This paper is concerned with synaptic coding when inputs to a neuron change over time. Experiments were performed on a living and simulated embodiment of a prototypical inhibitory synapse. These were used to test a simple model composed of a fixed delay preceding a nonlinear encoder. Based on these results, we present a qualitative model for phenomena previously observed in the living preparation, including hysteresis and dependence of discharge regularity on rate of change of presynaptic spike rate. As change is the rule rather than the exception in nature, understanding neurons responses to nonstationarity is essential for understanding their function.
In recent years, peer-to-peer (P2P) applications have become the dominant form of Internet traffic. Foxy, a Chinese community focused filesharing tool, is increasingly being used to disseminate private data and sensitive documents in Hong Kong. Unfortunately, its scattered design and a highly distributed network make it difficult to locate a file originator. This paper proposes an investigative model for analyzing Foxy communications and identifying the first uploaders of files. The model is built on the results of several experiments, which reveal behavior patterns of the Foxy protocol that can be used to expose traces of file originators.
The community of peer-to-peer (P2P) file-sharing networks has been expanding swiftly since the appearance of the very first P2P application (Napster) in 2001. These networks are famous for their excellent file transfer rates and adversely, the flooding of copyright-infringed digital materials. Recently, a number of documents containing personal data or sensitive information have been shared in an unbridled manner over the Foxy network (a popular P2P network in Chinese regions). These incidents have urged the authors to develop an investigation model for tracing suspicious P2P activities. Unfortunately, hindered by the distributed design and anonymous nature of these networks, P2P investigation can be practically difficult and complicated. In this chapter, the authors briefly review the characteristics of current P2P networks. By observing the behaviors of these networks, they propose some heuristic rules for identifying the first uploader of a shared file. Also, the rules have been demonstrated to be applicable to some simulated cases. The authors believe their findings provide a foundation for future development in P2P file-sharing networks investigation.
Performing live forensics investigation becomes a trend in digital forensics. Different vendors and software developer implement their own investigation procedures. By applying FORZA framework -a digital forensics investigation framework, investigation requirement could be translated and formulated into criteria in applying appropriate forensics investigation requirement. Through this model, only necessary searching would be applied to live investigation process instead of simply passing all investigation process to live investigation unintentionally.In this paper, the FORZA framework that applied to live forensics investigation will be presented and illustrated using the investigation of the first BT illegal movie upload investigation.
International audienceOne of the core components of live forensics is to collect and analyze volatile memory data. Since the dynamic analysis of memory is not possible, most live forensic approaches focus on analyzing a single snapshot of a memory dump. Analyzing a single memory dump raises questions about evidence reliability; consequently, a natural extension is to study data from multiple memory dumps. Also important is the need to differentiate static data from dynamic data in the memory dumps; this enables investigators to link evidence based on memory structures and to determine if the evidence is found in a consistent area or a dynamic memory buffer, providing greater confidence in the reliability of the evidence. This paper proposes an indexing data structure for analyzing pages from multiple memory dumps in order to identify static and dynamic pages
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