{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:48Z","timestamp":1740120228922,"version":"3.37.3"},"reference-count":12,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"name":"the National Natural Science Foundation for \"targeting complex network modeling and behavioral analysis of the research\"","award":["61572115"],"award-info":[{"award-number":["61572115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2019,12,15]]},"abstract":"<jats:p> With the development of computers and network technologies, network security has gradually become a global problem. Network security defenses need to be carried out not only on the Internet, but also on other communication media, such as electromagnetic signals. Existing electromagnetic signal communication is easily intercepted or infiltrated. In order to effectively detect the abnormal electromagnetic signal to find out the specific location, then classify it, it is necessary to study the way of communication. The existing electromagnetic signal detection accuracy is low and cannot be located. Considering the characteristics of different power sources in different locations, combined with spark streaming technology and machine learning classification technology, a joint platform for electromagnetic signal anomaly detection based on big data analysis is proposed. The electromagnetic signal is abnormally detected by feature comparison and small signal analysis, and the position and number between the signal sources are determined by three-point positioning and signal attenuation. The experimental results show\u00a0that the method can detect abnormal electromagnetic signals and classify abnormal electromagnetic signals well, the accuracy rate can reach 95%, and the positioning accuracy can reach 89%. <\/jats:p>","DOI":"10.1142\/s0218001419580096","type":"journal-article","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T04:12:50Z","timestamp":1548389570000},"page":"1958009","source":"Crossref","is-referenced-by-count":3,"title":["A Lockable Abnormal Electromagnetic Signal Joint Detection Algorithm"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5166-2432","authenticated-orcid":false,"given":"Jiazhong","family":"Lu","sequence":"first","affiliation":[{"name":"Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China"}]},{"given":"Weina","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu 610065, P. R. China"}]},{"given":"Xiaolei","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China"}]},{"given":"Teng","family":"Hu","sequence":"additional","affiliation":[{"name":"Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China"}]},{"given":"Xiaosong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"issue":"5","key":"S0218001419580096BIB002","first-page":"344","volume":"19","author":"Hu G. B.","year":"2011","journal-title":"J. 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