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  1. 1
    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
    “…For example,the spammer changes patterns of message for making spam such as writing the message by JavaScript, using different advertising images and words to form features or attributes. …”
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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
    “…Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. …”
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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. One of the main steps after the data collection stage of any method is selecting a subset of the features to be used for the feature selection process. …”
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  4. 4
    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
    “…The selected feature subset using ant system is then validated using support vector machine. …”
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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
    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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  7. 7
    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
    “…We used data set from 1999 KDD intrusion detection contest.…”
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  8. 8
    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
    “…A database of about 400 facial images was used to test the algorithm. Each image is represented by a matrix (112x 92). …”
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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 AHMAD, IFTIKHAR

    Published 2011
    “…Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. …”
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  10. 10
    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
    “…Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. …”
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  11. 11
    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification

    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fajila, Fathima, Yusof, Yuhanis

    Published 2024
    “…In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. …”
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  12. 12
    GA-based feature subset selection in a spam/non-spam detection system

    GA-based feature subset selection in a spam/non-spam detection system by Behjat, Amir Rajabi, Mustapha, Aida, Nezamabadi-pour, Hossein, Sulaiman, Md. Nasir, Mustapha, Norwati

    Published 2012
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  13. 13
    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
    “…Optimization algorithms are widely used for the identification of intrusion. …”
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  14. 14
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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  15. 15
    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
    “…The second phase derives a set of baseline features from the secondary feature subsets by using a function perturbation ensemble. …”
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  16. 16
    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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  17. 17
    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

    Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection by Tubishat, Mohammad, Idris, Norisma, Shuib, Liyana, Abushariah, Mohammad A.M., Mirjalili, Seyedali

    Published 2020
    “…The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. …”
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  18. 18
    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
    “…The proposed algorithm employs a combined model that uses two different measures (nonconformity metric measures and Local Distance-based Outlier Factor (LDOF)) to improve its detection ability. …”
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  19. 19
    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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    Final Year Project Report / IMRAD
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  20. 20
    Attacks detection in 6G wireless networks using machine learning

    Attacks detection in 6G wireless networks using machine learning by Saeed, Mamoon M., Saeed, Rashid A., Gaid, Abdulguddoos S. A., Mokhtar, Rania A., Khalifa, Othman Omran, Ahmed, Zeinab E.

    Published 2023
    “…Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. …”
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    Proceeding Paper
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