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Filtrado por: Creador Gonen, Bilal Eliminar la restricciónCreador: Gonen, Bilal Colecciones 2020 Information Technology Research Symposium Eliminar la restricciónColecciones: 2020 Information Technology Research Symposium

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  • Scraping The Deep Web: A 3-Dimensional Framework For Cyber-Threat Intelligence

    Article thumbnail: Scraping The Deep Web: A 3-Dimensional Framework For Cyber-Threat Intelligence
    Type:
    Article
    Descripción/Resumen:
    Cyberspace is one of the most complex systems ever built by humans. The utilization of cybertechnology resources are used ubiquitously by many, but sparsely understood by the majority of the users. In the past, cyberattacks were usually orchestrated in a random pattern of attack to lure unsuspecting targets. However, the cyber virtual environment is an ecosystem that provided a platform for an organized and sophisticated approach to launch an attack against a specific target group or organization by nefarious actors. In 2019, the average cost of cyber-attack in the US was about $1.6 million. This paper proposes a 3D framework to signal new threat alert before the actual occurrence of the threat on the surface web to alert cybersecurity experts and law enforcement agencies in preventive measures or means of mitigating the severity of damage caused by cyberattacks. The methodology combines information extracted from the deep web through a smart web crawler with socio-personal and technical indicators from twitter which is mapped with OTX (Open Threat Exchange). The OTX is an open-source cyber threat platform managed by security experts. The OTX endpoint security tool(OTX python SDK) will be used to identify a new type of cyber threats. The effectiveness of the framework will be tested using the machine learning algorithm precision-recall rate.
    Creador/Autor:
    Adewale, Adewopo Victor and Gonen, Bilal
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    All rights reserved

  • Relative Importance of Blockchain in Health Care: Enhancing data sharing and mitigating cyber security crimes

    Article thumbnail: Relative Importance of Blockchain in Health Care: Enhancing data sharing and mitigating cyber security crimes
    Type:
    Article
    Descripción/Resumen:
    This paper looks at the opportunities and challenges of implementing blockchain technology across the medical sector and provides a clear view which can enable blockchain for more extents. After a notable research on underlying blockchain technology which offers distributed governance, immutable audit trail, provenance of data, robustness and privacy, we contrasted blockchain innovations and identified prominent applications of it in historically decentralized healthcare sectors. As the healthcare industry faces many challenges like unauthorised data sharing, lack of data transparency, ransomware, data breaches and cyber crimes, blockchain is one of the best ways to enhance data sharing and to mitigate prominent cyber crimes. By proper designing of a decentralized and immutable blockchain network where the data is dispersed among credentialed social insurance experts guarantees that cybercriminals cannot touch single patient’s confidential data, which facilitates encryption or cryptography of personal data where no patient’s emergency data is at extreme hazard. Blockchain trust-worthy cloud is one of the most powerful and secure ways of storing high confidential data. After analysing Blockchain implementations and identifying its potential in healthcare, we conclude with several promising directions for future research.
    Creador/Autor:
    Ponnakanti, Hari Priya; Ozer, Murat, and Gonen, Bilal
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    All rights reserved

  • Application of Autoencoder Duets in Anomaly and Intrusion Detection

    Article thumbnail: Application of Autoencoder Duets in Anomaly and Intrusion Detection
    Type:
    Article
    Descripción/Resumen:
    Signature-based intrusion detection methods report high accuracy with a low false alarm rate. However, they do not perform well when faced with new or emerging threats. This work focuses on anomaly-based data driven methods to identify potential zero-day-attacks using a specific class of neural networks known as the autoencoder.
    Creador/Autor:
    Li, Chengcheng; Lee, Kijung; Mathur, Nitin, and Gonen, Bilal
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2020-04-14
    Licencia:
    All rights reserved