A novel ensemble method for enhancing Internet of Things device security against botnet attacks
Tài liệu tham khảo
Debicha, 2023, Adv-bot: Realistic adversarial botnet attacks against network intrusion detection systems, Comput. Secur., 10.1016/j.cose.2023.103176
Gul, 2021, A consolidated review of path planning and optimization techniques: Technical perspectives and future directions, Electronics, 10, 2250, 10.3390/electronics10182250
Abualigah, 2023, Swarm intelligence to face IoT challenges, Comput. Intell. Neurosci., 2023, 10.1155/2023/4254194
Kumari, 2023, A comprehensive study of ddos attacks over IoT network and their countermeasures, Comput. Secur., 10.1016/j.cose.2023.103096
Fang, 2023, Security evaluation method of distance education network nodes based on machine learning, 281
He, 2023, Adversarial machine learning for network intrusion detection systems: A comprehensive survey, IEEE Commun. Surv. Tutor., 10.1109/COMST.2022.3233793
Raza, 2022, Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, PLoS One, 17, 10.1371/journal.pone.0276525
Raza, 2022, Predicting employee attrition using machine learning approaches, Appl. Sci., 12, 10.3390/app12136424
Ibrahim, 2021, Multilayer framework for botnet detection using machine learning algorithms, IEEE Access, 9, 48753, 10.1109/ACCESS.2021.3060778
Dollah, 2018, Machine learning for HTTP botnet detection using classifier algorithms, J. Telecommun. Electron. Comput. Eng. (JTEC), 10, 27
Lee, 2021, Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning, PeerJ Comput. Sci., 7, 10.7717/peerj-cs.350
Khan, 2019, An adaptive multi-layer botnet detection technique using machine learning classifiers, Appl. Sci., 9, 10.3390/app9112375
Alkahtani, 2021, Botnet attack detection by using CNN-LSTM model for Internet of Things applications, Secur. Commun. Netw., 2021, 1, 10.1155/2021/3806459
Alissa, 2022, Botnet attack detection in IoT using machine learning, Comput. Intell. Neurosci., 2022, 10.1155/2022/4515642
Fadhilla, 2022, Lightweight meta-learning BotNet attack detection, IEEE Internet Things J., 1
Habibi, 2023, Imbalanced tabular data modelization using CTGAN and machine learning to improve IoT botnet attacks detection, Eng. Appl. Artif. Intell., 118, 10.1016/j.engappai.2022.105669
Rustam, 2023, Deep ensemble-based efficient framework for network attack detection, 1
Bojarajulu, 2023, Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model, Comput. Secur., 126, 10.1016/j.cose.2022.103064
García, 2014, An empirical comparison of botnet detection methods, Comput. Secur., 45, 100, 10.1016/j.cose.2014.05.011
Lucky, 2020, A lightweight decision-tree algorithm for detecting ddos flooding attacks, 382
Gandomi, 2022, Machine learning technologies for big data analytics, Electronics, 11, 421, 10.3390/electronics11030421
Kotsiantis, 2013, Decision trees: a recent overview, Artif. Intell. Rev., 39, 261, 10.1007/s10462-011-9272-4
Karthik, 2021, Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks, J. Ambient Intell. Humaniz. Comput., 1
Al-Manaseer, 2022, A novel big data classification technique for healthcare application using support vector machine, random forest and J48, 205
Speiser, 2019, A comparison of random forest variable selection methods for classification prediction modeling, Expert Syst. Appl., 134, 93, 10.1016/j.eswa.2019.05.028
Khoei, 2021, Boosting-based models with tree-structured parzen estimator optimization to detect intrusion attacks on smart grid, 0165
Alaiad, 2023, Predicting the severity of COVID-19 from lung CT images using novel deep learning, J. Med. Biol. Eng., 1
Heng, 2016, Research and application based on adaptive boosting strategy and modified CGFPA algorithm: a case study for wind speed forecasting, Sustainability, 8, 235, 10.3390/su8030235
Raza, 2023, Predicting genetic disorder and types of disorder using chain classifier approach, Genes, 14, 71, 10.3390/genes14010071
Abualigah, 2022
Bentéjac, 2021, A comparative analysis of gradient boosting algorithms, Artif. Intell. Rev., 54, 1937, 10.1007/s10462-020-09896-5
Besharati, 2019, LR-HIDS: logistic regression host-based intrusion detection system for cloud environments, J. Ambient Intell. Humaniz. Comput., 10, 3669, 10.1007/s12652-018-1093-8
Jamei, 2022, Estimating the density of hybrid nanofluids for thermal energy application: Application of non-parametric and evolutionary polynomial regression data-intelligent techniques, Measurement, 189, 10.1016/j.measurement.2021.110524
Peng, 2002, An introduction to logistic regression analysis and reporting, J. Educ. Comput. Res., 96, 3
Ahmed, 2019, Cyber physical security analytics for anomalies in transmission protection systems, IEEE Trans. Ind. Appl., 55, 6313, 10.1109/TIA.2019.2928500
Peng, 2020, Discriminative ridge machine: A classifier for high-dimensional data or imbalanced data, IEEE Trans. Neural Netw. Learn. Syst., 32, 2595, 10.1109/TNNLS.2020.3006877
Peppes, 2021, Performance of machine learning-based multi-model voting ensemble methods for network threat detection in agriculture 4.0, Sensors, 21, 7475, 10.3390/s21227475
Bottou, 2012, Stochastic gradient descent tricks, 421
Adeniji, 2022, Development of DDoS attack detection approach in software defined network using support vector machine classifier, 319
Lau, 2003, Online training of support vector classifier, Pattern Recognit., 36, 1913, 10.1016/S0031-3203(03)00038-4
Moorthy, 2020, Optimal detection of phising attack using SCA based K-NN, Procedia Comput. Sci., 171, 1716, 10.1016/j.procs.2020.04.184
Liao, 2002, Use of k-nearest neighbor classifier for intrusion detection, Comput. Secur., 21, 439, 10.1016/S0167-4048(02)00514-X
Jahromi, 2017, A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features, 209
Islam, 2022, GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus, Veh. Commun., 33
Raza, 2022, A novel approach to classify telescopic sensors data using bidirectional-gated recurrent neural networks, Appl. Sci., 12, 10268, 10.3390/app122010268
Kunang, 2021, Attack classification of an intrusion detection system using deep learning and hyperparameter optimization, J. Inf. Secur. Appl., 58
Raza, 2023, Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier, PLoS One, 18, 10.1371/journal.pone.0284522
Yadav, 2020, BotEye: Botnet detection technique via traffic flow analysis using machine learning classifiers, 154
Chen, 2017, An effective conversation-based botnet detection method, Math. Probl. Eng., 2017
Amini, 2019, Analysis of network traffic flows for centralized botnet detection, J. Telecommun. Electron. Comput. Eng. (JTEC), 11, 7
Jagadeesan, 2021, An efficient botnet detection with the enhanced support vector neural network, Measurement, 176, 10.1016/j.measurement.2021.109140
Safitri, 2022, Analyzing machine learning-based feature selection for botnet detection, 386
Moorthy, 2023, Botnet detection using artificial intelligence, Procedia Comput. Sci., 218, 1405, 10.1016/j.procs.2023.01.119
Gong, 2022, A mechine learning approach for botnet detection using lightgbm, 829
Ahmed, 2023, Effective and efficient DDoS attack detection using deep learning algorithm, multi-layer perceptron, Future Internet, 15, 10.3390/fi15020076