首页|Report Summarizes Machine Learning Study Findings from North Carolina State Univ ersity (Self-Supervised Machine Learning Framework for Online Container Security Attack Detection)
Report Summarizes Machine Learning Study Findings from North Carolina State Univ ersity (Self-Supervised Machine Learning Framework for Online Container Security Attack Detection)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting out of Raleigh, Nort h Carolina, by NewsRx editors, research stated, “Container security has received much research attention recently.” The news editors obtained a quote from the research from North Carolina State Un iversity: “Previous work has proposed to apply various machine learning techniqu es to detect security attacks in containerized applications. On one hand, superv ised machine learning schemes require sufficient labeled training data to achiev e good attack detection accuracy. On the other hand, unsupervised machine learni ng methods are more practical by avoiding training data labeling requirements, b ut they often suffer from high false alarm rates. In this article, we present a generic self-supervised hybrid learning (SHIL) framework for achieving efficient online security attack detection in containerized systems. SHIL can effectively combine both unsupervised and supervised learning algorithms but does not requi re any manual data labeling.”
North Carolina State UniversityRaleighNorth CarolinaUnited StatesNorth and Central AmericaCyborgsEmerging Te chnologiesMachine Learning
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