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 …

Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects

IH Sarker - Annals of Data Science, 2023 - Springer
Due to the digitization and Internet of Things revolutions, the present electronic world has a
wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a …

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 …

Machine learning and deep learning methods for cybersecurity

Y Xin, L Kong, Z Liu, Y Chen, Y Li, H Zhu, M Gao… - Ieee …, 2018 - ieeexplore.ieee.org
With the development of the Internet, cyber-attacks are changing rapidly and the cyber
security situation is not optimistic. This survey report describes key literature surveys on …

Enhanced quantum-secure ensemble intrusion detection techniques for cloud based on deep learning

DB Salvakkam, V Saravanan, PK Jain, R Pamula - Cognitive Computation, 2023 - Springer
The increasing popularity of cloud computing systems has drawn significant attention from
academics and businesses for several decades. However, cloud computing systems are …

Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning

P Spadaccino, F Cuomo - arXiv preprint arXiv:2012.01174, 2020 - arxiv.org
Key components of current cybersecurity methods are the Intrusion Detection Systems
(IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can …

A Black‐Box Attack Method against Machine‐Learning‐Based Anomaly Network Flow Detection Models

S Guo, J Zhao, X Li, J Duan, D Mu… - Security and …, 2021 - Wiley Online Library
In recent years, machine learning has made tremendous progress in the fields of computer
vision, natural language processing, and cybersecurity; however, we cannot ignore that …

[PDF][PDF] Network intrusion detection system using deep learning technique

DI Edeh - Master of Science, Department of Computing …, 2021 - utupub.fi
The performance results of the FFDNNs were calculated based on some important metrics
(FPR, FAR, F1 Measure, Precision), and these were compared to the conventional ML …

Toward an exhaustive review on Machine Learning for Cybersecurity

H Bahassi, N Edddermoug, A Mansour… - Procedia Computer …, 2022 - Elsevier
Cyber-attacks are becoming more and more multiple and sophisticated, Causing profound
consequences on humans and their organization. This has led researchers and specialists …

HIDIM: A novel framework of network intrusion detection for hierarchical dependency and class imbalance

W Zhou, C Xia, T Wang, X Liang, W Lin, X Li… - Computers & …, 2024 - Elsevier
Deep learning-based network intrusion detection has been extensively explored as a data-
driven approach. Therefore, paying attention to the data's characteristics is essential. By …