WebFeb 18, 2024 · Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning WebJul 29, 2024 · The DSSTE algorithm employs both Edited Nearest Neighbor (ENN) and K-Means clustering algorithms to reduce the data set’s majority class for improving the classifier’s training stage consequently enhances performance. The results show, using two hidden layers LSTM-NN provides best performance and time.
Network intrusion detection system: A systematic study of …
WebThe DSSTE algorithm employs both Edited Nearest Neighbor (ENN) and K-Means clustering algorithms to reduce the data set’s majority class for improving the classifier’s training stage consequently enhances performance. The results show, using two hidden layers LSTM-NN provides best performance and time. WebTo monitor this imbalanced traffic network DSSTE algorithm has been proposed to tackle this problem. This method slightly reduces the problem and increase the sampling rate more effectively. In our proposed approach, we are using machine and deep knowing technique to check the data, and the contributions are as follows: 1. maggie moo\u0027s ice cream
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WebIEEE Xplore Full-Text PDF: WebDSSTE ALGORITHM In imbalanced network traffic, different traffic data types have similar rep resentations, especially minority attacks can hide among a large amount of normal traff ic, making it difficult for the classifier to learn the differences between them during the training process. In the similar samples of the imbalanced WebNov 26, 2024 · We observed that deep learning outperformed machine learning in the experiment after using the DSSTE algorithm to sample the imbalanced training set samples. These methods outperform ML in terms of throughput because of the depth of their structure and the ease with which they can self-learn and produce relevant features from … maggie morales hilton