Attention based multi-agent intrusion detection systems using reinforcement learning
Designing an effective network intrusion system (IDS) is a challenging problem because of
the emergence of a large number of novel attacks and heterogeneous network applications …
the emergence of a large number of novel attacks and heterogeneous network applications …
A context-aware robust intrusion detection system: a reinforcement learning-based approach
Detection and prevention of intrusions in enterprise networks and systems is an important,
but challenging problem due to extensive growth and usage of networks that are constantly …
but challenging problem due to extensive growth and usage of networks that are constantly …
CPSS LR-DDoS detection and defense in edge computing utilizing DCNN Q-learning
Z Liu, X Yin, Y Hu - IEEE Access, 2020 - ieeexplore.ieee.org
Existing intrusion detection and defense models for CPSS (Cyber-Physical-Social Systems)
are based on analyzing the static intrusion characteristics, which cannot effectively detect …
are based on analyzing the static intrusion characteristics, which cannot effectively detect …
A hybrid parallel deep learning model for efficient intrusion detection based on metric learning
With the rapid development of network technology, a variety of new malicious attacks appear
while attack methods are constantly updated. As the attackers exploit the vulnerabilities of …
while attack methods are constantly updated. As the attackers exploit the vulnerabilities of …
A lightweight authentication scheme for telecare medical information system
L Xiao, S Xie, D Han, W Liang, J Guo… - Connection …, 2021 - Taylor & Francis
The rapid development of information technology promotes the development and
application of Telecare Information System (TMIS). However, TMIS also has security …
application of Telecare Information System (TMIS). However, TMIS also has security …
TCN enhanced novel malicious traffic detection for IoT devices
With the development of IoT technology, more and more IoT devices are connected to the
network. Due to the hardware constraints of IoT devices themselves, it is difficult for …
network. Due to the hardware constraints of IoT devices themselves, it is difficult for …
[HTML][HTML] Secure edge computing vulnerabilities in smart cities sustainability using petri net and genetic algorithm-based reinforcement learning
Abstract The Industrial Internet of Things (IIoT) revolution has emerged as a promising
network that enhanced information dissemination about the city's resources. This city's …
network that enhanced information dissemination about the city's resources. This city's …
A comparison of reinforcement learning frameworks for software testing tasks
Software testing activities scrutinize the artifacts and the behavior of a software product to
find possible defects and ensure that the product meets its expected requirements. Although …
find possible defects and ensure that the product meets its expected requirements. Although …
A Systematic Mapping Study on Intrusion Response Systems
A Rezapour, M GhasemiGol, D Takabi - IEEE Access, 2024 - ieeexplore.ieee.org
With the increasing frequency and sophistication of network attacks, network administrators
are facing tremendous challenges in making fast and optimum decisions during critical …
are facing tremendous challenges in making fast and optimum decisions during critical …
Adversarial deep reinforcement learning based adaptive moving target defense
Moving target defense (MTD) is a proactive defense approach that aims to thwart attacks by
continuously changing the attack surface of a system (eg, changing host or network …
continuously changing the attack surface of a system (eg, changing host or network …