Aiming at the problem that the traditional Faster R-CNN is not sensitive to small targets and occluded targets, this paper submits an improved Faster R-CNN target detection algorithm. In this paper, using PASCAL VOC07+2012 to be the experimental data sample set. For the large differences in the targets to be detected in this set, the general anchor size and dimensions is not often used for detecting multi-category problems. For the purpose of increasing small objects detection accuracy, using K-means to improve this situation, the annotation information is centralized for clustering, and the clustering result is replaced by the anchor scale and size in the original RPN. Finally, missed detection and false detection caused by partial overlap of objects in the image, this paper uses the improved soft-NMS algorithm. The experimental results show that, compared with the traditional Faster R-CNN algorithm, the average mean precision (mAP) of the algorithm under the PASCAL VOC07+2012 dataset can reach 80.7%, and it is enhanced by 6.5 percentage points.
To address the problems of botnet stealthiness and difficulty in detection, this paper proposes a botnet detection model based on dilated convolution. The model first uses dilated convolution to increase the perceptual field of information and extract features from it, and then uses reflection padding to expand the extracted spatial features with samples, then uses squeeze-and-excitation networks to assign different weights to feature channels, and then uses gate recurrent unit to extract the temporal relationships preserved between features, and finally implements botnet detection. The model is validated on the UNSW-NB15 and CIC-IDS-2017 datasets with 99.4% and 99.3% accuracy, respectively, which verifies the effectiveness of the model for botnet detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.