Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
Application of deep reinforcement learning to intrusion detection for supervised problems
M Lopez-Martin, B Carro… - Expert Systems with …, 2020 - Elsevier
The application of new techniques to increase the performance of intrusion detection
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
Adversarial environment reinforcement learning algorithm for intrusion detection
G Caminero, M Lopez-Martin, B Carro - Computer Networks, 2019 - Elsevier
Intrusion detection is a crucial service in today's data networks, and the search for new fast
and robust algorithms that are capable of detecting and classifying dangerous traffic is …
and robust algorithms that are capable of detecting and classifying dangerous traffic is …
Detection of DDOS attack using deep learning model in cloud storage application
In recent years, distributed denial of service (DDoS) attacks pose a serious threat to network
security. How to detect and defend against DDoS attacks is currently a hot topic in both …
security. How to detect and defend against DDoS attacks is currently a hot topic in both …
[PDF][PDF] 网络入侵检测技术综述
蹇诗婕， 卢志刚， 杜丹， 姜波， 刘宝旭 - 信息安全学报, 2020 - jcs.iie.ac.cn
摘要随着互联网时代的发展, 内部威胁, 零日漏洞和DoS 攻击等攻击行为日益增加,
网络安全变得越来越重要, 入侵检测已成为网络攻击检测的一种重要手段. 随着机器学习算法的 …
网络安全变得越来越重要, 入侵检测已成为网络攻击检测的一种重要手段. 随着机器学习算法的 …
Research and challenges of reinforcement learning in cyber defense decision-making for intranet security
In recent years, cyber attacks have shown diversified, purposeful, and organized
characteristics, which pose significant challenges to cyber defense decision-making on …
characteristics, which pose significant challenges to cyber defense decision-making on …
Intrusion detection framework using an improved deep reinforcement learning technique for IoT network
P May raju, GP Gupta - … for Security Applications: Proceedings of ICSCS …, 2022 - Springer
With the exponential increase in usage of the Internet-based services in various smart cities,
and smart environment applications, several cyber-attacks are reported in recent years in …
and smart environment applications, several cyber-attacks are reported in recent years in …
A systematic state-of-the-art analysis of multi-agent intrusion detection
Multi-agent architectures have been successful in attaining considerable attention among
computer security researchers. This is so, because of their demonstrated capabilities such …
computer security researchers. This is so, because of their demonstrated capabilities such …
A graphical and qualitative review of literature on ai-based cyber-threat intelligence (cti) in banking sector
ER Ndukwe, B Baridam - European Journal of Engineering and …, 2023 - ej-eng.org
Cyber threats have become a threat to the banking industry, and resulting in the business
attempting to implement artificial intelligence strategies while build resilient cyber-defense …
attempting to implement artificial intelligence strategies while build resilient cyber-defense …