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

“…Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

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

“…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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Automated feature selection using boruta algorithm to detect mobile malware by Che Akmal, Che Yahaya, Ahmad Firdaus, Zainal Abidin, Salwana, Mohamad, Ernawan, Ferda, Mohd Faizal, Ab Razak

“…Boruta algorithm is used to select features automatically for assisting the machine learning. …”
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A new SMS spam detection method using both Content-Based and non Content-Based features by Sulaiman N.F., Jali M.Z.

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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.

“…Android (operating system); Android malware; Classification (of information); Feature Selection; Learning systems; Mobile security; Android apps; Classification models; Feature weight; Features selection; Machine learning algorithms; Machine-learning; Malware detection; Malwares; Memory usage; Selection techniques; Learning algorithms…”

Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

“…With irrelevant and redundant features learning algorithm builds detection model with less accuracy rate. …”
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Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms by Murti M.A., Junior R., Ahmed A.N., Elshafie A.

Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin by Mohd Shahirudin, Syamir

“…The objective of this project is to develop the Windows malware detection model using supervised machine learning in Decision Tree, K-NN and Naïve Bayes, to evaluate the performance of malware detection in term of testing and training of the features selection and to compare the accuracy detection model in all three machine learning algorithms. …”
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Security alert framework using dynamic tweet-based features for phishing detection on twitter by Liew, Seow Wooi

“…This model is then embedded into the detection algorithm together with the inclusion of dynamic tweet-based features which are not as part of the features used to train a classification model for phishing tweet detection. …”
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

“…The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. …”
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Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms by Masrom, S., Rahman, R.A., Mohamad, M., Rahman, A.S.A., Baharun, N.

“…The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). …”
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Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms by Masrom, S., Rahman, R.A., Mohamad, M., Rahman, A.S.A., Baharun, N.

“…The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). …”
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models.…”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Feature subset selection and classifier ensemble learning are familiar techniques with high ability to optimize above problems. …”
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A Machine Learning Classification Approach To Detect Tls-Based Malware Using Entropy-Based Flow Set Features by Keshkeh, Kinan

“…The difficulty and impracticality of decrypting TLS network traffic before it reaches the Intrusion Detection System (IDS) has driven numerous research studies to focus on anomaly-based malware detection without decryption employing various features and Machine Learning (ML) algorithms. …”
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K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm by Al-Hafiz, Ali Raheem, Jabir, Adnan J., Subramaniam, Shamala

“…Phishing detection is considered a critical problem in cybersecurity, and utilising machine learning with an efficient feature selection method for precisely identifying malicious websites is deemed the most critical challenge. …”
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Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

“…This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. …”
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