Industrial Control Systems (ICS) are increasingly vulnerable to cyberattacks, making robust anomaly detection crucial for maintaining operational integrity and safety. This paper presents a novel hybrid deep learning framework designed to enhance anomaly detection capabilities in ICS environments. The framework combines the strengths of Convolutional Neural Networks (CNNs) for feature extraction from raw sensor data and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for capturing temporal dependencies within system behavior. By integrating these two architectures, the proposed model effectively learns complex patterns and detects subtle deviations indicative of anomalies. The framework is evaluated using a benchmark ICS dataset, demonstrating superior performance compared to traditional machine learning methods and single deep learning models. The results highlight the potential of the hybrid approach for improving the security and reliability of critical infrastructure.
Deep learning models have achieved state-of-the-art performance in various domains, but their effectiveness heavily relies on the proper tuning of hyperparameters. Traditional hyperparameter optimization methods often suffer from high computational costs and limited adaptability to different datasets and model architectures. This paper proposes a novel adaptive hyperparameter optimization approach that leverages reinforcement learning (RL) with dynamic exploration-exploitation balancing. The RL agent learns to select optimal hyperparameter configurations based on the observed performance of the deep learning model. A key contribution is the dynamic adjustment of the exploration-exploitation trade-off, allowing the agent to efficiently explore the hyperparameter space while also exploiting promising regions. We evaluate our approach on several benchmark datasets and deep learning architectures, demonstrating its superior performance compared to existing hyperparameter optimization techniques in terms of accuracy, convergence speed, and computational efficiency. The results highlight the potential of adaptive RL-based methods for automating and improving the hyperparameter tuning process in deep learning.
The proliferation of Internet of Things (IoT) devices in healthcare has generated vast amounts of sensitive patient data, creating opportunities for personalized and proactive care. However, directly centralizing this data poses significant privacy risks. This paper proposes a novel framework that integrates Federated Learning (FL) and Differential Privacy (DP) to address these challenges. FL enables collaborative model training across decentralized IoT devices without sharing raw data, while DP provides rigorous privacy guarantees by adding controlled noise during the learning process. Our approach enhances security and personalization in IoT healthcare applications by enabling robust model development while preserving patient confidentiality. We present a detailed methodology, experimental results on simulated healthcare datasets, and a thorough discussion of the trade-offs between privacy, accuracy, and communication efficiency. The results demonstrate the feasibility and effectiveness of the proposed framework for improving healthcare outcomes while maintaining stringent data privacy standards. We also explore the challenges of implementing this framework in real-world scenarios and suggest potential future research directions, including adaptive privacy mechanisms and optimized communication protocols.
Industrial Control Systems (ICS) are increasingly vulnerable to cyberattacks, posing significant risks to critical infrastructure. Traditional intrusion detection systems (IDS) often struggle to effectively identify sophisticated threats in ICS environments due to the unique characteristics of network traffic and the evolving threat landscape. This paper proposes a novel hybrid deep learning approach for enhanced intrusion detection in ICS, combining feature selection techniques with ensemble learning. The proposed methodology leverages the strengths of multiple deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to capture both spatial and temporal patterns in network traffic data. Feature selection is employed to identify the most relevant features, reducing dimensionality and improving model performance. The ensemble approach combines the predictions of individual deep learning models to enhance accuracy and robustness. The effectiveness of the proposed methodology is evaluated using a publicly available ICS dataset, demonstrating superior performance compared to existing state-of-the-art intrusion detection techniques. The results highlight the potential of the proposed hybrid deep learning approach to significantly improve the security of ICS environments.
The proliferation of Internet of Things (IoT) devices has created a vast and vulnerable attack surface, making intrusion detection a critical component of IoT security. Traditional intrusion detection systems (IDSs) often struggle with the complexity and dynamism of IoT network traffic. This paper proposes a novel Adaptive Hybrid Deep Learning Framework (AHDL-IDF) for enhanced intrusion detection in IoT networks. Our framework integrates Convolutional Neural Networks (CNNs) for effective feature extraction from network traffic data and Recurrent Neural Networks (RNNs) with attention mechanisms to capture temporal dependencies and prioritize relevant features for improved accuracy. The adaptive nature of the framework allows it to dynamically adjust its parameters based on the characteristics of the incoming traffic, enhancing its resilience to evolving attack patterns. We evaluate the performance of the AHDL-IDF using a publicly available IoT network traffic dataset and compare it against existing state-of-the-art IDS models. The experimental results demonstrate that the AHDL-IDF achieves significantly higher detection accuracy, lower false positive rates, and improved adaptability compared to existing approaches, making it a promising solution for securing IoT networks.