At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are oft...
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At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization (PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection.
With the increasingly wide application of Internet of Things technology in the industrial field, Industrial Internet of Things provides a viable and convenient service for the development of intelligent industry, mean...
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ISBN:
(数字)9783319607535
ISBN:
(纸本)9783319607535;9783319607528
With the increasingly wide application of Internet of Things technology in the industrial field, Industrial Internet of Things provides a viable and convenient service for the development of intelligent industry, meanwhile more and more countries pay attention to the development of Industrial Internet of Things. The data of Industrial Internet of Things have features such as massive polymorphism, dynamic heterogeneity, relevance, real-time etc. These features become resistance in the process of data to create value. This paper explores the issues and challenges of data of Industrial Internet of Things, and studies data processing technology deeply. It provide some help to improve data management of Industrial Internet of Things.
Based upon the synopsis of big data and electronic information system,this paper puts up the data framework,carries out practical analysis and systematic application of data collecting technology,data synchronization ...
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Based upon the synopsis of big data and electronic information system,this paper puts up the data framework,carries out practical analysis and systematic application of data collecting technology,data synchronization technology,data excavation technology,and data analysis technology,and describes the analysis model of system data *** will benefit the dynamic improvement of electronic information system.
Adanced traveler information systems (ATIS) use sensing, communication and dataprocessing technologies to collect and disseminate real time road traffic information. Route guidance systems utilize this data to estima...
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ISBN:
(纸本)9781467365963
Adanced traveler information systems (ATIS) use sensing, communication and dataprocessing technologies to collect and disseminate real time road traffic information. Route guidance systems utilize this data to estimate and optimize travel times. Paths with the shortest travel times are then computed to suggest to the users, an optimal path to reach their destination. In this study, we examine the accuracy of popular travel time calculation techniques, that use historical, instantaneous and predictive data to compute travel time. A dynamic time calculation technique that accounts for time varying road speeds is used and the driver is considered to experience changes in the traffic pattern as the journey progresses. We base our study on real world traffic data collected from Singapore. We calculate the travel times for trips on major routes based on these techniques and compare them with the true average travel time on the particular route. We also analyze the variablity in travel time that can be experienced by the user when guided by different route guidance system. The results show that dynamic predictive routing using multiple prediction horizons provides a better estimate of actual travel times as opposed to routing algorithms that only utilize real-time data about current traffic conditions.
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