Internet of Things (IoT) devices has witnessed a rapid growth and simultaneously has created a data-driven world as vast amounts of high-dimensional information are being generated constantly. However, it also introduces privacy as well as security challenges and protecting sensitive information in IoT networks is highly important. Researchers have come up with a new solution called Privacy-Preserving Statistical Learning to address the challenges. It is equipped with an Optimization Algorithm for High-Dimensional Big Data Environments (PPSLOA-HDBDE).<\/p>\n
The ability to scale and process large datasets effectively is its core. The challenge of high-dimensional data is addressed through feature selection using Sand Cat Swarm Optimizer (SCSO).<\/p>\n
It utilizes an ensemble of advanced machine learning models including Temporal Convolutional Networks (TCN), Multi-Layer Auto-Encoders (MAE) and Extreme Gradient Boosting (XGBoost) to do so. The performance is further optimized with the use of Improved Marine Predator Algorithm (IMPA) that fine-tunes parameters to ensure maximum effectiveness in detecting intrusions.<\/p>\n
The PPSLOA-HDBDE framework achieved an accuracy rate of 99.49% in detecting intrusions in extensive testing. The high level of precision is important for safeguarding IoT networks as these are increasingly vulnerable to cyberattacks such as data breaches, denial of service and unauthorized access.<\/p>\n
Traditional security methods are often insufficient to address the evolving threats posed by malicious attackers. The PPSLOA-HDBDE framework has come up with a promising solution by demonstrating the way advanced machine learning and optimization techniques can address real-world challenges.<\/p>\n","protected":false},"excerpt":{"rendered":"