LEADER 03585nam 22006855 450 001 9910483068103321 005 20230502194334.0 010 $a3-030-62743-8 024 7 $a10.1007/978-3-030-62743-0 035 $a(CKB)4100000011558812 035 $a(MiAaPQ)EBC6384536 035 $a(DE-He213)978-3-030-62743-0 035 $a(PPN)25250450X 035 $a(EXLCZ)994100000011558812 100 $a20201103d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy$b[electronic resource] $eSPIoT-2020, Volume 1 /$fedited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XXXI, 884 p. 221 illus., 150 illus. in color.) 225 1 $aAdvances in Intelligent Systems and Computing,$x2194-5365 ;$v1282 300 $aIncludes index. 311 $a3-030-62742-X 330 $aThis book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field. 410 0$aAdvances in Intelligent Systems and Computing,$x2194-5365 ;$v1282 606 $aEngineering?Data processing 606 $aCooperating objects (Computer systems) 606 $aComputational intelligence 606 $aMachine learning 606 $aBig data 606 $aData Engineering 606 $aCyber-Physical Systems 606 $aComputational Intelligence 606 $aMachine Learning 606 $aBig Data 615 0$aEngineering?Data processing. 615 0$aCooperating objects (Computer systems). 615 0$aComputational intelligence. 615 0$aMachine learning. 615 0$aBig data. 615 14$aData Engineering. 615 24$aCyber-Physical Systems. 615 24$aComputational Intelligence. 615 24$aMachine Learning. 615 24$aBig Data. 676 $a620.00285 702 $aMacIntyre$b J. D$g(John D.), 702 $aZhao$b Jinghua 702 $aMa$b Xiaomeng 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483068103321 996 $aThe 2020 international conference on machine learning and big data analytics for IoT security and privacy$92572141 997 $aUNINA LEADER 02409nam 2200397 450 001 996280664403316 005 20231206213944.0 010 $a1-5044-0238-3 024 70$a10.1109/IEEESTD.1966.7393375 035 $a(CKB)3710000000578030 035 $a(NjHacI)993710000000578030 035 $a(EXLCZ)993710000000578030 100 $a20231206d1966 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIEEE No 268-1966 $eIEEE Recommended Practice for Units in Published Scientific and technical Work /$fIEEE 210 1$aPiscataway, NJ :$cIEEE,$d1966. 215 $a1 online resource (8 pages) 225 1 $aIEEE Std ;$vNumber 268-1966 330 $aThis document is a companion to Technical Committee Report on Recommended Practices for Burst Measurements in the Time Domain, IEEE No. 257, May 1964. In the time domain document, bursts are defined and particular attention is placed on their duration and magnitude. The use of additional characteristics may prove desirable when investigating both cause and effect of a burst. Mathematical transformations have been widely used to bring out particular characteristics of signals. Perhaps the one most commonly used is the Fourier Transform, which defines the spectrum of signals. The energy density spectrum of a burst, a quantity derived from the Fourier Transform, is the subject of this report. Other transformations such as those of Hilbert or Henkel, may be used to display different characteristics of a burst but they will not be considered here. Sampling the energy density spectrum is the key concept of this document. It constitutes the basis by which this spectrum can be characterized comprehensively by a practical number of measurements. The sampling theorems in the frequency domain are, therefore, given detailed consideration in Appendices to the extent necessary for understanding the measurement methods to be discussed. 410 0$aIEEE Std ;$vNumber 268-1966. 517 $aIEEE No 268-1966 606 $aUnits of measurement 606 $aMeasurement$xStandards 615 0$aUnits of measurement. 615 0$aMeasurement$xStandards. 676 $a530.8 801 0$bNjHacI 801 1$bNjHacl 906 $aDOCUMENT 912 $a996280664403316 996 $aIEEE No 268-1966$93647224 997 $aUNISA