The Application of Reinforcement Learning in Cyber Security | 8kSec Blogs

At 8ksec, we are dedicated to developing cutting-edge security technologies that help our clients protect their critical assets. One of the areas we are focused on is the development of a next-generation vulnerability scanning tool.

Vulnerability scanning tools have been around for many years, but despite their widespread use, they still have some limitations. For example, many of these tools use a signature-based approach to identify known vulnerabilities, meaning that they are only effective in detecting vulnerabilities that have already been documented. This makes them powerless in the face of zero-day exploits, which are attacks that take advantage of vulnerabilities that are unknown and have not yet been documented.

We are leveraging reinforcement learning (RL) to train our AI agent to detect vulnerabilities in software code. Reinforcement learning is a type of machine learning that trains an agent to make decisions based on a set of rules. The agent is rewarded for making correct decisions and penalized for making incorrect decisions, allowing it to learn over time and improve its accuracy.

There are a few potential benefits of using RL for cyber security:

Real-time decision making: RL algorithms can make decisions quickly and efficiently, allowing them to respond to cyber threats in real time.

Improved threat detection: RL algorithms can be trained on a large dataset of known threats, allowing them to detect previously unseen threats with high accuracy.

Dynamic adaptation: RL algorithms can adapt to changing environments and threats, making them highly versatile and effective in a constantly evolving cybersecurity landscape.

However, there are also some challenges associated with using RL in cyber security. One of the biggest challenges is the lack of data on real-world cyber threats, which makes it difficult to train RL algorithms effectively. Additionally, there are concerns about the ethics and accountability of AI systems in decision-making related to security and privacy.

Here are some examples of how Reinforcement Learning (RL) can be used to detect vulnerabilities in code:

Vulnerability scanning: An RL-based system can be trained to scan code for vulnerabilities and make recommendations for remediation based on the rewards it receives for correct or incorrect decisions. The system can learn from its mistakes and continually improve its accuracy over time.

Input validation: An RL-based system can be trained to automatically validate user input to ensure that it does not contain any malicious payloads. The system can be rewarded for correctly identifying malicious input and penalized for failing to do so.

Threat modeling: An RL-based system can be trained to identify potential threats in the code and make recommendations for mitigation based on a set of predetermined security objectives. The system can learn to identify and prioritize threats based on their likelihood and impact.

Application security: An RL-based system can be trained to identify potential security vulnerabilities in applications and recommend fixes based on the rewards it receives for correct or incorrect decisions. The system can continually learn from its experience and improve its accuracy over time.

In all of these examples, the RL algorithm would be trained on a large dataset of known vulnerabilities and security issues, allowing it to learn and improve over time. This approach could lead to more accurate and efficient vulnerability detection, compared to traditional rule- based systems.


Here is a simple example of an RL agent using Q-Learning to detect a vulnerability in a Python program:

import random
import numpy as np

# Define the state space
states = ['input_valid', 'input_invalid']

# Define the action space
actions = ['accept', 'reject']

# Define the Q-table
Q = {}
for state in states:
    for action in actions:
        Q[(state, action)] = 0

# Define the learning rate
alpha = 0.8

# Define the discount factor
gamma = 0.95

# Define the exploration rate
epsilon = 0.1

# Function to choose an action based on the current state
def choose_action(state, epsilon):
    if np.random.uniform(0, 1) < epsilon:
        action = random.choice(actions)
        values = [Q[(state, action)] for action in actions]
        action = actions[np.argmax(values)]
    return action

# Function to update the Q-table
def update_Q(state, action, reward, next_state):
    Q[(state, action)] = (1 - alpha) * Q[(state, action)] + alpha * (reward + gamma * np.max([Q[(next_state, a)] for a in actions]))

# Function to implement the RL loop
def run_RL_agent():
    for episode in range(1000):
        state = random.choice(states)
        while state != 'input_invalid':
            action = choose_action(state, epsilon)
            if action == 'accept':
                reward = -1
                next_state = 'input_invalid'
                reward = 1
                next_state = random.choice(states)
            update_Q(state, action, reward, next_state)
            state = next_state

# Run the RL agent

In this example, the RL agent is trying to learn how to detect a vulnerability in a Python program by making decisions based on the current state of the program and the rewards it receives. The state space consists of two states: input_valid and input_invalid. The action space consists of two actions: accept and reject. The Q-table is used to store the Q-values for each state-action pair. The Q-values are updated using the Q-Learning algorithm, which updates the Q-values based on the reward received and the maximum Q-value of the next state. The choose_action function chooses an action based on the current state and the exploration rate. The update_Q function updates the Q-table based on the current state, action, reward, and next state. The run_RL_agent function implements the RL loop, running the agent for 1000 episodes and updating the Q-table based on the rewards received.


