{"title":"FASE:寻找调幅的侧信道辐射","authors":"R. Callan, A. Zajić, Milos Prvulović","doi":"10.1145/2749469.2750394","DOIUrl":null,"url":null,"abstract":"While all computation generates electromagnetic (EM) side-channel signals, some of the strongest and farthest-propagating signals are created when an existing strong periodic signal (e.g. a clock signal) becomes stronger or weaker (amplitude-modulated) depending on processor or memory activity. However, modern systems create emanations at thousands of different frequencies, so it is a difficult, error-prone, and time-consuming task to find those few emanations that are AM-modulated by processor/memory activity. This paper presents a methodology for rapidly finding such activity-modulated signals. This method creates recognizable spectral patterns generated by specially designed micro-benchmarks and then processes the recorded spectra to identify signals that exhibit amplitude-modulation behavior. We apply this method to several computer systems and find several such modulated signals. To illustrate how our methodology can benefit side-channel security research and practice, we also identify the physical mechanisms behind those signals, and find that the strongest signals are created by voltage regulators, memory refreshes, and DRAM clocks. Our results indicate that each signal may carry unique information about system activity, potentially enhancing an attacker's capability to extract sensitive information. We also confirm that our methodology correctly separates emanated signals that are affected by specific processor or memory activities from those that are not.","PeriodicalId":6878,"journal":{"name":"2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA)","volume":"85 1","pages":"592-603"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"49","resultStr":"{\"title\":\"FASE: Finding Amplitude-modulated Side-channel Emanations\",\"authors\":\"R. Callan, A. Zajić, Milos Prvulović\",\"doi\":\"10.1145/2749469.2750394\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"While all computation generates electromagnetic (EM) side-channel signals, some of the strongest and farthest-propagating signals are created when an existing strong periodic signal (e.g. a clock signal) becomes stronger or weaker (amplitude-modulated) depending on processor or memory activity. However, modern systems create emanations at thousands of different frequencies, so it is a difficult, error-prone, and time-consuming task to find those few emanations that are AM-modulated by processor/memory activity. This paper presents a methodology for rapidly finding such activity-modulated signals. This method creates recognizable spectral patterns generated by specially designed micro-benchmarks and then processes the recorded spectra to identify signals that exhibit amplitude-modulation behavior. We apply this method to several computer systems and find several such modulated signals. To illustrate how our methodology can benefit side-channel security research and practice, we also identify the physical mechanisms behind those signals, and find that the strongest signals are created by voltage regulators, memory refreshes, and DRAM clocks. Our results indicate that each signal may carry unique information about system activity, potentially enhancing an attacker's capability to extract sensitive information. We also confirm that our methodology correctly separates emanated signals that are affected by specific processor or memory activities from those that are not.\",\"PeriodicalId\":6878,\"journal\":{\"name\":\"2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA)\",\"volume\":\"85 1\",\"pages\":\"592-603\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2749469.2750394\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2749469.2750394","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
While all computation generates electromagnetic (EM) side-channel signals, some of the strongest and farthest-propagating signals are created when an existing strong periodic signal (e.g. a clock signal) becomes stronger or weaker (amplitude-modulated) depending on processor or memory activity. However, modern systems create emanations at thousands of different frequencies, so it is a difficult, error-prone, and time-consuming task to find those few emanations that are AM-modulated by processor/memory activity. This paper presents a methodology for rapidly finding such activity-modulated signals. This method creates recognizable spectral patterns generated by specially designed micro-benchmarks and then processes the recorded spectra to identify signals that exhibit amplitude-modulation behavior. We apply this method to several computer systems and find several such modulated signals. To illustrate how our methodology can benefit side-channel security research and practice, we also identify the physical mechanisms behind those signals, and find that the strongest signals are created by voltage regulators, memory refreshes, and DRAM clocks. Our results indicate that each signal may carry unique information about system activity, potentially enhancing an attacker's capability to extract sensitive information. We also confirm that our methodology correctly separates emanated signals that are affected by specific processor or memory activities from those that are not.