The Experts below are selected from a list of 162 Experts worldwide ranked by ideXlab platform
Engin Kirda - One of the best experts on this subject based on the ideXlab platform.
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Panorama: Capturing System-wide Information Flow for Malware Detection and Analysis
Proceedings of the 14th ACM conference on Computer and communications security (CCS '07), 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
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ACM Conference on Computer and Communications Security - Panorama: capturing system-wide information flow for malware detection and analysis
Proceedings of the 14th ACM conference on Computer and communications security - CCS '07, 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
Heng Yin - One of the best experts on this subject based on the ideXlab platform.
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Privacy-breaching Behavior Analysis
SpringerBriefs in Computer Science, 2012Co-Authors: Heng Yin, Dawn SongAbstract:Malicious programs spy on users’ behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users’ privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
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Panorama: Capturing System-wide Information Flow for Malware Detection and Analysis
Proceedings of the 14th ACM conference on Computer and communications security (CCS '07), 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
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ACM Conference on Computer and Communications Security - Panorama: capturing system-wide information flow for malware detection and analysis
Proceedings of the 14th ACM conference on Computer and communications security - CCS '07, 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
Geoffrey P. Goodwin - One of the best experts on this subject based on the ideXlab platform.
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Preferences for Enhancement Pharmaceuticals: The Reluctance to Enhance Fundamental Traits
2008Co-Authors: Jason Riis, Joseph P. Simmons, Geoffrey P. GoodwinAbstract:Four studies examined young healthy individuals' willingness to take drugs intended to enhance their own social, emotional, and cognitive Traits. We found that people were much more reluctant to enhance Traits believed to be more Fundamental to self-identity (e.g., social comfort) than Traits considered less Fundamental to self-identity (e.g., concentration ability). Moral acceptability of a Trait enhancement strongly predicted people's desire to legalize those enhancements, but not their willingness to take those enhancements. Ad taglines that framed enhancements as enabling rather than enhancing the Fundamental self increased people's interest in a Fundamental Trait enhancement, and eliminated the preference for less Fundamental over more Fundamental Trait enhancements.
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Preferences for Enhancement Pharmaceuticals: The Reluctance to Enhance Fundamental Traits
Journal of Consumer Research, 2008Co-Authors: Jason Riis, Joseph P. Simmons, Geoffrey P. GoodwinAbstract:Four studies examined the willingness of young, healthy individuals to take drugs intended to enhance their own social, emotional, and cognitive Traits. We found that people were much more reluctant to enhance Traits believed to be more fun- damental to self-identity (e.g., social comfort) than Traits considered less funda- mental to self-identity (e.g., concentration ability). Moral acceptability of a Trait enhancement strongly predicted people’s desire to legalize the enhancement but not their willingness to take the enhancement. Ad taglines that framed enhance- ments as enabling rather than enhancing the Fundamental self increased people’s interest in a Fundamental Trait enhancement and eliminated the preference for less Fundamental over more Fundamental Trait enhancements.
Manuel Egele - One of the best experts on this subject based on the ideXlab platform.
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Panorama: Capturing System-wide Information Flow for Malware Detection and Analysis
Proceedings of the 14th ACM conference on Computer and communications security (CCS '07), 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
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ACM Conference on Computer and Communications Security - Panorama: capturing system-wide information flow for malware detection and analysis
Proceedings of the 14th ACM conference on Computer and communications security - CCS '07, 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
Christopher Kruegel - One of the best experts on this subject based on the ideXlab platform.
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Panorama: Capturing System-wide Information Flow for Malware Detection and Analysis
Proceedings of the 14th ACM conference on Computer and communications security (CCS '07), 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
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ACM Conference on Computer and Communications Security - Panorama: capturing system-wide information flow for malware detection and analysis
Proceedings of the 14th ACM conference on Computer and communications security - CCS '07, 2007Co-Authors: Heng Yin, Manuel Egele, Christopher Kruegel, Dawn Song, Engin KirdaAbstract:Malicious programs spy on users' behavior and compromise their privacy. Even software from reputable vendors, such as Google Desktop and Sony DRM media player, may perform undesirable actions. Unfortunately, existing techniques for detecting malware and analyzing unknown code samples are insufficient and have significant shortcomings. We observe that malicious information access and processing behavior is the Fundamental Trait of numerous malware categories breaching users' privacy (including keyloggers, password thieves, network sniffers, stealth backdoors, spyware and rootkits), which separates these malicious applications from benign software. We propose a system, Panorama, to detect and analyze malware by capturing this Fundamental Trait. In our extensive experiments, Panorama successfully detected all the malware samples and had very few false positives. Furthermore, by using Google Desktop as a case study, we show that our system can accurately capture its information access and processing behavior, and we can confirm that it does send back sensitive information to remote servers in certain settings. We believe that a system such as Panorama will offer indispensable assistance to code analysts and malware researchers by enabling them to quickly comprehend the behavior and innerworkings of an unknown sample.
