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  1. 1
    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

    Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions by Gorment N.Z., Selamat A., Cheng L.K., Krejcar O.

    Published 2024
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  2. 2
    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin

    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin by Mohd Nordin, Ahmad Nasreen Aqmal

    Published 2024
    “…The key results encompass dataset preprocessing, Decision Tree classification model training, user interface development, and the evaluation of the Decision Tree model's performance. …”
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  3. 3
    An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.]

    An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset / Sarthak Rastogi ... [et al.] by Rastogi, Sarthak, Shrotriya, Archit, Singh, Mitul Kumar, Potukuchi, Raghu Vamsi

    Published 2022
    “…The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. …”
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  4. 4
    Malware Classification Using Ensemble Classifiers

    Malware Classification Using Ensemble Classifiers by Mohd Hanafi Ahmad Hijazi, Tan Choon Beng, Lim, Yuto, Kashif Nisar, James Mountstephen

    Published 2018
    “…Algorithms and classifiers such as k-Nearest Neighbor, Artificial Neural Network, Support Vector Machine, Naïve Bayes, and Decision Tree had shown their effectiveness towards malware classification in various recent researches. …”
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  5. 5
    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]

    Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.] by Mohd Fuzi, Mohd Faris, Mohd Shahirudin, Syamir, Abd Halim, Iman Hazwam, Jamaluddin, Muhammad Nabil Fikri

    Published 2023
    “…The results indicated that the Decision Tree and Random Forest algorithms provided the best detection accuracy at 96%, followed by the K-NN algorithm at 95%. …”
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  6. 6
    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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  7. 7
    A new mobile botnet classification based on permission and API calls

    A new mobile botnet classification based on permission and API calls by Yusof M., Madihah Mohd Saudi, Farida Hazwani Mohd Ridzuan

    Published 2024
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  8. 8
    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures

    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures by Hassan, Nor Hafeizah

    Published 2018
    “…Formal-based classification framework with notation may increase accuracy and vi-sualization compared with hierarchy-tree only,but the conclusion remains tentative because of methodological limitation in the studies.…”
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  9. 9
    Sentiment Analysis of Cyberbullying in Social Media Using Decision Trees

    Sentiment Analysis of Cyberbullying in Social Media Using Decision Trees by Sulaiman, Aliza Sharina

    Published 2020
    “…This has also risen the possibilities and advancement of security threats. This study uses data mining techniques from the views of users written ion Twitter to use in sentiment analysis of decision tree classification. …”
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  10. 10
    Prediction of college student academic performance using data mining techniques.

    Prediction of college student academic performance using data mining techniques. by Abd Jalil, Azura, Mustapha, Aida, Santa, Dzulizah, Zain, Nurul Zaiha, Radwan, Rizalina

    Published 2013
    “…The classification algorithms used are the Decision Tree, Naïve Bayesian, and Multilayer Perception with the highest classification accuracy by the Naive Bayes algorithm with accuracy of 95.3%. …”
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  11. 11
    Secure Access To Authorized Resources Based On Fingerprint Authentication

    Secure Access To Authorized Resources Based On Fingerprint Authentication by Elmadani, Ahmed Baba

    Published 2003
    “…The database can be manipulated (insertion, retrieving, and deletion) rapidly using the Adelson Velskii and Landis (AVL) tree searching technique. The A VL tree is used to increase the compression ratio as its compression algorithm works efficiently for all types of data. …”
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  12. 12
    Predicting Customer Behaviour on Buying Life Insurance using Machine Learning

    Predicting Customer Behaviour on Buying Life Insurance using Machine Learning by Fabu Ayub A Ghafar, Norhana Abd Rahim, Yumn Suhaylah Yusoff

    Published 2026
    journal::journal article
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  13. 13
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…References and theories to support the research of 'RSA Encryption/Decryption using Java' have been disclosed in Literature Review section. …”
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  14. 14
    Named entity recognition using a new fuzzy support vector machine.

    Named entity recognition using a new fuzzy support vector machine. by Mansouri, Alireza, Affendy, Lilly Suriani, Mamat, Ali

    Published 2008
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  15. 15
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
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  16. 16
    Provider independent cryptographic tools

    Provider independent cryptographic tools by Ibrahim, Subariah, Salleh, Mazleena, Abdul Aziz, Shah Rizan

    Published 2003
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  17. 17
    Pixel-based feature for android malware family classification using machine learning algorithms

    Pixel-based feature for android malware family classification using machine learning algorithms by Mohd Zamri, Osman, Rahiwan Nazar, Romli, Ahmad Firdaus, Zainal Abidin, Mohd Faaizie, Darmawan

    Published 2021
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  18. 18
    A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection

    A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection by Jia, Lu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

    Published 2022
    “…Meanwhile, for improving the efciency of training and predicting, Pearson Correlation analysis, standard deviation, and a new adaptive K-means are used to select attributes and make fuzzy interval decisions. The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. …”
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  19. 19
    Edge assisted crime prediction and evaluation framework for machine learning algorithms

    Edge assisted crime prediction and evaluation framework for machine learning algorithms by Adhikary, Apurba, Murad, Saydul Akbar, Munir, Md Shirajum, Choong Seon, Hong Seong

    Published 2022
    “…A maximum accuracy of 81% is obtained for Decision Tree algorithm during the prediction of crime. The findings demonstrate that employing Machine Learning techniques aids in the prediction of criminal events, which has aided in the enhancement of public security.…”
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  20. 20
    Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection

    Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection by Jia, Liu

    Published 2024
    “…To address this problem, this study first uses CART (Classification and Regression Tree) to enhance the depth of ANFIS, providing a deeper and interpretable hybrid architecture. …”
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