J Kim, SJ Lee - Journal of the Korea Institute of Information Security …, 2022 - koreascience.kr
… this leads darknet to be … darknettraffic to prevent the misuse and abuse of darknet. This work proposes a novel approach, which uses the Gradient Boosting techniques for darknettraffic …
N Rust-Nguyen, S Sharma, M Stamp - Computers & Security, 2023 - Elsevier
… darknettraffic by exploring the performance of Support Vector Machines (SVM), Random Forest (RF), Gradient-Boosting Decision Trees (GBDT), Extreme Gradient … of the darknettraffic …
… -term memory and extreme gradient boosting as feature selection techniques. … Darknet traffic with an accuracy of 89% for categorization and 96% for detection. To identify Darknettraffic, …
… for gradient-based attacks to craft adversarial samples by utilizing distillation training and hiding the gradients … , calculation gradients from the logits layer, and using transferable attacks. …
J Lan, X Liu, B Li, Y Li, T Geng - Computers & Security, 2022 - Elsevier
… algorithms to solve the darknettraffic classification problem. For instance, several conventional machine learning methods, including the LightGradient Boosting Machine (LightGBM), K-…
… In this paper, we propose a novel darknettraffic analysis and … network traffic analysis, the demystification of malware traffic, … are updated one by one, using a stochastic gradient descent. …
D Brown, C Sepula - Inventive Systems and Control: Proceedings of ICISC …, 2023 - Springer
… [2] determined the classification effectiveness of CatBoost, SVC Linear, lightgradient boosting machine (LightGBM), and logistic regression models on the CICIDS2017 network dataset. …
N Rust-Nguyen, M Stamp - arXiv preprint arXiv:2206.06371, 2022 - arxiv.org
… However, for portability and convenience we also use the first computer, which is a light-… between convolution layers to regularize the training gradient step size. This is thought to …
… However, for portability and convenience we also use the smaller computer, which is a light-… between convolution layers to regularize the training gradient step size. This is thought to …