KEYWORDS: Data modeling, Random forests, Detection and tracking algorithms, Machine learning, Feature extraction, Education and training, Systems modeling, Statistical modeling, Feature selection, Particle swarm optimization
Aiming at the problems of the high dimension of features, high complexity of feature processing, and low efficiency of model detection of traditional industrial control network traffic data in complex network environments, this study uses an abnormal network flow identification and detection method based on random forest (RF), multi-head attention (ATT) and long short-term memory (LSTM) network. Firstly, the random forest algorithm is used to calculate the importance score of flow characteristics, screen out important features, and eliminate redundant features. Then, LSTM is adopted to identify and detect abnormal flows. In order to evaluate the effectiveness and superiority of the model, the accuracy, precision, recall, and F1-score are used in this study to evaluate the model, and the model is compared with traditional machine learning methods including Naive Bayes, QDA, and KNN algorithms. The experimental results show that the overall accuracy of abnormal flow identification reaches 99% on the CIC-IDS-2017 public data set.
The shape of defects on steel surfaces is highly variable and training samples are limited, making it a significant challenge to transfer a high-performance pretrained vision language model to steel surface defect detection. Therefore, a Multi-level Supervised Vision Language Model based Steel Surface Defect Detection method MLS-VLM is proposed in this paper. MLS-VLM delves deeply into the extraction of profound features from limited samples with three levels of training: supervised contrast training from labeled areas and the entire image, as well as self-supervised contrast learning from Region Proposals. MLS-VLM can be rapidly transferred to two-stage object detector. Experimental results demonstrate that, compared to traditional object detection methods, MLS-VLM achieves 5.68~8.37 mAP improvement on three benchmark object detectors.
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