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Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection

Hooman Alavizadeh, Julian Jang-Jaccard, Hootan Alavizadeh

arXiv:2111.13978v1cs.CRcs.AI

TL;DR

Advanced cyber threats motivate intrusion-detection systems that can learn autonomously with limited human intervention. The paper combines Q-learning with a deep feed-forward neural network and evaluates hyperparameter tuning on NSL-KDD, reporting effective classification and best performance with discount factor 0.001 under 250 episodes.

  • Problem

    Existing intrusion-detection approaches need more autonomous learning, while prior DRL studies often omit comprehensive details on agent formulation, tuning, and environment interaction.

  • Method

    The proposed DQL combines Q-learning-based reinforcement learning with a deep feed-forward neural network for self-learning network intrusion detection.

  • Results

    The model detects different intrusion classes on NSL-KDD with more than 90% classification accuracy, and the best discount factor is 0.001 with 250 learning episodes.

  • Takeaways & Limitations

    The proposed DQL can learn autonomously from the network environment and classify different network intrusion attack types with high accuracy.

Abstract

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The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor which is set as 0.001 under 250 episodes of training yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.

I. INTRODUCTION

The paper addresses limitations in autonomous intrusion detection by combining Q-learning with a deep feed-forward network and studying hyperparameter tuning for self-learning.

  • Existing intrusion-detection research seeks autonomous agents that require little human intervention and can improve against unfamiliar threats.
  • Conventional reinforcement-learning IDS approaches face uncertainty in recognizing legitimate traffic and state-explosion problems with large learning spaces.
  • Most prior DRL-based IDS studies emphasize detection performance without fully specifying agent formulation, hyperparameter tuning, or environment interaction.
  • The proposed method combines Q-learning-based reinforcement learning with a deep feed-forward neural network for intrusion detection and ongoing auto-learning.
  • The paper details hyperparameter fine-tuning, including learning rates and discount factor, to support more effective self-learning and network-environment interaction.
  • More than 90% accuracy was achieved across classification tasks involving different network intrusion classes on NSL-KDD.

II. RELATED WORK

The paper situates its approach within reinforcement-learning intrusion detection, emphasizing agent-environment interaction, model-free value learning, and deep methods for large state spaces.

  • Q-learning is presented as a model-free approach that can be useful when traditional optimization and supervised learning are not applicable.
  • Prior cybersecurity research has applied deep, supervised, and unsupervised learning across live, real, and simulated environments.
  • Earlier deep reinforcement-learning studies evaluated factors including learning episodes, execution time, cumulative reward, and comparisons with planning-based approaches.
  • Related approaches include router-based reinforcement-learning agents, hidden Markov models for multistep attacks, and decision-theoretic or evolutionary intrusion-response systems.
  • In reinforcement learning, an agent observes a state, selects an action, receives a reward, transitions to a new state, and improves through experience.

B. FEED FORWARD NEURAL NETWORK

The feed-forward neural network serves as the deep reinforcement-learning model’s fully connected Q-value approximator, transforming prepared inputs through layered nonlinear computations.

  • The model uses a fully connected feed-forward neural network to approximate Q-values and train on preprocessed NSL-KDD data.
  • The preprocessing module cleans the intrusion dataset, prepares it, and extracts related features before neural-network training.
  • Fully connected layers link every neuron in one layer to neurons in the previous layer.
  • The network output is represented as a composition of transformations across computational layers, including hidden and output layers.
  • Each perceptron transfers preceding-layer outputs through a nonlinear activation function.
  • Activation functions convert neural-network inputs to outputs; the paper discusses Sigmoid, Tanh, and ReLU.

IV. DATASET

The study uses the labeled NSL-KDD dataset, preprocessing its 41 features numerically and normalizing them for intrusion-detection modeling. The dataset covers normal traffic and four attack classes.

  • NSL-KDD provides 41 features labeled as normal traffic or a specific attack type for intrusion-detection evaluation.
  • The features are converted from categorical values using one-hot encoding and normalized to values between 0 and 1.
  • The dataset features are grouped into basic, content-based, time-based, and host-based traffic categories.
  • NSL-KDD contains five classes: Normal, Denial-of-Service (DoS), Probe, Root to Local (R2L), and Unauthorized to Root (U2R).

V. ANOMALY DETECTION USING DEEP Q LEARNING

The paper formulates intrusion detection as a Deep Q-Learning task in which NSL-KDD features define states and the agent selects classification actions. Rewards are based on agreement between predicted actions and dataset labels.

