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How should I assess the training of my agent using PPO and Q-learning?

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Urgent !!!!
Hello everybody
I am working on my project to implement a reinforcement learning agent to evaluate the security level of a waf with sql injection.
I started by training two PPO and Q-learning algorithms. I would like you to help me analyze the convergent curve of my models and also if there are parameters to adjust in order to find the right learning rate value.
you will find my code attached and on the image the rewards by episodes
  18 Comments
sidik
sidik on 11 Sep 2024
Hello @Umar
i hope you're fine .
I trained my PPO model on several Learning rates and entropy in order to find the right model which finds the balance between exploration and exploitation, which has a good success rate and a good reward per episode. after analysis I took the model with LR=0.001 and Entropy=0.01. It provides excellent stability while maintaining a good exploration-exploitation balance, high success rate and stable rewards.
with your expertise I would like to have your opinion on this choice and in relation to my curves, if I did a good analysis or not
you will find attached my curves

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Answers (3)

Umar
Umar on 11 Sep 2024

Hi @sidik,

After analyzing your attached plots, your approach to tuning the PPO model by experimenting with different learning rates and entropy values is commendable. You have to bear in mind that the balance between exploration and exploitation is crucial in reinforcement learning, and your choice of parameters reflects a thoughtful analysis of the trade-offs involved. Let me delve deeper into your findings based on the provided plots and the implications of your selected parameters.

Success Rate Analysis

From your first plot (p1.png), you observed the following success rates:

  • PPO (LR=0.0001, Entropy=0.1): Success rate between 32 and 36.
  • PPO (LR=0.001, Entropy=0.3): Success rate between 44 and 46.

The increase in success rate with a higher learning rate (0.001) and a different entropy value (0.3) suggests that the model is effectively learning and adapting to the environment. A higher success rate indicates that the agent is making better decisions, which is a positive outcome. However, it is essential to consider the trade-off with entropy; a higher entropy value typically encourages exploration, which can lead to more diverse actions but may also result in less stable learning.

Reward Variance Analysis

In your second plot (p3.png), the variance in rewards is as follows:

  • PPO (LR=0.001, Entropy=0.3): Reward variance between 4000 and 5000.
  • PPO (LR=0.0001, Entropy=0.1): Reward variance above 16000.

The significant reduction in reward variance when using a learning rate of 0.001 and an entropy of 0.3 indicates that this configuration leads to more consistent performance. High variance in rewards can be detrimental, as it suggests that the agent's performance is unstable and unpredictable. Your choice of parameters appears to have successfully mitigated this issue, leading to a more reliable learning process.

Total Reward Analysis

In the third plot (PPO.png), you noted the total rewards:

  • PPO (LR=0.0001, Entropy=0.01): Total reward between 400 and 450.
  • PPO (LR=0.001, Entropy=0.1): Total reward between 450 and 460.

The increase in total rewards with the learning rate of 0.001 and entropy of 0.1 further supports your decision. Higher total rewards indicate that the agent is not only succeeding more often but is also achieving better outcomes when it does succeed. This is a critical aspect of reinforcement learning, as the ultimate goal is to maximize cumulative rewards. Based on your analysis and the provided plots, your choice of the PPO model with a learning rate of 0.001 and an entropy coefficient of 0.01 appears to be well-founded. The combination of a higher success rate, reduced reward variance, and increased total rewards suggests that you have effectively balanced exploration and exploitation. However, it is essential to remain vigilant and continue monitoring the model's performance over time. Reinforcement learning can be sensitive to hyperparameter choices, and what works well in one scenario may not necessarily hold in another. Consider conducting further experiments with slight variations in the parameters to ensure robustness and to explore the potential for even better performance. In nutshell, your analysis seems thorough, and your selected model parameters are justified based on the observed performance metrics. Keep up the excellent work in refining your PPO model!

  11 Comments
sidik
sidik on 18 Sep 2024
hello @Umar, after read , I tried to summarize the training process of PPO and Q-learning fusion based on the code you sent me. Do you think it summarizes well?
The PPO and Q-Learning fusion training process consists of several key steps. Each module interacts to enhance the agent’s ability to adapt to the WAF’s defenses. Initialization phase: The environment is set up to simulate a system protected by a WAF. The agent starts with a random initial action policy and an empty Q-table. The WAF returns an HTTP response, such as 403 (access denied) or 200 (success), depending on the attack launched. The user provides the URL or IP address of the WAF, which is then analyzed to identify potential injection points (e.g., a login page or a vulnerable parameter). Agent-environment interaction: The agent executes actions based on injected SQL queries, and the WAF responds by allowing or blocking these queries. The PPO algorithm is used to adjust the agent’s policy progressively. Small policy updates allow the exploration of different strategies without altering previously learned actions too much. Simultaneously, Q-Learning memorizes the most effective actions by storing them in a Q-table, assigning values to the actions based on the encountered states. Rewards and adjustments: Each agent action receives a reward based on the WAF response: +10 for a successful attack (HTTP 200 response), 0 for a blocked attack (HTTP 403 response). PPO adjusts the agent action policy based on these rewards, while Q-Learning updates the value of each action in the Q-table. Attack mutation: A mutation module is used to modify SQL queries to generate different attack variants. This allows the testing of several forms of SQL injections and verification of how the WAF reacts to each of them. The agent uses these variants to explore different attack paths, increasing its chances of finding a successful combination. Iterative learning cycle: The process continues in iterative loops, where the agent learns to exploit the weaknesses of the WAF. Thanks to PPO, the agent continues to explore new actions while refining its policy, and Q-Learning memorizes the effective actions to optimize the exploitation of attacks
Umar
Umar on 18 Sep 2024
Hi @sidik,
Your summary accurately encapsulates the training process of a hybrid PPO-Q-Learning agent designed for SQL injection detection against WAFs. The structured breakdown emphasizes each component's role in enhancing overall agent performance while maintaining clarity throughout the training framework. By integrating both methodologies thoughtfully, you can leverage their respective strengths to improve learning outcomes significantly.

