Search Results - (( botnet detection device algorithm ) OR ( based optimization isotherm algorithm ))*

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  1. 1
    Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

    Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis by Akash, Nazmus Sakib, Rouf, Shakir, Jahan, Sigma, Chowdhury, Amlan, Uddin, Jia

    Published 2022
    “…This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. …”
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  2. 2
    Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm

    Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm by Rahman, MZA, Madihah Mohd Saudi

    Published 2024
    “…Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques.…”
    Proceedings Paper
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  3. 3
    A Static Approach towards Mobile Botnet Detection

    A Static Approach towards Mobile Botnet Detection by Shahid, Anwar, Jasni, Mohamad Zain, Inayat, Zakira, Ul Haq, Riaz, Ahmad, Karim, Jaber, Aws Naser

    Published 2016
    “…Mobile botnet is one of the crucial threat to mobile devices. …”
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  4. 4
    Systematic analysis on mobile botnet detection techniques using genetic algorithm

    Systematic analysis on mobile botnet detection techniques using genetic algorithm by Rahman M.Z.A., Madihah Mohd Saudi

    Published 2024
    Conference Paper
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  5. 5
    An enhanced android botnet detection approach using feature refinement

    An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

    Published 2019
    “…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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  6. 6
    Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network

    Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network by Ahmed, Abdulghani Ali

    Published 2019
    “…The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. …”
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  7. 7
    A smart framework for mobile botnet detection using static analysis

    A smart framework for mobile botnet detection using static analysis by Anwar, Shahid, Mohamad Fadli, Zolkipli, Mezhuyev, Vitaliy, Inayat, Zakira

    Published 2020
    “…This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. …”
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  8. 8
    IoT-based botnet attacks systematic mapping study of literature

    IoT-based botnet attacks systematic mapping study of literature by Habiba Hamid, Rafidah Md Noor, Syaril Nizam Omar, Ismail Ahmedy, Shaik Shabana Anjum, Syed Adeel Ali Shah, Sheena Kaur, Fazidah Othman, Emran Mohd Tamil

    Published 2024
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  9. 9
    Intersection Features For Android Botnet Classification

    Intersection Features For Android Botnet Classification by Ismail, Najiahtul Syafiqah, Yusof, Robiah, Saad, Halizah, Abdollah, Mohd Faizal, Yusof, Robiah

    Published 2019
    “…This paper proposed an enhancement approach for Android botnet classification based on features selection and classification algorithms. …”
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  10. 10
    Android mobile malware detection model based on permission features using machine learning approach

    Android mobile malware detection model based on permission features using machine learning approach by Sharfah Ratibah, Tuan Mat

    Published 2022
    “…Different techniques have been adopted to detect and prevent the spread of Android malware, including anomaly, signature-based, and hybrid detection techniques. …”
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  11. 11
    A gauss-newton approach for nonlinear optimal control problem with model-reality differences

    A gauss-newton approach for nonlinear optimal control problem with model-reality differences by Sie, Long Kek, Jiao, Li, Leong, Wah June, Abd Aziz, Mohd Ismail

    Published 2017
    “…Here, the linear model-based optimal control model is considered, so as the optimal control law is constructed. …”
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  12. 12
    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Temitope T., Dele-Afolabi, Masoud, Ahmadipour, Mohamed Ariff, Azmah Hanim, A.A., Oyekanmi, M.N.M., Ansari, Sikiru, Surajudeen, Kumar, Niraj

    Published 2024
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  13. 13
    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer

    Performance assessment of Sn-based lead-free solder composite joints based on extreme learning machine model tuned by Aquila optimizer by Dele-Afolabi T.T., Ahmadipour M., Azmah Hanim M.A., Oyekanmi A.A., Ansari M.N.M., Sikiru S., Kumar N.

    Published 2025
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  14. 14
    Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption

    Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption by Zamani, Seyed Ali

    Published 2015
    “…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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  15. 15
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.

    Published 2023
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  16. 16
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan, T., Manan, T.S.B., Isa, M.H., Ghanim, A.A.J., Beddu, S., Jusoh, H., Iqbal, M.S., Ayele, G.T., Jami, M.S.

    Published 2020
    “…The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
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  17. 17
    Modelling and simulation of hollow profile aluminium extruded product

    Modelling and simulation of hollow profile aluminium extruded product by Sulaiman, Shamsuddin, Baharudin, B. T. Hang Tuah, Mohd Ariffin, Mohd Khairol Anuar, Magid, Hani Mizhir

    Published 2015
    “…This process is an isothermal process with an extrusion ratio of 3.3. Subsequently, the optimized algorithm for these extrusion parameters was suggested based on the simulation results. …”
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  18. 18
    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)

    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) by Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi

    Published 2020
    “…The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). …”
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  19. 19
    Design, optimization, fabrication and analysis of Cu microheater for loop-mediated isothermal amplification (LAMP) applications

    Design, optimization, fabrication and analysis of Cu microheater for loop-mediated isothermal amplification (LAMP) applications by Narayanamurthy, Vigneswaran, Samsuri, Fahmi, Jeroish, Z. E., Shankar, Abhishek Futane, Shanmugavel, Bhuvaneshwari Kumaravel

    Published 2023
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