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  1. 1
    Machine learning algorithms in context of intrusion detection

    Machine learning algorithms in context of intrusion detection by Mehmood, T., Rais, H.B.Md.

    Published 2016
    “…Asymmetrically, anomaly based detection method can detect novel attacks but it has high false positive rate. …”
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  2. 2
    Algorithm enhancement for host-based intrusion detection system using discriminant analysis

    Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

    Published 2004
    “…Algorithms for building detection models are usually classified into two categories: misuse detection and anomaly detection. …”
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  3. 3
    Artificial immune system based on real valued negative selection algorithms for anomaly detection

    Artificial immune system based on real valued negative selection algorithms for anomaly detection by Khairi, Rihab Salah

    Published 2015
    “…Experimental results illustrate that RNSA and V-Detector algorithms are suitable for the detection of anomalies, with SVM and KNN producing significant efficiency rates and increase in execution time. …”
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  4. 4
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…An efficient IDS uses computational methods as techniques of machine learning (ML) to enhance the rates of detection to obtain the lowest false positive rate, although such rates tend to be reduced by the big amount of irrelevant features as an optimization issue. …”
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  5. 5
    Analysis of artificial neural network and viola-jones algorithm based moving object detection

    Analysis of artificial neural network and viola-jones algorithm based moving object detection by Rashidan, M. Ariff, Mohd Mustafah, Yasir, Zainal Abidin, Zulkifli, Zainuddin, N. Afiqah, A. Aziz, Nor Nadirah

    Published 2014
    “…Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. …”
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  6. 6
    On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection

    On the combination of adaptive neuro-fuzzy inference system and deep residual network for improving detection rates on intrusion detection by Jia, Liu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

    Published 2022
    “…Also, the performance of the proposed method is compared with the original ResNet algorithm and other deep learning-based and ANFIS-based methods. …”
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  7. 7
    Improved Switching-Basedmedian Filter For Impulse Noise Removal

    Improved Switching-Basedmedian Filter For Impulse Noise Removal by Teoh, Sin Hoong

    Published 2013
    “…Based on the evaluations from root mean square error (RMSE), false positive detection rate, false negative detection rate, mean structure similarity index (MSSIM), processing time, and visual inspection, it is shown that the proposed method is the best method when compared with seven other state-of-the art median filtering methods.…”
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  8. 8
    An immune-genetic algorithm with tabu local search for network intrusion detection system / Hamizan Suhaimi

    An immune-genetic algorithm with tabu local search for network intrusion detection system / Hamizan Suhaimi by Suhaimi, Hamizan

    Published 2021
    “…The performance of the proposed method and other existing techniques (Genetic Algorithm, Artificial Immune System and Immune-Genetic Algorithm) were analysed to evaluate and determine its efficiency in terms of maximum intrusion detection rate and the highest true positive rate. …”
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  9. 9
    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems

    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

    Published 2022
    “…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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  10. 10
    A study on advanced statistical analysis for network anomaly detection

    A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

    Published 2005
    “…Algorithms for building detection models are usually classified into two categories: misuse detection and anomaly detection. …”
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  11. 11
    Fetal heart rate monitoring during pregnancy for assessing the well-being of the fetus

    Fetal heart rate monitoring during pregnancy for assessing the well-being of the fetus by Ibrahimy, Muhammad Ibn

    Published 2018
    “…The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. …”
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  12. 12
    Integrating genetic algorithms and fuzzy c-means for anomaly detection

    Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

    Published 2005
    “…Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. …”
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  13. 13
    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level

    Cloud Worm Detection and Response Technique By Integrating The Enhanced Genetic Algorithm An Threat Level by Hasan Mahmoud Sha'ban Kanaker

    Published 2024
    thesis::doctoral thesis
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  14. 14
    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus

    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus by Ibrahimy, Muhammad Ibn

    Published 2017
    “…The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. …”
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  15. 15
    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus

    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus by Ibrahimy, Muhammad Ibn

    Published 2018
    “…The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. …”
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  16. 16
    Facial features for template matching based face recognition

    Facial features for template matching based face recognition by Chai, Tong Yuen, Mohd Rizon, Mohamed Juhari, Prof. Dr., Woo, San San, Tan, Ching Seong, Dr.

    Published 2011
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  17. 17
    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus

    Fetal heart rate monitoring during pregnancy for assessing the well being of the fetus by Ibrahimy, Muhammad Ibn

    Published 2017
    “…The performance of the algorithm has been evaluated of the heart rates tracing processed from the abdominal signals. …”
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  18. 18
    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. …”
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  19. 19
    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition

    Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition by Nur Farahaina, Idris, Mohd Arfian, Ismail

    Published 2021
    “…This paper proposed the fuzzy-ID3 (FID3) algorithm, a fuzzy decision tree as the classification method in breast cancer detection. …”
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  20. 20
    Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan

    Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan by Diaa , Mohammed Hassan Uliyan

    Published 2016
    “…The average detection rate of our algorithm maintained 96 % true positive rate and 7 % false positive rate which outperform several current detection methods. …”
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