The chapter tries to resolve this emerging problem of machine learning-based smart heterogeneous networks, and cybersecurity by creating a multi-layered security infrastructure which is scalable to detect and react to security attacks in real-time. It proposes a multi-modal deep learning (CNNLSTM), adaptive control by reinforcement learning, and privacy-sensitive controls, such as federated learning. Heterogeneous datasets, including the IoT traffic, intrusion detection benchmarks, and multi-step attack scenarios, which are synthetic, are trained and tested with the model. The accuracy, precision, recall, F1-score, AUC-ROC, and the computational efficiency metrics are used to measure the performance in dynamic and noisy settings. The suggested framework is also a better performer than the baseline models, where accuracy is 98.37 and has a better ability to resist multi-step and evolving cyber threats. Integration of reinforcement learning increases adaptability and multi-modal features fusion increases detection accuracy.
Harish Reddy Gantla, N. Thangadurai, Manikandan Hariharan et al.· Advances in wireless technol...· 0 citations
The proposed chapter suggest a highly complex machine learning crime prediction model on an intelligent heterogeneous policing network, which will have to consider dynamic environment, multimodal, and real-time decision-making. A deep learning (CNN LSTM) hybrid architecture that is founded on graph neural networks and reinforcement learning is created. With the assistance of multimodal fusion strategy and the security and privacy preservation measures that are inherent in the framework, the spatial, temporal, behavioral and environmental data are calculated. The proposed model is more predictive since it has an accuracy of 96.8, and AUC-ROC of 0.98, which is better than the traditional and standalone deep learning models. The adaptive learning and multimodal data can be much more useful regarding context awareness, power and responsiveness in the intricate police situations. The framework will also help in the real-time crime prediction and the best distribution of resources and as such can be applied in smart city policing systems.
Pradeep Sambamurthy, Sanjeev Gour, S. Vamsee Krishna et al.· Advances in wireless technol...· 0 citations
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