首页|Research from National Institute of Technology Provides New Data on Machine Lear ning (Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection)
Research from National Institute of Technology Provides New Data on Machine Lear ning (Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting out of the National Institute of Technology by NewsRx editors, research stated, "The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detecti on. Effective feature selection is crucial for eliminating irrelevant informatio n." Our news reporters obtained a quote from the research from National Institute of Technology: "Presently, most filtering and embedded methods rely on fixed thres holds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduc e biases in evaluation. To address these challenges, this study introduces Adapt ive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection a pproach that adapts to local statistical properties of the data. Unlike traditio nal methods, AN-SFS adjusts its threshold based on the characteristics of the cu rrent feature subset and incorporates statistical measures of neighboring featur es, capturing subtle relationships and dependencies. This adaptability enables A N-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accu racy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB1 5, significantly outperforming contemporary methods."
National Institute of TechnologyCybers ecurityCyborgsEmerging TechnologiesMachine Learning
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