  • Deep Q-Learning combines Q-learning with a deep neural network to estimate action values for network intrusion detection.
  • The environment uses preprocessed NSL-KDD records, with the first 41 features as states and the label reserved for reward computation.
  • During training, the agent explores the action space and estimates future reward values through interaction with the environment.
  • The DQL agent generates action vectors from neural-network inputs, using final Q-values to judge whether attacks were captured successfully.
  • Correct classifications receive positive rewards, whereas incorrect classifications may receive negative rewards.

C. DEEP Q-LEARNING PROCESS

The DQL process trains a neural-network Q-function over batched NSL-KDD states, balancing exploration and exploitation while updating targets from rewards and next-state predictions. After training, actions are selected by maximum predicted Q-value.

  • The DQN uses NSL-KDD feature values as state variables and a deep neural network to approximate Q-values when a lookup table is impractical.
  • Training initializes model parameters, processes normalized data across episodes, and preserves trained DNN parameters between episode iterations.
  • The network uses four layers with ReLU activations, while each iteration adjusts the DNN-based Q-function approximator.
  • Epsilon-greedy training begins with high exploration and gradually reduces random action selection to emphasize learned actions.
  • The target Q-value combines rewards, discounted future Q-values, and next-state predictions before neural-network loss computation.
  • After training, the action with the maximum Q-value is selected for each state.

A. EXPERIMENT SETUP AND PARAMETERS

The study evaluates a DQL intrusion-detection model on NSL-KDD, treating network features as states and labels as actions, while examining discount-factor effects and standard classification metrics.

  • The model uses NSL-KDD network features as states and intrusion labels as actions within the DQN formulation.
  • Lower discount factors produce lower loss values, with the worst-case loss reaching 1.7 at λ = 0.001 versus 4 at λ = 0.9.
  • The study evaluates Accuracy, Precision, Recall, and F1 score because accuracy alone ignores incorrectly classified samples.
  • Precision measures positive predictions against all predicted positives, whereas Recall measures positive instances against all actual positives.
  • F1 score is the harmonic mean of precision and recall, decreasing significantly when either component is low.

C. PERFORMANCE EVALUATION

The evaluation tests the DQL model on NSL-KDD data across discount factors, intrusion classes, and training durations. Results favor γ = 0.001 and 250 training episodes, while minority-class performance remains weak under class imbalance.

  • The DQN is evaluated on test data after training with the parameters in Table 3 and 200 episodes.
  • For normal, DoS, and Probe classes, γ = 0.001 produces higher true-positive rates than γ = 0.9, while R2L remains very low.
  • After 200 episodes with λ = 0.001, correct estimations are highest for Normal, DoS, and Probe and lower for minority classes.
  • Accuracy rises from 100 to 250 episodes, then declines at 300 episodes, whose training lasts more than 20 minutes.
  • The best reported training setting is 250 episodes, requiring around 17 minutes in the authors’ implementation.
  • All reported performance metrics are higher for the smaller discount factor λ = 0.001 than for the larger value.

D. COMPARISON WITH OTHER APPROACHES

The paper compares DQL with conventional, neural, and hybrid machine-learning approaches on NSL-KDD metrics and training time. DQL reports higher accuracy and shorter training time overall, with class-specific F1 differences across methods.

  • The comparison includes SOM, SVM, Random Forest, Naive Bayes, CNN, BiLSTM, and CNN-BiLSTM models.
  • The models are compared using Accuracy, Recall, F1 Score, Precision, and training time.
  • DQL reports higher accuracy and lower training time than the other approaches in the literature.
  • SVM’s worst reported accuracy is about 68%, while BiLSTM and CNN-BiLSTM report 79% and 83%, respectively.
  • DQL reaches about 81% F1 for the Normal class, below CNN and CNN-BiLSTM at around 90%.
  • DQL performs better on DoS, Probe, and R2L F1 scores than other listed machine-learning and hybrid approaches.

VII. CONCLUSION

The proposed DQL model combines Q-learning with a deep feed-forward neural network to detect and classify network intrusion classes through autonomous, trial-and-error learning. Fine-tuning identified a discount factor of 0.001 with 250 learning episodes as the best configuration, while future work targets realistic cloud deployment.

  • The DQL model takes a labeled dataset as input and uses deep Q networks to detect and classify different network intrusion attack classes.
  • Q-learning is combined with a deep feed-forward neural network so DQL agents can analyze captured traffic and detect malicious payloads without human knowledge.
  • The study fine-tunes DQL parameters, including discount factor, batch size, and learning episodes, to identify self-learning strategies for network intrusion tasks.
  • 0.001 discount factor with 250 learning episodes produced the best reported configuration, while the model learned autonomously and classified different intrusion attack types with high accuracy.
  • Future work plans to deploy the method in a realistic cloud-based environment for real-time threat classification and improved self-learning.
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