The proliferation of Internet of Things (IoT) devices has led to a surge in network traffic and potential security vulnerabilities, making intrusion detection systems (IDS) crucial for protecting IoT infrastructure. However, traditional centralized IDS approaches face challenges in handling the volume, velocity, and variety of IoT data, while also raising privacy concerns due to the collection and storage of sensitive network information. This paper proposes a federated learning (FL) framework for enhanced intrusion detection in IoT networks. FL enables collaborative model training across decentralized IoT devices without directly sharing raw data, thus addressing privacy concerns. The proposed framework utilizes a deep learning model trained in a federated manner to detect various types of network intrusions. We evaluate the performance of our approach using a simulated IoT network environment and demonstrate its effectiveness in detecting intrusions while preserving data privacy and achieving scalability. The results show that the FL-based IDS achieves comparable or superior performance to centralized approaches, while significantly reducing the risk of data breaches and complying with data privacy regulations. Finally, we discuss the challenges and future directions for applying FL in IoT security.
The increasing volume and complexity of healthcare data present both opportunities and challenges for predictive modeling. While big data analytics holds immense promise for improving diagnosis, treatment, and patient outcomes, the sensitive nature of medical information necessitates stringent privacy safeguards. This paper proposes a novel approach that combines Federated Learning (FL) with Differential Privacy (DP) to enable collaborative model training across multiple healthcare institutions without directly sharing patient data. We develop and evaluate a framework that allows for distributed model training while ensuring patient privacy through the application of DP mechanisms during the aggregation of model updates. Our results demonstrate that this approach can achieve comparable predictive performance to centralized training while significantly mitigating privacy risks. We analyze the trade-off between privacy and accuracy and provide insights into the optimal configuration of DP parameters for healthcare applications. The proposed framework offers a practical and scalable solution for leveraging the power of big data in healthcare while upholding ethical and legal obligations related to data privacy.
This paper presents an adaptive distributed deep learning framework designed for real-time predictive maintenance within Industrial Internet of Things (IIoT) environments. The framework addresses the challenges of processing massive, high-velocity data streams generated by industrial sensors. We propose a novel architecture that combines edge computing with cloud-based deep learning, enabling real-time anomaly detection and predictive failure analysis. The framework incorporates an adaptive learning mechanism that dynamically adjusts model parameters based on the evolving characteristics of the data stream, ensuring sustained accuracy and robustness. We evaluate the performance of the proposed framework using a real-world industrial dataset and demonstrate its superiority over existing methods in terms of prediction accuracy, latency, and resource utilization.
The rapid growth of data generation, particularly in streaming environments, presents significant challenges for anomaly detection. High-dimensionality, temporal dependencies, and the sheer volume of data necessitate sophisticated approaches. This paper proposes a novel hybrid deep learning framework that integrates the strengths of autoencoders and Long Short-Term Memory (LSTM) networks for anomaly detection in high-dimensional streaming data. The autoencoder component reduces dimensionality and extracts salient features, while the LSTM network models temporal dependencies to identify deviations from normal patterns. The framework is evaluated on a real-world network traffic dataset and compared with state-of-the-art anomaly detection methods. The results demonstrate that the proposed hybrid approach achieves superior performance in terms of accuracy, precision, recall, and F1-score, offering a robust and efficient solution for anomaly detection in complex big data environments.
This paper explores the application of distributed deep learning techniques, coupled with advanced feature engineering, to enhance predictive maintenance capabilities within the Industrial Internet of Things (IIoT) landscape. The increasing volume and velocity of data generated by IIoT devices present significant challenges for traditional predictive maintenance approaches. We propose a novel methodology that leverages the distributed processing capabilities of Apache Spark to handle large-scale sensor data, combined with carefully engineered features derived from time-series analysis and domain expertise. A Long Short-Term Memory (LSTM) network, trained in a distributed manner using TensorFlow on a Spark cluster, is employed to predict equipment failures. The efficacy of the proposed approach is demonstrated through experiments on a simulated industrial dataset, showcasing significant improvements in prediction accuracy and reduced false positive rates compared to conventional methods. The results highlight the potential of distributed deep learning and feature engineering to revolutionize predictive maintenance in IIoT environments, leading to reduced downtime, improved operational efficiency, and cost savings.