Enhancing Data Security: Using Big Data and Reinforcement Learning to Improve Data Security
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The interconnection of billions of devices, as well as the ever-increasing size of Internet-connected devices, provided a large surface area for cyber attacks. In the age of big data and the Internet of Things, data collection volumes are constantly increasing, with some systems absorbing up to a petabyte of security events each day, and ingestion rates are only expected to rise exponentially over time. As the sophistication, volume, and variety of cyber threats grows, so does the need for a strong, data-driven, real-time cyber security defensive plan. RL (Reinforcement Learning) is a popular paradigm for sequential decision making under uncertainty. A typical RL algorithm operates with only limited knowledge of the environment and with limited feedback on the quality of the decisions. To function effectively in complicated settings, learning agents must be capable of forming meaningful abstractions—that is, the ability to disregard unimportant facts selectively. In the world of cybersecurity, RL has proved to be one of the significant methods to encounter cyber attacks. Further, in this paper, we will discuss how RL is using big data to provide data security along with some different models.
Abstract
Paper Outline
Introduction
Data Security
Reinforcement Learning
Big Data Analytics in Cyber Defense
Role of Big Data in Cybersecurity
Predective Models
Automation and Monitoring
3. Applying Big Data and Reinforcement Learning together
Network Intrusion Detection System (NIDS)
Big Data Architecture for Large-Scale Security
Conclusion
References
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