Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Event Website
https://github.com/ReneLisasi/Deep_Learning_NAD
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
A model of network anomaly detection capable of detecting a multitude of network attacks. This model is based on the hypothesis that by studying a system’s network records for irregular patterns during system usage, network anomalies can be identified. This model contains information about the type of attacks and metrics. This model is to be used in any type of distributed environment. The general purpose of this model is to detect when an attack is or has happened using deep learning techniques to optimize the training speed, accuracy and robustness of attack detection. This is done to stop the epidemic of attacks that hit companies like GitHub, Nobel Foundation, Vodafone, and some Russian banks [13], with Google being the only company to block the 46 million DDOS attacks per second [13].
Included in
UR-62 Deep Learning Approach to Network Anomaly Detection
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
A model of network anomaly detection capable of detecting a multitude of network attacks. This model is based on the hypothesis that by studying a system’s network records for irregular patterns during system usage, network anomalies can be identified. This model contains information about the type of attacks and metrics. This model is to be used in any type of distributed environment. The general purpose of this model is to detect when an attack is or has happened using deep learning techniques to optimize the training speed, accuracy and robustness of attack detection. This is done to stop the epidemic of attacks that hit companies like GitHub, Nobel Foundation, Vodafone, and some Russian banks [13], with Google being the only company to block the 46 million DDOS attacks per second [13].
https://digitalcommons.kennesaw.edu/cday/Spring_2024/Undergraduate_Research/5