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Systematic review of intrusion detection system based on machine learning techniques for internet of things by Idris, Muhammad, Md Yasin, Sharifah, Audi, Giade Hamza

“…We highlighted the most efficient machine learning algorithms and feature selection techniques, along with strategies to address issues related to dataset imbalance in benchmarked datasets, with the goal of fostering the development of robust IDS solutions for IoT security.…”
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Detection of DDoS attacks in IoT networks using machine learning algorithms by Abdulrahman Alwan, Alwan Ahmed, Shah, Asadullah, Abdulrahman Alwan, Alwan Abdullah, Laghari, Shams Ul Arfeen

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Determination of suitable resource discovery tool and methodology for high-volume internet of things (IoT) by Jamal, A.A., Bakar, M.T.A.

“…As the chosen model to be implemented in this analysis, the Q-learning algorithm proposal offers a possible solution for addressing evolving IoT environments and configurations. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by SALH, ADEB, NGAH, RAZALI, Audah, Lukman, SOON KIM, KWANG, ALJALOUD, KHALED A., TALIB, HAIRUL NIZAM

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Development of IoT-based automated dynamic emergency response system against fire incidents in academic building by Al-Hady, Syed Mohammed Zakaria, Islam, Md Rafiqul, Rashid, Muhammad Mahbubur

“…The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. …”
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Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G by Ngah, Razali, Salh, Adeeb, Audah, Lukman, Abdullah, Qazwan, Kim, Kwang Soon, Al-Moliki, Yahya Mohammed Hameed, AlJaloud, Khaled A., Talib, Md Hairul Nizam

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by SALH, ADEB, NGAH, RAZALI, AUDAH, LUKMAN, KWANG SOON KIM, KWANG SOON KIM, ABDULLAH, QAZWAN, M. AL-MOLIKI, YAHYA, A. ALJALOUD, KHALED, TALIB, HAIRUL NIZAM

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Preliminary analysis of malware detection in opcode sequences within IoT environment by Ahmed, Firas Shihab, Mustapha, Norwati, Mustapha, Aida, Kakavand, Mohsen, Mohd Foozy, Cik Feresa

“…Three machine learning algorithms are evaluated and compared for accuracy, precision, recall and F-measure. …”
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Internet of Things (IoT) intrusion detection by Machine Learning (ML): a review by Dehkordi, Iman Farhadian, Manochehri, Kooroush, Aghazarian, Vahe

“…The goal of this study is to show the results of analyzing various classification algorithms in terms of confusion matrix, accuracy, precision, specificity, sensitivity, and f-score to Develop an Intrusion Detection System (IDS) model.…”
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Real-time and predictive analytics of air quality with IoT system: A review by Nurmadiha, Osman, Mohd Faizal, Jamlos, Fatimah, Dzaharudin, Aidil Redza, Khan, You, Kok Yeow, Khairil Anuar, Khairi

“…(iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can as-sist in the development of real-time, and continuous high precision environmen-tal monitoring systems. v) Machine Learning (ML) Regression algorithm is suit-able for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting.…”
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Home intruder detection system using machine learning and IoT by Sahlan, Fadhluddin, Feizal, Faeez Zimam, Mansor, Hafizah

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IoT-Enabled Waste Tracking and Recycling Optimization : Enhancing Sustainable Waste Management by Eugine Teh, Yin Jie, Chee Soon, Chong, Rozaimi, Ghazali, Hazriq Izzuan, Jaafar, Muhamad Fadli, Ghani, Howe Cheng, Teng, Nur Farhanah, Zulkipli, Siaw Hong, Liew

“…The methodology integrates IoT technology and machine learning to tackle waste classification and collection inefficiencies. …”
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Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques by Muhammad Afnan, Mohammad Nasir

“…An effective machine learning approach created from a separate examination of many machine learning algorithms in WEKA should be applied for the correct identification of heart disease. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by ADEB SALH, ADEB SALH, RAZALI NGAH, RAZALI NGAH, LUKMAN AUDAH, LUKMAN AUDAH, KWANG SOON KIM, KWANG SOON KIM, QAZWAN ABDULLAH, QAZWAN ABDULLAH, YAHYA M. AL-MOLIKI, YAHYA M. AL-MOLIKI, KHALED A. ALJALOUD, KHALED A. ALJALOUD, HAIRUL NIZAM TALIB, HAIRUL NIZAM TALIB

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by ADEB SALH, ADEB SALH, RAZALI NGAH, RAZALI NGAH, LUKMAN AUDAH, LUKMAN AUDAH, QAZWAN ABDULLAH, QAZWAN ABDULLAH, YAHYA M. AL-MOLIK, YAHYA M. AL-MOLIK, KHALED A. ALJALOUD, KHALED A. ALJALOUD, HAIRUL NIZAM TALIB, HAIRUL NIZAM TALIB

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by ADEB SALH, ADEB SALH, RAZALI NGAH, RAZALI NGAH, LUKMAN AUDAH, LUKMAN AUDAH, KWANG SOON KIM, KWANG SOON KIM, QAZWAN ABDULLAH, QAZWAN ABDULLAH, YAHYA M. AL-MOLIKI, YAHYA M. AL-MOLIKI, KHALED A. ALJALOUD, KHALED A. ALJALOUD, HAIRUL NIZAM TALIB, HAIRUL NIZAM TALIB

“…Federatedm Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G by ADEB SALH, ADEB SALH, RAZALI NGAH, RAZALI NGAH, LUKMAN AUDAH, LUKMAN AUDAH, KWANG SOON KIM, KWANG SOON KIM, QAZWAN ABDULLAH, QAZWAN ABDULLAH, YAHYA M. AL-MOLIKI, YAHYA M. AL-MOLIKI, KHALED A. ALJALOUD, KHALED A. ALJALOUD, HAIRUL NIZAM TALIB, HAIRUL NIZAM TALIB

“…Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. …”
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Trust-aware authentication and authorization for IoT: a federated machine learning approach by Ahmed, Kazi Istiaque, Tahir, Mohammad, Lau, Sian Lun, Habaebi, Mohamed Hadi, Ahad, Abdul, Mughees, Amna

“…The proposed solution for AA IoT utilizes a trust calculation algorithm based on Federated Learning (FL), which is suitable for IoT environments and enables data privacy and scalability. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Saleh Ahmed,, Ali Mohammed, Ali Majeed Alhammadi, Nafea, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. …”
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Deep learning in distributed denial-ofservice attacks detection method for Internet of Things networks by Mohammed Aswad, Firas, Ahmed, Ali Mohammed Saleh, Majeed Alhammadi, Nafea Ali, Ahmad Khalaf, Bashar, A. Mostafa, Salama

“…Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. …”
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