首页|Data on Machine Learning Published by Researchers at Sejong University (An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning)

Data on Machine Learning Published by Researchers at Sejong University (An Incremental Majority Voting Approach for Intrusion Detection System Based on Machine Learning)

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A new study on artificial intelligence is now available. According to news reporting from Seoul, South Korea, by NewsRx journalists, research stated, "With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate." Financial supporters for this research include National Research Foundation of Korea; Ministry of Education; Nrf; Ministry of Science And Ict. Our news editors obtained a quote from the research from Sejong University: "Intrusion Detection Systems (IDS) have been widely employed in conjunction with firewalls to safeguard networks. However, traditional IDS systems operate in a static manner, rendering them vulnerable to obsolescence and necessitating costly retraining efforts. As a result, the demand for dynamic models capable of handling continuous streams of network traffic has surged as they can learn from the incoming traffic without the need to old data and costly retraining. In response to this challenge, we have implemented an enhanced approach: an incremental majority voting IDS system, which utilizes existing tools and techniques to improve the robustness and adaptability of intrusion detection By leveraging the collective decision-making power of multiple machine learning models such as: KNN classifier, Softmax Regressor and Adaptive Random Forest classifier, our system aims to improve the accuracy, especially reducing false alarm rates, and effectiveness of intrusion detection in real-time scenarios. Through this research, we have managed to obtain a model which scores 96.43% of accuracy as well as giving 100% precision for majority type of attacks."

Sejong UniversitySeoulSouth KoreaAsiaCybersecurityCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.28)
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