Description: 基于机器学习的入侵检测技术研究,机器学习是CS领域中最好玩的分支之一。把它应用到网络安全领域也是很有意思的。-Intrusion detection based on machine learning technology research, the field of machine learning is the CS branch of one of the most fun. And apply it to the field of network security is also very interesting. Platform: |
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Author:张伟 |
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Description: The problem of intrusion detection has been studied and received a lot of attention in
machine learning and data mining in the literature survey. The existing techniques are not
effective to improve the classification accuracy and to reduce high false alarm rate.
Therefore, it is necessary to propose new technique for IDS. In this work, we propose a
new K-means clustering method with a different Preprocessing and Genetic Algorithm
for identifying intrusion and classification for both anomaly and misuse.
The experiments of the proposed IDS are performed with KDD cup’99 data set. The
experiments will clearly results the proposed method provides better classification
accuracy over existing method. Platform: |
Size: 400384 |
Author:Sumit |
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Description: A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning
Techniques for Intrusion Detection Platform: |
Size: 112640 |
Author:salem |
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Description: In recent years, the needs of the Internet are felt in lives of all people.
Accordingly, many studies have been done on security in virtual environment. Old
technics such as firewalls, authentication and encryption could not provide Internet
security completely; So, Intrusion detection system is created as a new solution and
a defense wall in cyber environment. Many studies were performed on different
algorithms but the results show that using machine learning technics and swarm
intelligence are very effective to reduce processing time and increase accuracy as
well. In this paper, hybrid SVM and ABC algorithms has been suggested to select
features to enhance network intrusion detection and incr Platform: |
Size: 11619328 |
Author:alqatf |
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Description: intrusion detection system (IDS) Deep Learning (DL) has emerged as a new approach that delivers higher accuracy than traditional machine learning techniques. DL has the ability to process raw data and learn the high level features on its own, and so DL has a strong case for its adaptability in resource constrained networks like SDNs. Platform: |
Size: 3205378 |
Author:thirumal@techquest.info |
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Description: The cybersecurity community is slowly leveraging machine learning (ML) to
combat ever-evolving threats. One of the biggest drivers for the successful
adoption of these models is how well domain experts and users can under
stand and trust their functionality. Most models are perceived as a black box
despite the growing popularity of machine learning models in cybersecurity
applications (e.g., an intrusion detection system (IDS)). As these black-
box models are employed to make meaningful predictions, the stakeholders’
demand for transparency and explainability increases. Explanations support
ing the output of ML models are crucial in cybersecurity, where experts
require far more information from the model than a simple binary output for
their analysis. Platform: |
Size: 28830371 |
Author:iqzer0 |
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