A review of generative models in generating synthetic attack data for cybersecurity

G Agrawal, A Kaur, S Myneni - Electronics, 2024 - mdpi.com
The ability of deep learning to process vast data and uncover concealed malicious patterns
has spurred the adoption of deep learning methods within the cybersecurity domain …

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

YL Khaleel, MA Habeeb, AS Albahri… - Journal of Intelligent …, 2024 - degruyter.com
This study aims to perform a thorough systematic review investigating and synthesizing
existing research on defense strategies and methodologies in adversarial attacks using …

Anomaly and intrusion detection using deep learning for software-defined networks: A survey

VG da Silva Ruffo, DMB Lent, M Komarchesqui… - Expert Systems with …, 2024 - Elsevier
Abstract Software-Defined Networks (SDN) represent an adaptable paradigm for dealing
with network users' dynamic demands. Confidentiality, integrity, and availability are …

An unsupervised generative adversarial network system to detect ddos attacks in sdn

DMB Lent, VGDS Ruffo, LF Carvalho, J Lloret… - IEEE …, 2024 - ieeexplore.ieee.org
Network management is a crucial task to maintain modern systems and applications
running. Some applications have become vital for society and are expected to have zero …

Detecting and mitigating security anomalies in software-defined networking (SDN) using gradient-boosted trees and floodlight controller characteristics

T Jafarian, A Ghaffari, A Seyfollahi, B Arasteh - Computer Standards & …, 2025 - Elsevier
Cutting-edge and innovative software solutions are provided to address network security,
network virtualization, and other network-related challenges in highly congested SDN …

Intrusion detection with federated learning and conditional generative adversarial network in satellite-terrestrial integrated networks

W Jiang, H Han, Y Zhang, J Mu, A Shankar - Mobile Networks and …, 2024 - Springer
Network intrusion detection is a challenging network security research topic, especially
when data privacy has become an increasing concern in satellite-terrestrial integrated …

Cyberattack defense mechanism using deep learning techniques in software-defined networks

DS Rao, AJ Emerson - International Journal of Information Security, 2024 - Springer
Software-defined networking (SDN) is a network architecture. It is becoming more popular
due to its centralized network administration, adaptability, and speed. However, the …

A survey of intelligent multimedia forensics for internet of things communications: Approaches, strategies, perspectives, and challenges for a sustainable future

W Ding, M Abdel-Basset, AM Ali, N Moustafa - Engineering Applications of …, 2024 - Elsevier
Digital forensics is a proven method for collecting, preserving, reporting, analyzing,
identifying, and presenting digital evidence from the original data, and it helps find evidence …

Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research …

N Khan, K Ahmad, AA Tamimi, MM Alani… - arxiv preprint arxiv …, 2024 - arxiv.org
Industry 5.0, which focuses on human and Artificial Intelligence (AI) collaboration for
performing different tasks in manufacturing, involves a higher number of robots, Internet of …

Generative AI in Network Security and Intrusion Detection

SR Sindiramutty, KRV Prabagaran… - … With Generative AI …, 2025 - igi-global.com
Protecting virtual assets from cyber threats is essential as we live in a digitally advanced
world. Providing a responsible emphasis on proper network security and intrusion detection …