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  1. 1
    Features selection for intrusion detection system using hybridize PSO-SVM

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

    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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  2. 2
    An enhancement of classification technique based on rough set theory for intrusion detection system application

    An enhancement of classification technique based on rough set theory for intrusion detection system application by Noor Suhana, Sulaiman

    Published 2019
    “…Experimental results show the proposed technique increases accuracy classification percentage up to 99.95%; and the minimum number of bins determine good discretization algorithm. Consequently, attack detection rate increases and false positive alarm rate minimizes. …”
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  3. 3
    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling

    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

    Published 2011
    “…The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART.…”
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  4. 4
    Effective mining on large databases for intrusion detection

    Effective mining on large databases for intrusion detection by Adinehnia, Reza, Udzir, Nur Izura, Affendey, Lilly Suriani, Ishak, Iskandar, Mohd Hanapi, Zurina

    Published 2014
    “…Results show that higher detection rate is achieved when using apriori algorithm on the proposed dataset. …”
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  5. 5
    Designing a new model to detect Trojan Horse based on knowledge discovery and data mining

    Designing a new model to detect Trojan Horse based on knowledge discovery and data mining by Areej Mustafa Khlaif Abuzaid

    Published 2024
    thesis::master thesis
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  6. 6
    Experimenting the dendrite cell algorithm for disease outbreak detection model

    Experimenting the dendrite cell algorithm for disease outbreak detection model by Mohamad Mohsin, Mohamad Farhan, Hamdan, Abdul Razak, Abu Bakar, Azuraliza

    Published 2014
    “…The characteristics of early outbreak signal which are weak and behaved under uncertainties has brought to the development of outbreak detection model based on dendrite cell algorithm.Although the algorithm is proven can improve detection performance, it relies on several parameters which need to be defined before mining.In this study, the most appropriate parameter setting for outbreak detection using dendrite cell algorithm is examined.The experiment includes four parameters; the number of cell cycle update, the number of dendrite cell allowed to be in population, weight, and migration threshold value. …”
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  7. 7
    Improving intrusion detection using genetic algorithm

    Improving intrusion detection using genetic algorithm by Hashemi, V. Moraveji, Muda, Zaiton, Yassin, Warusia

    Published 2013
    “…The validity of this approach is verified using Knowledge Discovery and Data Mining Cup 1999 (KDD Cup ’99) dataset. The experimental results demonstrate that the proposed approach outperforms the existing techniques, with the detection rate of attack and false alarm rates of 95.7265 and 4.2735, respectively. …”
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  8. 8
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…The simulation is implemented with iFogSim and java programming language. …”
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  9. 9
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…An efficient IDS uses computational methods as techniques of machine learning (ML) to enhance the rates of detection to obtain the lowest false positive rate, although such rates tend to be reduced by the big amount of irrelevant features as an optimization issue. …”
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  10. 10
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  11. 11
    A simultaneous spam and phishing attack detection framework for short message service based on text mining approach

    A simultaneous spam and phishing attack detection framework for short message service based on text mining approach by Mohd Foozy, Cik Feresa

    Published 2017
    “…Additionally, the proposed framework also can detect the attack simultaneously using text mining approaches.…”
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  12. 12
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  13. 13
    Improved Malware detection model with Apriori Association rule and particle swarm optimization

    Improved Malware detection model with Apriori Association rule and particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2019
    “…In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. …”
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  14. 14
    Unsupervised Anomaly Detection with Unlabeled Data Using Clustering

    Unsupervised Anomaly Detection with Unlabeled Data Using Clustering by Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor

    Published 2005
    “…We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. …”
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  15. 15
    An Improved Artificial Dendrite Cell Algorithm for Abnormal Signal Detection

    An Improved Artificial Dendrite Cell Algorithm for Abnormal Signal Detection by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak, Abdul Wahab, Mohd Helmy

    Published 2018
    “…From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.…”
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  16. 16
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  17. 17
    An improved hybrid learning approach for better anomaly detection

    An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

    Published 2011
    “…Therefore, anomaly detection is often associated with high false alarm with only moderate accuracy of detection rates. …”
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  18. 18
    Development and implementation of a technique for norms-adaptable agents in open multi-agent communities

    Development and implementation of a technique for norms-adaptable agents in open multi-agent communities by Mahmoud M., Ahmad M.S., Mohd Yusoff M.Z.

    Published 2023
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  19. 19
    An adaptive anomaly threshold in artificial dendrite cell algorithm

    An adaptive anomaly threshold in artificial dendrite cell algorithm by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak

    Published 2017
    “…The dendrite cell algorithm (DCA) relies on the multi-context antigen value (MCAV) to determine the abnormality of a record by comparing it with anomaly threshold.In practice, the threshold is pre-determined before mining based on previous information and the existing MCAV is inefficient when expose to extreme values.This causes the DCA fails to detect unlabeled data if the new pattern distinct from previous information and reduces the detection accuracy.This paper proposed an adaptive anomaly threshold for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability.In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV.From the experiments towards 12 datasets, the new version of DCA generated a better detection result than its previous version in term of sensitivity, specificity, false detection rate, and accuracy.…”
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  20. 20
    An improved artificial dendrite cell algorithm for abnormal signal detection

    An improved artificial dendrite cell algorithm for abnormal signal detection by Mohamad Mohsin, Mohamad Farhan, Abu Bakar, Azuraliza, Hamdan, Abdul Razak, Abdul Wahab, Mohd Helmy

    Published 2018
    “…From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.…”
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