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

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

“…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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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

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

“…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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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

“…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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Parallel distributed genetic algorithm development based on microcontrollers framework by Krishnan P.S., Kiong T.S., Koh J.

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

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

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

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

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

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

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

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

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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

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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

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

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

“…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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Machine learning-based enhanced deep packet inspection for IP packet priority classification with differentiated services code point for advance network management by Khan, Fazeel Ahmed, Abubakar, Adamu

“…This study presents an approach to enhance intelligent packet forwarding priority classification on Differentiated Services Code Point (DSCP), leveraging classifiers from machine learning algorithms for Deep Packet Inspection (DPI). …”
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XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection by Bouke, Mohamed Aly, Alramli, Omar Imhemed, Abdullah, Azizol

“…However, these algorithms often suffer from the "black box" dilemma, a lack of transparency that hinders their applicability in security contexts where understanding the reasoning behind classifications is essential for effective risk assessment and mitigation strategies. …”
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