An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

Various attacks have emerged as the major threats to the success of a connected world like the Internet of Things (IoT), in which billions of devices interact with each other to facilitate human life. By exploiting the vulnerabilities of cheap and insecure devices such as IP cameras, an attacker can...

Full description

Bibliographic Details
Main Authors: Ren-Hung Hwang, Min-Chun Peng, Chien-Wei Huang, Po-Ching Lin, Van-Linh Nguyen
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
IoT security
anomaly detection
convolutional neural network
autoendcoder
online DL-based anomaly detection
Online Access:https://ieeexplore.ieee.org/document/8990084/

Internet

https://ieeexplore.ieee.org/document/8990084/

Similar Items