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 …
existing research on defense strategies and methodologies in adversarial attacks using …
A Systematic Literature Review on the Methods and Challenges in Detecting Zero-Day Attacks: Insights from the Recent CrowdStrike Incident
The detection of zero-day attacks remains one of the most critical challenges in
cybersecurity. This systematic literature review focuses on the various AI-based methods …
cybersecurity. This systematic literature review focuses on the various AI-based methods …
Advancing Cyber Incident Timeline Analysis Through Rule Based AI and Large Language Models
FY Loumachi, MC Ghanem - arXiv preprint arXiv:2409.02572, 2024 - arxiv.org
Timeline Analysis (TA) plays a crucial role in Timeline Forensics (TF) within the field of
Digital Forensics (DF). It focuses on examining and analyzing time-based digital artefacts …
Digital Forensics (DF). It focuses on examining and analyzing time-based digital artefacts …
Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning
This paper investigates the application of Deep Reinforcement Learning (DRL) for attributing
malware to specific Advanced Persistent Threat (APT) groups through detailed behavioural …
malware to specific Advanced Persistent Threat (APT) groups through detailed behavioural …
[HTML][HTML] Artificial intelligence driven cyberattack detection system using integration of deep belief network with convolution neural network on industrial IoT
Abstract In Industry 4.0, information and communication technology (ICT) was employed in
numerous significant infrastructures, like financial networks, smart factories, and power …
numerous significant infrastructures, like financial networks, smart factories, and power …
Threat Intelligence and Information Sharing
SR Sindiramutty, KRV Prabagaran… - … Generative AI for …, 2024 - books.google.com
Threat intelligence and information sharing are two structural data that cannot be replaced in
cybersecurity as they are the primary defence against various digital threats. The strategic …
cybersecurity as they are the primary defence against various digital threats. The strategic …
Threat Hunting and Behaviour Analysis
SR Sindiramutty, NZ Jhanjhi… - Utilizing Generative AI for …, 2024 - books.google.com
Cybersecurity-the core of operations is represented by threat hunting and behaviour
analysis that help shield digital assets from the constantly defined cyberattacks. Looking for …
analysis that help shield digital assets from the constantly defined cyberattacks. Looking for …
A Review on Generative Intelligence in Deep Learning based Network Intrusion Detection
M Ali, I Udoidiok, F Li, J Zhang - 2024 Cyber Awareness and …, 2024 - ieeexplore.ieee.org
The incorporation of generative intelligence into deep learning-based intrusion detection
systems (IDS) has become a viable method for improving cybersecurity. While existing …
systems (IDS) has become a viable method for improving cybersecurity. While existing …
Federated Learning Models for Intrusion Detection in Industrial IoT Networks
The Industrial Internet of Things has emerged as an essential tool for building Industry 4.0
and Industry 5.0 where timely information can be retrieved from different scenarios. These …
and Industry 5.0 where timely information can be retrieved from different scenarios. These …
Introduction to Generative AI in Cybersecurity
The intent of this chapter is to introduce the reader to the foundations upon which Generative
Artificial Intelligence (GenAI) is slowly revolutionizing the field of cybersecurity. Over the next …
Artificial Intelligence (GenAI) is slowly revolutionizing the field of cybersecurity. Over the next …