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Automatically Unaware: Using Data Analytics to Detect Physiological Markers of Cybercrime

机译:自动不知道:使用数据分析检测网络犯罪的生理标志

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Cybercrime investigation is reliant on availability of adequate and valid digital artifacts useable for reconstructing security incidents or triangulating other available information to make it useful. Various operational artifacts of computer systems, networks and software have been studied and gradually applied as forensic evidence. However the scope of studies on human-generated artifacts as forensic evidence has been limited mostly focusing on surveillance images, with DNA deposits being widely studied via older forensic fields. We present the case that further focus on human-centric evidence in form of physiological measurements is useful in triangulating other evidence as well as in making some direct inferences. In this concept paper: we pair electroencephalography (EEG) with change point detection algorithms to conceptually model the acquisition and processing of EEG signals into forensic artifacts; propose continuous data reduction and packaging to keep the system forensic-ready; suggest a schema for validating such artifacts towards their applicability as forensic evidence; and model a study to be used in testing the conceptual model. This work contributes to cybersecurity research by high-lighting human-generated artifacts as a forensic big data resource and presenting a methodology for harnessing the data to turn it into useful information.
机译:网络犯罪调查依赖于可用于重建安全事件或三角测量其他可用信息来实现适当和有效的数字伪影的可用性,以使其有用。已经研究了各种计算机系统,网络和软件的操作文物,并逐步应用于法医证据。然而,作为法医证据的人生成的工件的研究范围受到限制,主要关注监测图像,DNA沉积物通过较旧的法医领域被广泛研究。我们展示了进一步关注以生理测量形式的人为正的证据,可用于三角训练其他证据以及制作一些直接推论。在这篇概念论文中:我们将脑电图(EEG)与变更点检测算法捆绑在概念上模拟EEG信号的获取和处理到法医伪影;提出连续的数据减少和包装,以保持系统取得的准备;建议验证其适用性的艺术品作为法医证据的架构;并模拟用于测试概念模型的研究。这项工作通过作为法医基础数据资源的高点亮人类生成的工件而有助于网络安全,并呈现用于利用数据的方法将其转化为有用信息。

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