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  1. 1
    SVM for network anomaly detection using ACO feature subset

    SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

    Published 2016
    “…Intrusion detection systems are developed on the bases of two different detection techniques, signature-based technique and anomaly-based technique. …”
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  2. 2
    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
    “…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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  3. 3
    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
    “…Further, the shortage of efficient feature selection techniques gives rise to low accuracy in anomaly detection. …”
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  4. 4
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…Recently, various techniques based on different algorithms have been developed. …”
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  5. 5
    Rao-SVM machine learning algorithm for intrusion detection system

    Rao-SVM machine learning algorithm for intrusion detection system by Abd, Shamis N., Alsajri, Mohammad, Ibraheem, Hind Raad

    Published 2020
    “…This article presents the development of an improved intrusion detection method for binary classification. In the proposed IDS, Rao Optimization Algorithm, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) were combined with NTLBO algorithm with supervised ML techniques (for feature subset selection (FSS). …”
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  6. 6
    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques by AHMAD, IFTIKHAR

    Published 2011
    “…Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. …”
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  7. 7
    Algorithm of face recognition by principal component analysis

    Algorithm of face recognition by principal component analysis by Al-Khateeb, Khalid A. Saeed, Yeop Johari, Jaiz Anuar

    Published 2002
    “…The method proved to be simple and effective. The simplified algorithm and techniques expected the process without seriously compromising the accuracy.…”
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  8. 8
    A Naïve-Bayes classifier for damage detection in engineering materials

    A Naïve-Bayes classifier for damage detection in engineering materials by Addin, O., Salit, Mohd Sapuan, Mahdi Ahmad Saad, Elsadig, Othman, Mohamed

    Published 2007
    “…The Naïve-Bayes classifier and the proposed feature subset selection algorithm have been shown as efficient techniques for damage detection in engineering materials. …”
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  9. 9
    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

    Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques by Iftikhar , Ahmad, Azween, Abdullah

    Published 2011
    “…Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. …”
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  10. 10
    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques

    Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

    Published 2011
    “…Traditional intrusion detection techniques have had trouble dealing with dynamic environments. …”
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  11. 11
    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification by Petwan, Montha

    Published 2023
    “…The ant colony optimisation (ACO) algorithm have been used to select feature subset. …”
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  12. 12
    Improved intrusion detection algorithm based on TLBO and GA algorithms

    Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

    Published 2021
    “…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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  13. 13
    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

    BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.

    Published 2023
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  14. 14
    A COLLABORATIVE FRAMEWORK FOR ANDROID MALWARE IDENTIFICATION USING DYNAMIC ANALYSIS

    A COLLABORATIVE FRAMEWORK FOR ANDROID MALWARE IDENTIFICATION USING DYNAMIC ANALYSIS by Thayaaleni, Rajandran

    Published 2019
    “…The project proposed a dynamic analysis technique in Android malware detection. The objectives of the project are to investigate the Android malware using dynamic analysis technique and to enhance the accuracy of malware detection. …”
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  15. 15
    Adaptive feature selection for denial of services (DoS) attack

    Adaptive feature selection for denial of services (DoS) attack by Yusof, Ahmad Riza'ain, Udzir, Nur Izura, Selamat, Ali, Hamdan, Hazlina, Abdullah @ Selimun, Mohd Taufik

    Published 2017
    “…In this paper, we propose combining two techniques in feature selection algorithm, namely consistency subset evaluation (CSE) and DDoS characteristic features (DCF) to identify and select the most important and relevant features related DDoS attacks. …”
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  16. 16
    A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

    A new hybrid ensemble feature selection framework for machine learning-based phishing detection system by Chiew, Kang Leng, Tan, Choon Lin, Wong, KokSheik, Yong, Kelvin S.C., Tiong, Wei King

    Published 2019
    “…In the first phase of HEFS, a novel Cumulative Distribution Function gradient (CDF-g) algorithm is exploited to produce primary feature subsets, which are then fed into a data perturbation ensemble to yield secondary feature subsets. …”
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  17. 17
    Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification

    Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification by Saw, Yee Ching

    Published 2018
    “…The proposed feature selection approach has a simple algorithmic framework and makes use of the existing feature selection techniques to cater different variety of data issues, namely Ensemble Filter-Embedded Feature Ranking Approach (FEFR). …”
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  18. 18
    Test cases reduction using similarity relation and conditional entropy

    Test cases reduction using similarity relation and conditional entropy by Md Nasir, Noor Fardzilawati

    Published 2017
    “…The experimental results show a test case reduction that is smaller in size without affecting the decision of the testing. The proposed technique was able to reduce up to 50% of the reduction rate compared to base-line techniques such as MFTS Algorithm, FLOWER, RZOLTAR and Weighted Greedy Algorithm.…”
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  19. 19
    The importance of data classification using machine learning methods in microarray data

    The importance of data classification using machine learning methods in microarray data by Jaber, Aws Naser, Moorthy, Kohbalan, Machap, Logenthiran, Safaai, Deris

    Published 2021
    “…These emerging techniques can be used in detecting cancer. The swarm intelligence technique can be combined with other statistical methods for attaining better results.…”
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  20. 20
    Hyperparameter tuned deep learning enabled intrusion detection on internet of everything environment

    Hyperparameter tuned deep learning enabled intrusion detection on internet of everything environment by Ahmed Hamza, Manar, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, S. Alotaibi, Saud, Mahgoub, Hany, S. Mehanna, Amal, Motwakel, Abdelwahed

    Published 2022
    “…In addition, Chaotic Local Search Whale Optimization Algorithm-based Feature Selection (CLSWOA-FS) technique is employed to choose the optimal feature subsets. …”
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