CLASSIFICATIONObjectives: Recognition of mobile applications within encrypted network traffic holds considerable effects across multiple domains, encompassing network administration, security, and digital marketing. The creation of network traffic classifiers capable of adjusting to dynamic and unforeseeable real-...
Accurate traffic classification and categorization can offer obvious advantages to a number of network activities such as the QOS control, network security monitoring, and traffic modeling. In order to avoid the disadvantages of port-based or payload-based classification methods, this paper proposes a ...
TrafficLLMis built upon a sophisticated fine-tuning framework using natural language and traffic data, which proposes the following techniques to enhance the utility of large language models in network traffic analysis. Traffic-Domain Tokenization.To overcome the modality gap between natural language and...
Cromvik and Patriksson (2010b) apply results on the robustness of stochastic mathematical programs with equilibrium constraints to intensity-modulated radiation therapy, in addition to traffic network design. Further, Chen et al. (2011) proposes a robust optimization approach for managing hospital beds...
TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Robust Traffic Representation The repository of TrafficLLM, a universal LLM adaptation framework to learn robust traffic representation for all open-sourced LLM in real-world scenarios and enhance the generalization across diverse...
To this end, the authors propose a novel model named Robust Spatiotemporal Graph Convolutional Network (RT-GCN) to predict traffic flow. The RT-GCN is a combination of a variant GCN and GRU models, which can capture the spatiotemporal dependence of traffic data and improve predictive robustness...
4.1 Scenarios and datasets Two distinct scenarios were considered for IoT net- work intrusion detection: binary and multi-class classification. In the former, the aim of a model was to detect that a network traffic flow was malicious, whereas in the latter, a model had to correctly ...
The middle left is the Siamese sub-network for feature extraction. RPN sub-network lies in the middle right, which has two branches, one for classification and the other for regression. The top is UpdateNet for model update. The online classifier lies at the bottom, which consists of three ...
motor drives. This improved system performance requires even tighter synchronization of servo motor axes used within the end equipment. Real-time 100 Mb Ethernet is widely used in motion control systems today. However, the synchronization only involves data traffic between ...
Network intrusion detection using oversampling technique and machine learning algorithms PeerJ Comput. Sci., 8 (Jan. 2022), p. e820, 10.7717/peerj-cs.820 View in ScopusGoogle Scholar [32] M. Andrecut Attack vs benign network intrusion traffic classification (2022), 10.48550/ARXIV.2205.07323 Go...