A survey on encrypted network traffic analysis applications, techniques, and countermeasures
E Papadogiannaki, S Ioannidis - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
The adoption of network traffic encryption is continually growing. Popular applications use
encryption protocols to secure communications and protect the privacy of users. In addition …
encryption protocols to secure communications and protect the privacy of users. In addition …
A review on machine learning–based approaches for Internet traffic classification
Traffic classification acquired the interest of the Internet community early on. Different
approaches have been proposed to classify Internet traffic to manage both security and …
approaches have been proposed to classify Internet traffic to manage both security and …
A novel stacking approach for accurate detection of fake news
With the increasing popularity of social media, people has changed the way they access
news. News online has become the major source of information for people. However, much …
news. News online has become the major source of information for people. However, much …
A hierarchical hybrid intrusion detection approach in IoT scenarios
Internet of Things (IoT) fosters unprecedented network heterogeneity and dynamicity, thus
increasing the variety and the amount of related vulnerabilities. Hence, traditional security …
increasing the variety and the amount of related vulnerabilities. Hence, traditional security …
Didarknet: A contemporary approach to detect and characterize the darknet traffic using deep image learning
Darknet traffic classification is significantly important to categorize real-time applications.
Although there are notable efforts to classify darknet traffic which rely heavily on existing …
Although there are notable efforts to classify darknet traffic which rely heavily on existing …
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 …
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 …
Learning to classify: A flow-based relation network for encrypted traffic classification
W Zheng, C Gou, L Yan, S Mo - Proceedings of The Web Conference …, 2020 - dl.acm.org
As the size and source of network traffic increase, so does the challenge of monitoring and
analyzing network traffic. The challenging problems of classifying encrypted traffic are the …
analyzing network traffic. The challenging problems of classifying encrypted traffic are the …
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 …