Here is an example of detecting a directory traversal vulnerability using Reinforcement Learning (RL):

import os
import random

class VulnerableCode8ksec:
    def __init__(self):
        self.root_dir = '/var/www'

    def read_file(self, file_path):
        full_path = os.path.join(self.root_dir, file_path)
            with open(full_path, 'r') as f:
        except Exception as e:
            return 'Error: ' + str(e)

class RLAgent8ksec:
    def __init__(self):
        self.vulnerable_code = VulnerableCode8ksec()

        # Define the state space
        self.states = ['not_traversed', 'traversed']

        # Define the action space
        self.actions = ['run_scan', 'skip_scan']

        # Define the initial state
        self.state = 'not_traversed'

        # Define the Q-table with default values of 0
        self.q_table = {}
        for s in self.states:
            for a in self.actions:
                self.q_table[(s, a)] = 0

        # Set the learning rate and discount factor
        self.learning_rate = 0.8
        self.discount_factor = 0.95

        # Define the maximum number of episodes
        self.max_episodes = 1000

    def run_scan(self):
        test_file = '../../etc/passwd'
        output = self.vulnerable_code.read_file(test_file)
        if 'root:' in output:
            return 'traversed'
            return 'not_traversed'

    def learn(self):
        for episode in range(self.max_episodes):
            # Choose an action based on the current state
            if random.uniform(0, 1) < 0.5:
                action = 'run_scan'
                next_state = self.run_scan()
                action = 'skip_scan'
                next_state = self.state

            # Evaluate the action and reward
            if self.state == 'not_traversed':
                if action == 'run_scan':
                    reward = -1
                    reward = 0
                if action == 'run_scan':
                    reward = 100
                    reward = -100

            # Update the Q-table
            q_value = self.q_table[(self.state, action)]
            max_q_value = max(self.q_table[(next_state, a) for a in self.actions])
            self.q_table[(self.state, action)] = q_value + self.learning_rate * (reward + self.discount_factor * max_q_value - q_value)

            # Update the current state
            self.state = next_state

        # Choose the best action for the final state
        best_action = max(self.actions, key=lambda x: self.q_table[(self.state, x)])

The code demonstrates how to detect a directory traversal vulnerability using Reinforcement Learning (RL). The code consists of two classes: VulnerableCode8ksec and RLAgent8ksec.

The VulnerableCode8ksec class represents a vulnerable piece of code that could be susceptible to a directory traversal attack. It has a read_file method that takes a file_path parameter, joins it with the root directory (/var/www), and opens the file for reading.

The RLAgent8ksec class represents the Reinforcement Learning (RL) agent that will detect the vulnerability in the code. The agent has the following steps:

Define the state space: The state space is defined as two states: not_traversed and traversed.

Define the action space: The action space is defined as two actions: run_scan and skip_scan.

Define the initial state: The initial state is set to not_traversed.

Define the Q-table: The Q-table is a dictionary that stores the Q-values for each state-action pair. The Q-table is initialized with all values set to 0.

Set the learning rate and discount factor: The learning rate and discount factor control how the agent updates the Q-table. The learning rate determines how much weight to give to the new reward, and the discount factor determines how much weight to give to future rewards.

Define the maximum number of episodes: The maximum number of episodes determines the maximum number of iterations the agent will run to detect the vulnerability.

Implement the run_scan method: The run_scan method tests the vulnerability by reading the /etc/passwd file using the read_file method of the VulnerableCode8ksec class. If the output contains the string root:, the method returns the traversed state. Otherwise, it returns the not_traversed state.

Implement the learn method: The learn method implements the Reinforcement Learning (RL) algorithm to detect the vulnerability. It does the following steps for each episode:

Choose an action based on the current state: The agent randomly chooses an action with a 50% probability of choosing run_scan and 50% probability of choosing skip_scan . If the action is run_scan, the next state is determined by the run_scan method. If the action is skip_scan, the next state remains the same.

Evaluate the action and reward: The agent evaluates the action and assigns a reward based on the current state and the chosen action. If the state is not_traversed, a reward of -1 is given for the run_scan action and a reward of 0 is given for the skip_scan action. If the state is traversed, a reward of 100 is given for the run_scan action and a reward of -100 is given for the skip_scan action.

Update the Q-table: The agent updates the Q-table by updating the Q-value for the current state-action pair. The Q-value is updated using the following formula: `q_value = q_


Reinforcement learning (RL) has some limitations when it comes to detecting vulnerabilities in software:

Complexity: The development and training of an RL agent to detect vulnerabilities in software can be a complex and time-consuming task. It requires a deep understanding of both RL algorithms and software security.

Limited applicability: RL is best suited to problems that involve making a sequence of decisions based on rewards. This makes it well-suited to testing software for vulnerabilities, but less well-suited to other aspects of software security, such as authentication and access control.

Lack of precise knowledge: RL agents make decisions based on the current state of the environment and the rewards they receive. However, in many cases, the precise relationships between the state of the environment and the vulnerabilities being tested may not be well understood. This can lead to suboptimal performance or incorrect decisions by the agent.

Difficulty in defining reward functions: Defining a reward function that accurately incentivizes the agent to identify vulnerabilities is challenging. If the reward function is not well-designed, the agent may make incorrect decisions or miss vulnerabilities.

Data requirements: Training an RL agent requires a large amount of data to be collected and processed. This data must be representative of the software being tested and the vulnerabilities.

Real-world testing: In some cases, it may not be possible to fully test an RL agent in a real-world environment, which can lead to potential limitations in its ability to detect vulnerabilities.

Overall, while RL has potential applications in the area of software security, it is important to carefully consider its limitations and limitations of the individual tools and techniques before deciding to use it for detecting vulnerabilities in software. It may be more appropriate to use other methods, such as code analysis, testing, or formal verification, depending on the specific requirements and limitations of the project.

At 8ksec, we are committed to helping organizations achieve the highest levels of cybersecurity. Our research and development into the next generation of vulnerability scanning tools is just one example of our commitment to this mission. We look forward to bringing these innovative technologies to market and helping our clients stay ahead of the latest cyber threats.


Visit our training page if you’re interested in learning more about these techniques and developing your abilities further. Additionally, you may look through our Events page and sign up for our upcoming Public trainings. 

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