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1
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. 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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Thesis -
2
Machine learning algorithms in context of intrusion detection
Published 2016“…These machine learning algorithms develop a detection model in a training phase. …”
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3
Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
Published 2021“…For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% & 41.58% of detection rate, respectively. …”
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4
Comparison of supervised machine learning algorithms for malware detection / Mohd Faris Mohd Fuzi ... [et al.]
Published 2023“…Then, the percentage of detection accuracy was used to compare the detection performance of all five algorithms. …”
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5
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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6
Designing a new model for Trojan horse detection using sequential minimal optimization
Published 2024Subjects: “…Malwares Trojan horse Detection Automated analysis Sequential minimal optimization (SMO) True positive rate False positive rate Machine learning…”
Conference Paper -
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Designing a New Model for Trojan Horse Detection Using Sequential Minimal Optimization
Published 2024“…Based on the experiment conducted, the Sequential Minimal Optimization (SMO) algorithm has outperformed other machine learning algorithms with 98.2 % of true positive rate and with 1.7 % of false positive rate.…”
Proceedings Paper -
8
Polymorphic malware detection based on dynamic analysis and supervised machine learning / Nur Syuhada Selamat
Published 2021“…The benefit of this work indicated that the implementation of a feature selection technique plays an important role in machine learning algorithms to increase the performance of detection.…”
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9
Anomaly detection in ICS datasets with machine learning algorithms
Published 2021“…The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. …”
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10
Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques
Published 2011“…A series of experimental results demonstrate that the proposed intrusion detection model can effectively detect anomalies with low false positive rate, high detection rate and achieve high detection accuracy.…”
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11
Intruder detection in camera networks using the one-class neighbor machine
Published 2011“…We propose a new algorithm based on machine learning techniques for automatic intruder detection in surveillance networks. …”
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Proceeding Paper -
12
Performance evaluation for different intrusion detection system algorithms using machine learning
Published 2018“…The objectives of this project is to evaluate the performance of various intrusion detection algorithms based on machine learning. The algorithms considered are the Naive Bays Algorithm, Decision Tree Algorithm and Hybrid Algorithm for different datasets. …”
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13
Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
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14
Performance evaluation of real-time multiprocessor scheduling algorithms
Published 2016“…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
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15
Target heart rate zone detector during exercise based on real-time facial expression using single shot detection algorithm / Muhammad Azziq Shamsudin, Raihah Aminuddin and Ummu Mar...
Published 2022“…Their facial expression is measure and determine based on Rating of Perceived Exertion (RPE) scale. The object detection machine learning model used in this project is Single Shot Detector (SSD) algorithm. …”
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Book Section -
16
Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System
Published 2020“…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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17
Early detection of dengue disease using extreme learning machine
Published 2018“…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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SVM for network anomaly detection using ACO feature subset
Published 2016“…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. This paper proposes a detection model, ant system with support vector machine, which uses ant system, a variation of ant colony optimization, to filter out the redundant and irrelevant features for support vector machine classification algorithm. …”
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19
A Cryptojacking Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
Published 2023“…Aside from that, the objective of this project is to investigate cryptojacking in cryptocurrency users' devices, develop a machine learning model to detect cryptojacking, and evaluate the machine learning model's accuracy, true positive rate (TPR), false positive rate (FPR), and precision in detecting cryptojacking. …”
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Undergraduates Project Papers -
20
Improving intrusion detection using genetic algorithm
Published 2013“…To overcome this problem, a genetic algorithm approach is proposed. Genetic Algorithm (GA) is most frequently employed as a robust technology based on machine learning for designing IDS. …”
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