Malicious Traffic Identification with Self-Supervised Contrastive Learning [PDF]
Sensors, 2023 As the demand for Internet access increases, malicious traffic on the Internet has soared also. In view of the fact that the existing malicious-traffic-identification methods suffer from low accuracy, this paper proposes a malicious-traffic ...
Jin Yang +4 more
doaj +5 more sources
Semi-Supervised Encrypted Malicious Traffic Detection Based on Multimodal Traffic Characteristics [PDF]
SensorsThe exponential growth of encrypted network traffic poses significant challenges for detecting malicious activities online. The scale of emerging malicious traffic is significantly smaller than that of normal traffic, and the imbalanced data distribution
Ming Liu +3 more
doaj +5 more sources
TSFN: A Novel Malicious Traffic Classification Method Using BERT and LSTM [PDF]
Entropy, 2023 Traffic classification is the first step in network anomaly detection and is essential to network security. However, existing malicious traffic classification methods have several limitations; for example, statistical-based methods are vulnerable to hand-
Zhaolei Shi +3 more
doaj +3 more sources
Malicious traffic detection on sampled network flow data with novelty-detection-based models [PDF]
Scientific Reports, 2023 Cyber-attacks are a major problem for users, businesses, and institutions. Classical anomaly detection techniques can detect malicious traffic generated in a cyber-attack by analyzing individual network packets. However, routers that manage large traffic
Adrián Campazas-Vega +5 more
doaj +3 more sources
Efficient Detection of Malicious Traffic Using a Decision Tree-Based Proximal Policy Optimisation Algorithm: A Deep Reinforcement Learning Malicious Traffic Detection Model Incorporating Entropy. [PDF]
Entropy (Basel)With the popularity of the Internet and the increase in the level of information technology, cyber attacks have become an increasingly serious problem. They pose a great threat to the security of individuals, enterprises, and the state.
Zhao Y, Ma D, Liu W.
europepmc +2 more sources
A Framework for Malicious Traffic Detection in IoT Healthcare Environment. [PDF]
Sensors (Basel), 2021 The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life.
Hussain F +7 more
europepmc +2 more sources
Distributed Malicious Traffic Detection
ElectronicsWith the wide deployment of edge devices, distributed network traffic data are rapidly increasing. Traditional detection methods for malicious traffic rely on centralized training, in which a single server is often used to aggregate private traffic data from edge devices, so as to extract and identify features.
Ying Liu +3 more
openaire +2 more sources
Encrypted Malicious Traffic Detection Based on Word2Vec [PDF]
Electronics, 2022 Network-based intrusion detections become more difficult as Internet traffic is mostly encrypted. This paper introduces a method to detect encrypted malicious traffic based on the Transport Layer Security handshake and payload features without waiting for the traffic session to finish while preserving privacy.
Andrey Ferriyan +3 more
openaire +2 more sources
Malicious Traffic Classification via Edge Intelligence in IIoT
Mathematics, 2023 The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is
Maoli Wang +4 more
doaj +2 more sources
Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning [PDF]
SensorsWith the rapid development of the internet, the increasing amount of malicious traffic poses a significant challenge to the network security of critical infrastructures, including power monitoring systems.
Hao Zhang +6 more
doaj +2 more sources