Chaudhary, P, Gupta, B B and Singh, A K (2022) XSS Armor: Constructing XSS Defensive Framework for Preserving Big Data Privacy in Internet-of-Things (IoT) Networks. Journal of Circuits, Systems and Computers. ISSN 0218-1266
Full text not available from this repository. (Request a copy)Abstract
Big data characterizes superfluity of voluminous data that may be in unstructured, structured and/or semi-structured format. Internet of Things networks emerged as one of the biggest sources of big data, stored at the cloud servers. Thereby, it inflates the threats of security attacks such as cross-site scripting (XSS) for stealing the sensitive information and violates the user’s privacy. It leverages an adversary to intervene into user’s individual space. Thus, in this paper, we propose an XSS defensive framework, named as Big IoT Data XSS Armor, to protect user’s privacy in IoT networks. It is a server-side framework that mitigates nonpersistent and persistent XSS attack. Former attack is detected by measuring the similarities in request and response URL with the XSS attack strings. To identify persistent XSS attack, the proposed framework operates by unveiling the irregularities between the genuine features and generated features. The experimental outcomes yield that this framework performs efficiently in shielding against XSS attack and surpasses the detection rate of other existing XSS thwarting techniques because it attains an accuracy of 98.9% and F-measure of 98.4%.
Affiliation: | Skyline University College |
---|---|
SUC Author(s): | Gupta, B B |
All Author(s): | Chaudhary, P, Gupta, B B and Singh, A K |
Item Type: | Article |
Uncontrolled Keywords: | Big data, IoT networks, persistent XSS attack, nonpersistent XSS attack cloud computing, code injection, vulnerability illicit script code |
Subjects: | B Information Technology > BD Big Data Analitics B Information Technology > BP Internet of Things |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 30 May 2022 06:40 |
Last Modified: | 30 May 2022 06:40 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/281 |
Publisher URL: | https://doi.org/10.1142/S021812662250222X |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/9720 |
Related URLs: |
Actions (login required)
![]() |
Statistics for this ePrint Item |