Machine-learning-based darknet traffic detection system for IoT applications
The massive modern technical revolution in electronics, cognitive computing, and sensing
has provided critical infrastructure for the development of today's Internet of Things (IoT) for a …
has provided critical infrastructure for the development of today's Internet of Things (IoT) for a …
Sok: An evaluation of the secure end user experience on the dark net through systematic literature review
The World Wide Web (www) consists of the surface web, deep web, and Dark Web,
depending on the content shared and the access to these network layers. Dark Web consists …
depending on the content shared and the access to these network layers. Dark Web consists …
[HTML][HTML] Darknet traffic classification and adversarial attacks using machine learning
N Rust-Nguyen, S Sharma, M Stamp - Computers & Security, 2023 - Elsevier
The anonymous nature of darknets is commonly exploited for illegal activities. Previous
research has employed machine learning and deep learning techniques to automate the …
research has employed machine learning and deep learning techniques to automate the …
DarknetSec: A novel self-attentive deep learning method for darknet traffic classification and application identification
J Lan, X Liu, B Li, Y Li, T Geng - Computers & Security, 2022 - Elsevier
Darknet traffic classification is crucial for identifying anonymous network applications and
defensing cyber crimes. Although notable research efforts have been dedicated to …
defensing cyber crimes. Although notable research efforts have been dedicated to …
Darknet traffic big-data analysis and network management for real-time automating of the malicious intent detection process by a weight agnostic neural networks …
Attackers are perpetually modifying their tactics to avoid detection and frequently leverage
legitimate credentials with trusted tools already deployed in a network environment, making …
legitimate credentials with trusted tools already deployed in a network environment, making …
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 …
Darkdetect: Darknet traffic detection and categorization using modified convolution-long short-term memory
Darknet is commonly known as the epicenter of illegal online activities. An analysis of
darknet traffic is essential to monitor real-time applications and activities running over the …
darknet traffic is essential to monitor real-time applications and activities running over the …
Flow topology-based graph convolutional network for intrusion detection in label-limited IoT networks
Given the distributed nature of the massively connected “Things” in IoT, IoT networks have
been a primary target for cyberattacks. Although machine learning based network intrusion …
been a primary target for cyberattacks. Although machine learning based network intrusion …
Graph-based solutions with residuals for intrusion detection: The modified e-graphsage and e-resgat algorithms
L Chang, P Branco - arXiv preprint arXiv:2111.13597, 2021 - arxiv.org
The high volume of increasingly sophisticated cyber threats is drawing growing attention to
cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection …
cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection …
Robust stacking ensemble model for darknet traffic classification under adversarial settings
Encrypted traffic tunnelled by Tor or VPN is referred to as darknet traffic. The ability to detect,
identify, and characterize darknet traffic is critical for detecting network traffic generated by a …
identify, and characterize darknet traffic is critical for detecting network traffic generated by a …