Machine learningFeature selectionClusteringUnsupervised learningNetwork trafficTraffic analysisNetwork slicingRecent development in smart devices has lead us to an explosion in data generation and heterogeneity, which requires new network solutions for better analyzing and understanding traffic. These solutions ...
我们的特征集包括下面评价指标的最小值、最大值、平均值、中位数、方差:数据包间隔时间、数据包长、TCP窗口大小,以及下面的统计特征:包数量、总的流长、源和目的IP地址、源和目的端口号。 数据集公开可用:[61] S. Fathi-Kazerooni and R. Rojas-Cessa, PCAP Dataset of Applications Traffic Captured with Wire...
This work provides a comparative analysis illustrating how Deep Learning (DL) surpasses Machine Learning (ML) in addressing tasks within Internet of Things (IoT), such as attack classification and device-type identification. Our approach involves training and evaluating a DL model using a range of ...
Analysis of Early Traffic Processing and Comparison of Machine Learning Algorithms for Real Time Internet Traffic Identification Using Statistical Approach Chapter © 2014 Network Traffic Classification Techniques: A Review Chapter © 2023 References Leiner BM, Cerf VG, Clark DD, Kahn RE, Kleinrock...
Device Functional Role ID via Machine Learning and Network Traffic Analysis Overview NetworkML is the machine learning portion of ourPoseidonproject. The model in networkML classifies each device into a functional role via machine learning models trained on features derived from network traffic. "Functi...
展开 关键词: Internet learning (artificial intelligence telecommunication computing telecommunication traffic C4.5 decision tree Internet machine learning approach network applications traffic classification efficiency Classification tree analysis 会议名称: International Symposium on Modeling 会议...
Second, we introduce a time-based model suitable for the bursty nature of network traffic: the probability of an event depends on the time since it last occurred rather than just its average frequency. Third, we introduce an algorithm for learning conditional rules from attack free training data...
Machine Learning Explained Machine learning is a technique that discovers previously unknown relationships in data by searching potentially very large data sets to discover patterns and trends that go beyond simple statistical analysis. Machine learning uses sophisticated algorithms that are trained to identi...
Machine learning lets organizations extract insights from their data that they might not be able to find any other way. Some of the most common benefits from integrating machine learning into processes include the following: Streamlining Decision-Making and Predictive Analysis: Data-driven decisions sta...
Machine learning can be used to build intelligent systems that can make decisions and predictions based on data. This can help organizations make better decisions, improve their operations, and create new products and services. Machine learning is an important tool for data analysis and visualization...