The Experts below are selected from a list of 3900 Experts worldwide ranked by ideXlab platform
Mirco Musolesi - One of the best experts on this subject based on the ideXlab platform.
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stop socio temporal opportunistic patching of short range Mobile Malware
World of Wireless Mobile and Multimedia Networks, 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
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WOWMOM - STOP: Socio-Temporal Opportunistic Patching of short range Mobile Malware
2012 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
John Tang - One of the best experts on this subject based on the ideXlab platform.
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stop socio temporal opportunistic patching of short range Mobile Malware
World of Wireless Mobile and Multimedia Networks, 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
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WOWMOM - STOP: Socio-Temporal Opportunistic Patching of short range Mobile Malware
2012 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
Cecilia Mascolo - One of the best experts on this subject based on the ideXlab platform.
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stop socio temporal opportunistic patching of short range Mobile Malware
World of Wireless Mobile and Multimedia Networks, 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
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WOWMOM - STOP: Socio-Temporal Opportunistic Patching of short range Mobile Malware
2012 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
Hyoungshick Kim - One of the best experts on this subject based on the ideXlab platform.
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stop socio temporal opportunistic patching of short range Mobile Malware
World of Wireless Mobile and Multimedia Networks, 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
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WOWMOM - STOP: Socio-Temporal Opportunistic Patching of short range Mobile Malware
2012 IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2012Co-Authors: John Tang, Hyoungshick Kim, Cecilia Mascolo, Mirco MusolesiAbstract:Mobile phones are integral to everyday life with emails, social networking, online banking and other applications; however, the wealth of private information accessible increases economic incentives for attackers. Compared with fixed networks, Mobile Malware can replicate through both long range messaging and short range radio technologies; the former can be filtered by the network operator but determining the best method of containing short range Malware is an open problem. While global software updates are sometimes possible, they are often not practical. An alternative and more efficient strategy is to distribute the patch to the key nodes so that they can opportunistically disseminate it to the rest of the network via short range encounters; but how can these key nodes be identified in a highly dynamic network topology? In this paper, we address these questions by presenting Socio- Temporal Opportunistic Patching (STOP), a two-tier predictive Mobile Malware containment system: devices collect co-location data in a decentralized manner and report to a central server which processes and targets delivery of hot fixes to a small subset of k devices at runtime; in turn Mobile devices spread the patch opportunistically. The STOP system is underpinned by a recent theoretical framework for analysing dynamic networks that takes into account temporal information of links. Using empirical contact traces, we find firstly, the top-k ranking temporal centrality nodes are highly correlated with past time windows; and secondly, simple prediction functions can be designed to select the set of top-k nodes that are optimal for patch spreading.
Victor Chang - One of the best experts on this subject based on the ideXlab platform.
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Mobile Malware attacks review taxonomy future directions
Future Generation Computer Systems, 2019Co-Authors: Attia Qamar, Ahmad Karim, Victor ChangAbstract:Abstract A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent years. Android’s popular and attractive environment not only captured the attention of users but also increased security concerns. As a result, Android Malware detection is one of the sizzling topics in the Mobile security domain. This paper provides a comprehensive review of state-of-the-art Mobile Malware attacks, vulnerabilities, detection techniques and security solutions over the period of 2013–2019 that majorly targeted Android platform. We have presented various well-organized and in-depth taxonomies that uncover Mobile Malware detection approaches based on their analysis techniques, working platform, data acquisition, operational impact, obtained results and artificial intelligence component involved. Another taxonomy comprises of Mobile Malware attack vector is presented to look threat clusters and loopholes to locate their malicious widespread impact on communities. Furthermore, we have discussed and classified forensic analysis efforts in Mobile Malware detection perspective. From the intruder point of view, we have compared various evasion techniques that are used prominently by the Malware authors to hinder detection efforts. Finally, future work directions are presented as guidelines for academia and industry alike to help them reduce or even avoid the harmful impact of these annoying efforts.
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Mobile Malware attacks: Review, taxonomy & future directions
Future Generation Computer Systems, 2019Co-Authors: Attia Qamar, Ahmad Karim, Victor ChangAbstract:Abstract A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent years. Android’s popular and attractive environment not only captured the attention of users but also increased security concerns. As a result, Android Malware detection is one of the sizzling topics in the Mobile security domain. This paper provides a comprehensive review of state-of-the-art Mobile Malware attacks, vulnerabilities, detection techniques and security solutions over the period of 2013–2019 that majorly targeted Android platform. We have presented various well-organized and in-depth taxonomies that uncover Mobile Malware detection approaches based on their analysis techniques, working platform, data acquisition, operational impact, obtained results and artificial intelligence component involved. Another taxonomy comprises of Mobile Malware attack vector is presented to look threat clusters and loopholes to locate their malicious widespread impact on communities. Furthermore, we have discussed and classified forensic analysis efforts in Mobile Malware detection perspective. From the intruder point of view, we have compared various evasion techniques that are used prominently by the Malware authors to hinder detection efforts. Finally, future work directions are presented as guidelines for academia and industry alike to help them reduce or even avoid the harmful impact of these annoying efforts.
