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    Cyberstalking Issues

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    Authors
    Feng, Xiaohua
    Asante, Audrey
    Short, Emma
    Abeykoon, Iroshan
    Affiliation
    University of Bedfordshire
    Issue Date
    2018-04-02
    Subjects
    digital forensics
    machine learning
    cyber-stalking frameworks
    data mining
    commonsense knowledge
    artificial intelligence
    GDPR
    profiling general data protection regulation
    cyberstalking
    
    Metadata
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    Abstract
    Cyberstalking is closely related to Digital forensics. Digital forensics is an investigation and analysis technique to gather and preserve evidence from a particular computing device in a way that is suitable for presentation in a court of law. The goal of computer forensics is to perform a structured investigation while maintaining a documented chain of evidence to find out exactly what happened on a computing device and who was responsible for it. We can extract evidences from digital devices. The cutting edge technology is to track down the Cyber-stalking evidence with the aid of AI technology, report to law enforcement, and pin down the identity of the stalker. A key focus at this moment is how to proactively detect and prevent this kind of crime.
    Citation
    Feng X, Asante A, Short E, Abeykoon I (2018) 'Cyberstalking Issues', 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) - Orlando, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/624212
    DOI
    10.1109/DASC-PICom-DataCom-CyberSciTec.2017.78
    Additional Links
    https://ieeexplore.ieee.org/document/8328421
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781538619551
    ae974a485f413a2113503eed53cd6c53
    10.1109/DASC-PICom-DataCom-CyberSciTec.2017.78
    Scopus Count
    Collections
    Computing

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