Cyber threat intelligence mining for proactive cybersecurity defense: a survey and new perspectives

N Sun, M Ding, J Jiang, W Xu, X Mo… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Today's cyber attacks have become more severe and frequent, which calls for a new line of
security defenses to protect against them. The dynamic nature of new-generation threats …

Ai-driven cybersecurity: an overview, security intelligence modeling and research directions

IH Sarker, MH Furhad, R Nowrozy - SN Computer Science, 2021 - Springer
Artificial intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (or
Industry 4.0), which can be used for the protection of Internet-connected systems from cyber …

Deep learning based attack detection for cyber-physical system cybersecurity: A survey

J Zhang, L Pan, QL Han, C Chen… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …

[HTML][HTML] MapReduce based intelligent model for intrusion detection using machine learning technique

M Asif, S Abbas, MA Khan, A Fatima, MA Khan… - Journal of King Saud …, 2022 - Elsevier
With the emergence of the Internet of Things (IoT), the computer networks' phenomenal
expansion, and enormous relevant applications, data is continuously increasing. In this way …

Cybersecurity data science: an overview from machine learning perspective

IH Sarker, ASM Kayes, S Badsha, H Alqahtani… - Journal of Big …, 2020 - Springer
In a computing context, cybersecurity is undergoing massive shifts in technology and its
operations in recent days, and data science is driving the change. Extracting security …

A survey of android malware detection with deep neural models

J Qiu, J Zhang, W Luo, L Pan, S Nepal… - ACM Computing Surveys …, 2020 - dl.acm.org
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …

Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey

J Chen, C Yi, SD Okegbile, J Cai… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Digital twin (DT), referring to a promising technique to digitally and accurately represent
actual physical entities, has attracted explosive interests from both academia and industry …

Automation of human behaviors and its prediction using machine learning

H Jupalle, S Kouser, AB Bhatia, N Alam… - Microsystem …, 2022 - Springer
Prediction is a method of detecting a person's behavior toward online buying by evaluating
publically available evaluations on the web. Understanding expressive human …

Software vulnerability detection using deep neural networks: a survey

G Lin, S Wen, QL Han, J Zhang… - Proceedings of the …, 2020 - ieeexplore.ieee.org
The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

Combining graph-based learning with automated data collection for code vulnerability detection

H Wang, G Ye, Z Tang, SH Tan… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …