security. The limitations of IDS in reacting to cyberattacks as well as in establishing private controls in the field of smart healthcare have motivated this research. An efficient and lightweight deep learning-based CNN-Bidirectional LSTM is proposed for the DDoS detection that uses the features of Convolutional Neural Networks (CNNs) to classify traffic flows as benign and malicious in this study. The results are achieved using Python where four convolutional layers, Maximum Pooling, that ends with the Dense Layer. The hyperparameters used are batch size of 500, epochs 20, number of classes 25, and Relu and softmax pooling activation function along with the softmax

Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.

The rapid growth of online social networks has created significant opportunities for large-scale information dissemination, where many users seek to maximize the visibility and influence of their information, bringing significant attention to the problem of influence maximization (IM). IM aims to identify a limited set of influential users (seed nodes) to maximize information spread. However, existing IM approaches typically provide a unified solution for all users under a fixed seed budget, without considering that different users in real-world scenarios have inherently different target diffusion ranges and seed budgets. Such omissions lead to inefficiency and resource waste, particularly when excessive seed budgets are allocated to users with low diffusion demands. To overcome this issue, we introduce Personalized Influence Maximization (PIM) as an extension of classical IM. Building upon this formulation, we propose the Adaptive Graph Influencer Generator (AGIG), which models seed set selection as a sequence generation task and employs a causal transformer to autoregressively generate personalized and cost-effective seed sets tailored to diverse demands of users. In particular, AGIG incorporates an enhanced dual-view influence encoder that models realistic scenarios where information can reach users without direct connections but with similar interests, thereby strengthening node representations for high-quality seed generation. For effective training, we construct the Propagation Pathways Sequence Dataset by simulating diffusion processes under multiple classical diffusion models and graph structures, enabling AGIG to learn diverse propagation patterns across varying diffusion settings. Extensive experiments demonstrate that AGIG effectively adapts to diverse personalized propagation requirements, achieving an average improvement of approximately 15% in influence spread and cost efficiency over strong baseline methods across multiple datasets and diffusion settings.