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sidik
sidik on 26 Sep 2024
Hello @Umar, I hope you are well?
I trained my Qlearning model and evaluated it according to the following metrics:
awards per episode
success rate
and reward variance
I would like to know for you which is the right model with the right parameter values.
me after my analysis I found
LR = 0.1 with a discount factor of 0.9 and an epsilon decay of 0.999
i upload the image in attached

sidik
sidik on 8 Oct 2024
Hello @Umar, I hope you are doing well. I have finished conducting the experiments for my project, which we discussed, and your help and ideas were invaluable to me. I have prepared a report summarizing my results and I would like to share it with you to get your feedback. This report is important for a presentation I have to make, so your opinion is really important to me.
Thank you
  1 Comment
Umar
Umar on 8 Oct 2024

Hi @sidik,

Please see my comprehensive response below towards attached “reporting.fr.en.pdf”.

PPO Experimentation

Objective & Model Training Parameters: Your choice of PPO is well-justified given its balance between exploration and exploitation. The detailed explanation of hyperparameters such as entropy coefficients, learning rates, and gamma provides clarity on their roles in training stability.

Training Process: The iterative learning process you described effectively highlights how the agent adapts to WAF responses through a mutation mechanism. However, consider including more details on how the mutation process was implemented, as it is pivotal in understanding the exploration strategy.

Results Analysis: The graphs illustrating total rewards and success rates are insightful. The conclusion that a learning rate of 0.001 and an entropy coefficient of 0.01 yield optimal performance is compelling. However, further analysis on why higher entropy values negatively impacted stability would enhance this section.

Q-Learning Experimentation

Objective & Training Parameters: The rationale for using Q-Learning in discrete environments is sound. Your parameter selection reflects an understanding of the trade-offs involved in learning rates and discount factors.

Training Process: The explanation of the epsilon-greedy strategy effectively conveys how exploration is balanced with exploitation. Consider incorporating specific examples or scenarios that demonstrate how the agent adjusted its Q-table based on feedback.

Results Analysis: While you provided a clear overview of cumulative rewards and success rates across different configurations, a deeper discussion on the implications of reward variance would be beneficial—particularly how it relates to stability in attack strategies.

Deep Learning Experimentation

Objective & Architecture: Your choice to utilize a fully connected neural network highlights an advanced approach; however, elaborating on why this architecture was chosen over others (e.g., convolutional or recurrent networks) could strengthen your justification.

Results Analysis: The performance metrics indicate limitations in adaptability and effectiveness against WAFs. It would be valuable to discuss potential reasons for these shortcomings—such as overfitting or insufficient training data—and suggest possible improvements or alternative architectures.

Combination of PPO & Q-Learning

Objective & Justification of Hyperparameters: This section effectively articulates the benefits of combining both algorithms, capturing their strengths while mitigating weaknesses. Including a flowchart of the training process could visually enhance this explanation.

Results Analysis: Your conclusion regarding improved performance through synergy is robust. However, consider discussing how future iterations might further refine this approach—perhaps through advanced hybrid models or by integrating additional RL techniques like actor-critic methods.

Comparative Analysis and Synthesis

Your comparative analysis succinctly summarizes the strengths and weaknesses of each model based on empirical results. Including specific numerical data (e.g., standard deviations for reward variance) would provide a more quantitative foundation for your conclusions. Additionally, suggesting avenues for further research—such as testing additional algorithms or exploring adversarial machine learning techniques—could enhance future work.

It may be beneficial to integrate a discussion on ethical considerations related to using reinforcement learning for penetration testing against WAFs. Addressing potential implications could demonstrate a holistic understanding of cybersecurity practices. Also, incorporating real-world case studies where similar methodologies have been applied could serve as a practical reference point, further validating your findings.

Overall, your report demonstrates thorough experimentation and insightful analysis regarding the application of reinforcement learning to bypass WAF protections against SQL injection attacks. By addressing the aforementioned points, you can enhance clarity and depth while reinforcing the credibility of your findings.

I look forward to seeing how you incorporate this feedback into your final presentation!

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