DECH: A Novel Attack Pattern of Cloud Environment and Its Countermeasures

Gao, Haoyu; Li, Leixiao; Lin, Haoming; Wan, Jianxiong; Deng, Dan; Li, Jie

doi:10.1109/csp51677.2021.9357594

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…These deep learning models deployed in cyberspace are always at risk of data leakage [4]. The virtual machines used to train deep learning models have the vulnerability of virtual machine escape [5]. The drivers will be directed to the wrong destination because the vehicle navigation system may receive false and malicious information [6].…”

Section: Introductionmentioning

confidence: 99%

How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

Lin¹

2022

Preprint

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Cybersecurity researchers always ignore the “people” behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— “How to solve cyberspace security issues via social engineering?” We briefly review existing frameworks for social engineering attacks and pro?pose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four “guidelines” for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field.

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Section: Introductionmentioning

confidence: 99%