Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints ...Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.展开更多
The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves stora...The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.展开更多
文摘Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.
基金the National Key Basic Research and Development (973) Program of China (Nos. 2012CB315801 and 2011CB302805)the National Natural Science Foundation of China A3 Program (No. 61161140320) and the National Natural Science Foundation of China (No. 61233016)Intel Research Councils UPO program with title of security Vulnerability Analysis based on Cloud Platform with Intel IA Architecture
文摘The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.
