Asynchronous advantage actor-critic (a3c) learning for cognitive network security
Undoubtedly, the recent implacable, widespread, and intricate cyber-attacks demand
cognitive cyber-defense techniques. Although machine learning (ML) and artificial intel …
cognitive cyber-defense techniques. Although machine learning (ML) and artificial intel …
Development of System for Detection and Prevention of Cyber Attacks Using Artifıcial Intelligence Methods
G Abdiyeva-Aliyeva, M Hematyar… - 2021 2nd Global …, 2021 - ieeexplore.ieee.org
Artificial intelligence (AI) technologies have given the cyber security industry a huge
leverage with the possibility of having significantly autonomous models that can detect and …
leverage with the possibility of having significantly autonomous models that can detect and …
Strengthening IDS against evasion attacks with GAN-based adversarial samples in SDN-enabled network
CPX Qui, DH Quang, PT Duy… - 2021 RIVF International …, 2021 - ieeexplore.ieee.org
With the spread of the number of smart devices in the context of Smart City, Software
Defined Networking (SDN) is considered as a vital principle to manage a large-scale …
Defined Networking (SDN) is considered as a vital principle to manage a large-scale …
Autonomous network cyber offence strategy through deep reinforcement learning
M Sultana, A Taylor, L Li - Artificial Intelligence and Machine …, 2021 - spiedigitallibrary.org
Network defensive cyber operations (DCO) are inherently multi-domain, traversing different
network segments and functional levels that encompass networking devices, protocols …
network segments and functional levels that encompass networking devices, protocols …
A convenient machine learning model for cyber security
In recent years, deep neural network approaches have been generally embraced for AI
undertakings, including characterization. Nonetheless, they were demonstrated to be …
undertakings, including characterization. Nonetheless, they were demonstrated to be …
Packet-level and flow-level network intrusion detection based on reinforcement learning and adversarial training
B Yang, MH Arshad, Q Zhao - Algorithms, 2022 - mdpi.com
Powered by advances in information and internet technologies, network-based applications
have developed rapidly, and cybersecurity has grown more critical. Inspired by …
have developed rapidly, and cybersecurity has grown more critical. Inspired by …
Towards learning-automation IoT attack detection through reinforcement learning
As a massive number of the Internet of Things (IoT) devices are deployed, the security and
privacy issues in IoT arouse more and more attention. The IoT attacks are causing …
privacy issues in IoT arouse more and more attention. The IoT attacks are causing …
Enhanced neural network-based attack investigation framework for network forensics: Identification, detection, and analysis of the attack
S Bhardwaj, M Dave - Computers & Security, 2023 - Elsevier
Network forensics aids in the identification of distinct network-based attacks through packet-
level analysis of collected network traffic. It also unveils the attacker's intentions and …
level analysis of collected network traffic. It also unveils the attacker's intentions and …
BDDTPA: Blockchain-Driven Deep Traffic Pattern Analysis for Enhanced Security in Cognitive Radio Ad-Hoc Networks
Cognitive Radio Ad-hoc Networks (CRAHNs) combines characteristics of ad-hoc networks
with cognitive radios to facilitate a variety of communication scenarios. However, these …
with cognitive radios to facilitate a variety of communication scenarios. However, these …
A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning
The fast growth of Internet of Things (IoT) gadgets and 5G networks has increased linkage
and accessibility. However, growing interconnectivity poses new threat levels in these …
and accessibility. However, growing interconnectivity poses new threat levels in these …