Detection of suspicious activity using mobile sensor data and Modified Sub-space K-NN for criminal investigations

YMER Digital Pub Date : 2022-08-17 DOI:10.37896/ymer21.08/49
Sukhada Aloni, Divya Shekhawata
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Abstract

With the bulk availability of mobile sensors, the data collected from them mustn’t be wasted. Nowadays the creation of black-box software that collects this data is not a very difficult task. It is possible to detect suspicious unlawful events using this black-box data. In this paper, we present a novel way of doing forensic investigation using a modified sub-space K-NN (MSK) algorithm. The MSK algorithm is capable of detecting suspicious activities from mobile sensor data. Using this technique, we could detect any normal activity versus suspicious activity with 99.7 % accuracy. We expect the future researcher to develop on this idea and build a solid digital forensic system capable of doing bias-free decisions. Keywords: Forensic, Mobile sensor data, Black box, mobile data collection
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基于移动传感器数据和改进子空间K-NN的犯罪侦查可疑活动检测
随着移动传感器的大量可用性,从它们收集的数据不能浪费。如今,创建收集这些数据的黑匣子软件并不是一项非常困难的任务。使用这些黑匣子数据可以检测到可疑的非法事件。在本文中,我们提出了一种使用改进的子空间K-NN (MSK)算法进行法医调查的新方法。MSK算法能够从移动传感器数据中检测可疑活动。使用这种技术,我们可以以99.7%的准确率检测任何正常活动和可疑活动。我们期待未来的研究人员在这个想法的基础上发展,并建立一个可靠的数字法医系统,能够做出无偏见的决定。关键词:法医,移动传感器数据,黑匣子,移动数据采集
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