The purpose chapter objectives to design a scalable and intelligent threat detection system to smart policing networks that leverages the IoT technology at scale. It will deal with the problems of timely processing of data of various types of devices and security issues related to such devices. It is suggested to use a multi-layer architecture based on multi-modal (different forms of media) deep learning, near the source (edge) processing of data as well as federated (distributed) learning to provide/network intelligence to the threat detection system. The system is a combination of CNN-BLSTM-based feature learning and adaptive decision mechanisms to allow real-time detection and response. The hybrid dataset of benchmark intrusion data, real IoT traffic, and simulated attack scenarios are used to validate this experiment. The proposed framework has a high detection accuracy (96.7%) and low latency (~280 ms), which is better than the traditional, machine learning, and state-of-the-art deep learning models.
Megha Mudholkar, Pankaj Mudholkar, Prasuna Kotturu et al.· Advances in wireless technol...· 0 citations
The excessive proliferation of Internet of Things (IoT) ecosystems, which are characterized by the high number of devices, transient data generation, and constantly changing cyber threats have presented serious challenges to digital forensic investigations. To solve these problems, Adaptive Forensic Intelligence Model (AFIM) is suggested as a combined model. AFIM is a multi-modal evidence-gathering mechanism, a blockchain-based secure evidence management mechanism, and an AI-based forensic analytics engine. The model has been experimentally tested on a hybrid dataset of real data of the IoT and simulated cyberattack scenarios. The federated learning framework enables models to be developed in a decentralized manner as well as using adaptive thresholding for anomaly detection “on the fly”, thereby creating scalable and resilient systems to operate in distributed environments.
Meenakshi Gupta, R. Udaya Bharathi, Subarno Bhattacharyya 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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