Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …
As a result, there is an immediate need to protect these networks from a variety of threats …
[PDF][PDF] Deep transfer learning applications in intrusion detection systems: A comprehensive review
Globally, the external Internet is increasingly being connected to the contemporary industrial
control system. As a result, there is an immediate need to protect the network from several …
control system. As a result, there is an immediate need to protect the network from several …
Graph neural networks for intrusion detection: A survey
T Bilot, N El Madhoun, K Al Agha, A Zouaoui - IEEE Access, 2023 - ieeexplore.ieee.org
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
TS-IDS: Traffic-aware self-supervised learning for IoT Network Intrusion Detection
With recent advances in the Internet of Things (IoT) technology, more people can have
instant and easy access to the IoT network of vast and diverse interconnected devices (eg …
instant and easy access to the IoT network of vast and diverse interconnected devices (eg …
A new concatenated multigraph neural network for iot intrusion detection
The last few years have seen a high volume of sophisticated cyber-attacks leading to
financial instability and privacy breaches. This reveals the need for a Network Intrusion …
financial instability and privacy breaches. This reveals the need for a Network Intrusion …
Point cloud analysis for ML-based malicious traffic detection: Reducing majorities of false positive alarms
As an emerging security paradigm, machine learning (ML) based malicious traffic detection
is an essential part of automatic defense against network attacks. Powered by dedicated …
is an essential part of automatic defense against network attacks. Powered by dedicated …
Controlled graph neural networks with denoising diffusion for anomaly detection
Leveraging labels in a supervised learning framework as prior knowledge to enhance
network anomaly detection has become a trend. Unfortunately, just a few labels are typically …
network anomaly detection has become a trend. Unfortunately, just a few labels are typically …
Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy
R Devendiran, AV Turukmane - Expert Systems with Applications, 2024 - Elsevier
Network intrusion is a huge harmful activity to the privacy of the data sharing network. The
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
A Survey on Graph Neural Networks for Intrusion Detection Systems: Methods, Trends and Challenges
Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …
increasing sophistication of cyber attack methods, traditional detection approaches are …
Multi-view graph neural network with cascaded attention for lncRNA-miRNA interaction prediction
H Li, B Wu, M Sun, Y Ye, Z Zhu, K Chen - Knowledge-Based Systems, 2023 - Elsevier
Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs
(miRNAs) reveals the mechanisms of biological processes, thereby contributing to disease …
(miRNAs) reveals the mechanisms of biological processes, thereby contributing to disease …