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Reinforcement Learning / Safety & Alignment

Automated Cloud Threat Detection Using Reinforcement Learning

Original: Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
By Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan, S M Ahsan Habib, Sonjoy Kumar Dey, Md. Salahuddin Gazi, Molay Kumar Roy, Asaduzzaman Anik Published 2026-08-12

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Key Takeaways

  • The framework achieves a high detection performance with 99.72% accuracy and a 99.54% attack mitigation rate.
  • The system is designed for high-throughput environments, maintaining a detection latency of 15 ms.
  • The model effectively balances sensitivity and precision, yielding a 0.31% false positive rate and a 0.35% false negative rate.
  • The architecture leverages reinforcement learning to create adaptive security policies rather than relying on static rules.

Summary & Methodology Analysis

The researchers developed an adaptive security framework for cloud environments by utilizing a Deep Q-Network (DQN). A DQN is a type of reinforcement learning model where a neural network approximates optimal decision-making policies through trial and error within an environment. The pipeline begins with the preprocessing of network traffic data, followed by feature engineering to optimize the input signals for the model. This setup allows the system to move beyond traditional reactive security measures toward autonomous threat response.

Interactive System Flowchart

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Cross-Examination & FAQs

A deeper dive clarifying mechanics, constraints, and baseline evaluations.

Q1. What is the primary goal of this research?

The goal is to provide adaptable security for cloud infrastructure capable of real-time intrusion detection and autonomous response to complex attacks.

Q2. Does this system require human intervention during an attack?

No, the framework is designed for autonomous threat mitigation.

Q3. How fast is the detection process?

The system maintains a detection latency of 15 ms.

Q4. What datasets were used to validate this model?

The researchers validated the framework using the CICIDS2017 and UNSW-NB15 datasets.

Q5. Which models were used as comparisons?

The study included comparisons against decision tree, support vector machine, random forest, XGBoost, and multilayer perceptron models.

Q6. What were the reported precision and recall metrics?

The DQN achieved 99.68% precision and 99.65% recall.

Q7. Does the paper mention any specific hardware requirements or constraints?

The paper does not specify hardware requirements or constraints.

Q8. What is the false positive rate of the proposed model?

The false positive rate is 0.31%.

Q9. What are the known limitations of this approach?

The paper does not specify any limitations for this approach.

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