Machine learning approach for detecting and combating bring your own device (BYOD) security threats and attacks: a systematic mapping review

CI Eke, AA Norman, M Mulenga - Artificial Intelligence Review, 2023 - Springer
Bring your own device (BYOD) paradigm that permits employees to come with their own
mobile devices to join the organizational network is rapidly changing the organizational …

Exploring the vulnerability in the inference phase of advanced persistent threats

Q Wu, Q Li, D Guo, X Meng - International Journal of …, 2022 - journals.sagepub.com
In recent years, the Internet of Things has been widely used in modern life. Advanced
persistent threats are long-term network attacks on specific targets with attackers using …

Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments

S Alabdulwahab, YT Kim, A Seo, Y Son - Applied Sciences, 2023 - mdpi.com
Networks within the Internet of Things (IoT) have some of the most targeted devices due to
their lightweight design and the sensitive data exchanged through smart city networks. One …

Foundations of Cybersecurity: Core Principles, Practices, and Emerging Trends

A Gaurav, V Arya - Metaverse Security Paradigms, 2024 - igi-global.com
The rapid advancement of digital technology has brought significant benefits but also
introduced a myriad of cybersecurity challenges. In this context, this chapter, addresses the …

[引用][C] Utilizing business analytics for cybersecurity: A proposal for protecting business systems against cyber attacks

C Okafor, M Agho, A Ekwezia, N Eyo-Udo… - Acta Electronica Malaysia, 2023