Yue Guan
ABSTRACT
We propose a hybrid framework of machine learning and deep learning networks for efficient classification of attacks and anomalies. The machine learning algorithms are adopted to distinguish between normal data and anomaly data. The deep networks, on the other hand, are used to perform anomaly type classification. The framework is optimally tuned by selecting the most efficient hyperparameter values. These values are selected experimentally for the proposed deep network for optimal and efficient training of the network. We further propose the use of the Synthetic Minority Oversampling Technique (SMOTE) to address the data imbalance problem and Particle swarm optimization (PSO) as a feature selection mechanism to improve accuracy as well as execution time.
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Cross Ref
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- Saad Gadal, Rania Mokhtar, Maha Abdelhaq, Raed Alsaqour, Elmustafa Sayed Ali, and Rashid Saeed. 2022. Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization. Electronics 11, 14 (2022), 2158.Google ScholarCross Ref
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