Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention
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Published:2024-05-07
Issue:1
Volume:17
Page:
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ISSN:1875-6883
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Container-title:International Journal of Computational Intelligence Systems
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language:en
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Short-container-title:Int J Comput Intell Syst
Abstract
AbstractThe intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.
Funder
Research on Network Ideological Risks and Prevention Strategies in Xinjiang Universities in the New Era" in 2023
Publisher
Springer Science and Business Media LLC
Reference33 articles.
1. He, W., Ash, I., Anwar, M., Li, L., Yuan, X., Xu, L., Tian, X.: Improving employees’ intellectual capacity for cybersecurity through evidence-based malware training. J. Intellect. Cap. 21(2), 203–213 (2020) 2. De Kimpe, L., Walrave, M., Verdegem, P., Ponnet, K.: What we think we know about cybersecurity: an investigation of the relationship between perceived knowledge, internet trust, and protection motivation in a cybercrime context. Behav. Inform. Technol. 41(8), 1796–1808 (2022) 3. Xu, W., Murphy, F., Xu, X., Xing, W.: Dynamic communication and perception of cyber risk: Evidence from big data in media. Comput. Hum. Behav. 122, 106851 (2021) 4. Xie, Y.X., Ji, L.X., Li, L.S., Guo, Z., Baker, T.: An adaptive defense mechanism to prevent advanced persistent threats. Connect. Sci. 33(2), 359–379 (2021) 5. Mehraj, H., Jayadevappa, D., Haleem, S.L.A., Parveen, R., Madduri, A., Ayyagari, M.R., Dhabliya, D.: Protection motivation theory using multi-factor authentication for providing security over social networking sites. Pattern Recogn. Lett. 152, 218–224 (2021)
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