Cybersecurity Challenge Analysis of Work-from-Anywhere (WFA) and Recommendations guided by a User Study
September 11, 2024 Β· Declared Dead Β· π IEEE Transactions on Human-Machine Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mohammed Mahyoub, Ashraf Matrawy, Kamal Isleem, Olakunle Ibitoye
arXiv ID
2409.07567
Category
cs.CR: Cryptography & Security
Citations
2
Venue
IEEE Transactions on Human-Machine Systems
Last Checked
4 months ago
Abstract
Many organizations were forced to quickly transition to the work-from-anywhere (WFA) model as a necessity to continue with their operations and remain in business despite the restrictions imposed during the COVID-19 pandemic. Many decisions were made in a rush, and cybersecurity decency tools were not in place to support this transition. In this paper, we first attempt to uncover some challenges and implications related to the cybersecurity of the WFA model. Secondly, we conducted an online user study to investigate the readiness and cybersecurity awareness of employers and their employees who shifted to work remotely from anywhere. The user study questionnaire addressed different resilience perspectives of individuals and organizations. The collected data includes 45 responses from remotely working employees of different organizational types: universities, government, private, and non-profit organizations. Despite the importance of security training and guidelines, it was surprising that many participants had not received them. A robust communication strategy is necessary to ensure that employees are informed and updated on security incidents that the organization encounters. Additionally, there is an increased need to pay attention to the security-related attributes of employees, such as their behavior, awareness, and compliance. Finally, we outlined best practice recommendations and mitigation tips guided by the study results to help individuals and organizations resist cybercrime and fraud and mitigate WFA-related cybersecurity risks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted