Recent progress of using knowledge graph for cybersecurity

K Liu, F Wang, Z Ding, S Liang, Z Yu, Y Zhou - Electronics, 2022 - mdpi.com
In today's dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk
of cyberattacks are both increasing. This means that organizations need to have a strong …

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

MA Khatun, SF Memon, C Eising, LL Dhirani - IEEE Access, 2023 - ieeexplore.ieee.org
The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-
driven infrastructure that highly relies on smart sensing devices (ie, blood pressure monitors …

A review of knowledge graph application scenarios in cyber security

K Liu, F Wang, Z Ding, S Liang, Z Yu, Y Zhou - arXiv preprint arXiv …, 2022 - arxiv.org
Facing the dynamic complex cyber environments, internal and external cyber threat
intelligence, and the increasing risk of cyber-attack, knowledge graphs show great …

A review of digital twins and their application in cybersecurity based on artificial intelligence

MH Homaei, Ó Mogollón-Gutiérrez, JC Sancho… - Artificial Intelligence …, 2024 - Springer
The potential of digital twin technology is yet to be fully realised due to its diversity and
untapped potential. Digital twins enable systems' analysis, design, optimisation, and …

[PDF][PDF] Machine Learning Techniques for Detecting Phishing URL Attacks

DT Mosa, MY Shams, AA Abohany… - CMC-COMPUTERS …, 2023 - cdn.techscience.cn
Cyber Attacks are critical and destructive to all industry sectors. They affect social
engineering by allowing unapproved access to a Personal Computer (PC) that breaks the …

Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review

S Tyagi, S Szénási - Multimedia Tools and Applications, 2023 - Springer
Human cognitive functions such as perception, attention, learning, memory, reasoning, and
problem-solving are all significantly influenced by emotion. Emotion has a particularly potent …

Artificial Intelligence for Threat Anomaly Detection Using Graph Databases–A Semantic Outlook

EGH Grata, A Deshpande, RT Lopes… - … Analytics and Cyber …, 2024 - Wiley Online Library
Facing the dynamic complex cyber environments, internal and external “cyber threat
intelligence”(CTI), and the snowballing risk of cyberattack, knowledge graphs (KGs) show …

Machine learning for healthcare-IoT security: a review and risk mitigation

M Akhi Khatun, S Farheen Memon, C Eising… - 2023 - researchrepository.ul.ie
The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a
data? driven infrastructure that highly relies on smart sensing devices (ie, blood pressure …

Protecting the Cybersecurity Network Using Lotus Effect Optimization Algorithm Based SDL Model

R Vallabhaneni, HS Nagamani… - 2024 International …, 2024 - ieeexplore.ieee.org
A one-dimensional convolution model serves as the feature extraction and classifier in the
study work's application of the simple deep learning model (SDL). The Lotus Effort …

Cybersecurity Knowledge Graph: Advanced Persistent Threat Organization Attribution

KS Chandra, C Mounika, IVS Kumar… - 2023 Third …, 2023 - ieeexplore.ieee.org
The ever-evolving and intricate nature of cyber environments, coupled with the escalating
risk of cyber-attacks, necessitates robust solutions in the realm of cybersecurity. Knowledge …