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

“…An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. …”
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A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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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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A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network by Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Abdullah F., Ahmad W.S.H.M.W.

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Performances of machine learning algorithms for binary classification of network anomaly detection system by Nawir, M., Amir, A., Lynn, O.B., Yaakob, N., Ahmad, R.B.

“…Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. …”
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Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: a systematic review by Musri, Nabila, Christie, Brenda, Ichwan, Solachuddin Jauhari Arief, Cahyanto, Arief

“…Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. …”
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

“…In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. …”
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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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Early detection of dengue disease using extreme learning machine by Suhaeri, Suhaeri, Mohd Nawi, Nazri, Fathurahman, Muhamad

“…The availability of nowadays clinical data of Dengue disease can be used to train machine learning algorithm in order to automaticaly detect the present of Dengue disease of the patients. …”
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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System by Ali, Mohammed Hasan, Mohamed Fadli, Zolkipli

“…As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. …”
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Dyslexia handwriting detection using Convolutional Neural Network (CNN) algorithm / Sofea Najihah Mohd Zaki by Mohd Zaki, Sofea Najihah

“…This project attempts to accurately detect the type of dyslexic handwriting. Convolutional Neural Network (CNN) algorithm was chosen as one of the possible solutions after a thorough analysis of many algorithms for dyslexic handwriting identification. …”
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Comparison of malware detection model using supervised machine learning algorithms / Syamir Mohd Shahirudin by Mohd Shahirudin, Syamir

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A review on object detection algorithms based deep learning methods / Wan Xing ... [et al.] by Wan Xing, Sultan Mohd, Mohd Rizman, Johari, Juliana, Ahmat Ruslan, Fazlina

“…Deep learning-based object detection algorithms can be categorized into three main types: end-to-end algorithms, two-stage algorithms, and one-stage algorithms. …”
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Feature selection in intrusion detection, state of the art: A review by Rais, H.M., Mehmood, T.

“…These input features give information to the learning algorithms which used in intrusion detection system in the form of the detection method. …”
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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

“…Then, the percentage of detection accuracy was used to compare the detection performance of all five algorithms. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms by Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah

“…As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. …”
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Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm by Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi

“…This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…However, these algorithms often fall short in consistently detecting and classifying network intrusions, particularly when distinctions between classes are subtle or when facing evolving attack patterns. …”
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Anomaly detection in ICS datasets with machine learning algorithms by Mubarak, Sinil, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Abdul Rahman, Farah Diyana, Tahir, Mohammad

“…The activity traffic between ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed.…”
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