首页|Study Findings from University of Technology Malaysia Advance Knowledge in Machi ne Learning (Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning)
Study Findings from University of Technology Malaysia Advance Knowledge in Machi ne Learning (Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning)
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Springer Nature
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 originating from the University o f Technology Malaysia by NewsRx correspondents, research stated, “In- ternet of Th ings (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT.”
University of Technology MalaysiaCyber securityCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Springer Nature
