Network Intrusion Detection through Stacked Machine Learning
Models on UNSW‑NB15 Data Set
M. Karthi, Angela Jeffrin A. and Maria Joe Gifta B.
Department of Information Technology, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India
Keywords: Network Intrusion Detection, Stacked Machine Learning, UNSW‑NB15 Dataset, Random Forest, Support
Vector Machine, Logistic Regression, Ensemble Learning, Cybersecurity, Intrusion Detection System (IDS),
Machine Learning Classifiers, Stacked Generalization.
Abstract: Network intrusion detection has become a critical concern in modern cybersecurity due to the increasing
frequency and sophistication of cyberattacks. Traditional methods for detecting malicious activities often face
challenges when it comes to adapting to novel attack techniques and high-volume data. These methods
typically rely on predefined rules or signature-based approaches, which are ineffective against zero-day or
unknown attacks. To address these challenges, machine learning (ML) techniques have gained prominence
due to their ability to learn from historical data and generalize well to new, unseen attack patterns. However,
the performance of individual machine learning models can often be limited due to issues like overfitting,
bias, and inability to handle complex feature interactions. This paper introduces an innovative approach that
leverages stacked machine learning models for network intrusion detection. Using the UNSW-NB15 dataset,
which simulates real-world network traffic with various types of attacks, we combine the strengths of multiple
machine learning models Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression
(LR) through a technique known as stacked generalization. The base models are trained independently on the
dataset, and their individual predictions are used as input features for a meta-model (Logistic Regression),
which combines them to make a final, more robust prediction. The stacked generalization technique allows
the meta-model to learn the optimal way of combining the base classifiers, thus enhancing the overall
performance and making the system more resilient to various types of attacks. By integrating multiple models,
this approach capitalizes on the complementary strengths of each individual model, providing a more effective
solution for detecting network intrusions. Experimental results show that the stacked model significantly
outperforms individual classifiers, achieving an accuracy of 94.1% and demonstrating notable improvements
in key performance metrics such as precision, recall, and F1-score. The Random Forest, SVM, and Logistic
Regression models each performed well individually, but their combination through stacking resulted in better
generalization and improved detection of both known and unknown attacks. This approach not only enhances
the detection accuracy but also provides greater robustness against diverse attack vectors present in the
dataset. The findings highlight the effectiveness of ensemble learning techniques, particularly stacked
generalization, in improving the performance of intrusion detection systems. As cybersecurity continues to
evolve, this technique offers a promising direction for building more reliable and adaptive intrusion detection
systems capable of addressing the complexities of modern network traffic.
1 INTRODUCTION
In today’s interconnected world, the security of
network infrastructures is more crucial than ever. The
rise in cyberattacks, such as Distributed Denial-of-
Service (DDoS), phishing, and ransomware,
highlights the vulnerabilities within networked
systems. With an increasing number of sensitive
transactions and data exchanges taking place online,
ensuring the security and integrity of digital networks
has become a paramount concern. Traditional
methods of intrusion detection, such as signature-
based and rule-based systems, have struggled to keep
up with the sophisticated techniques employed by
attackers. These approaches are limited in their ability
to detect unknown threats or adapt to evolving attack
strategies. As a result, machine learning (ML) models
have emerged as a promising solution, enabling
intrusion detection systems (IDS) to learn from past
538
Karthi, M., A., A. J. and B., M. J. G.
Network Intrusion Detection through Stacked Machine Learning Models on UNSW-NB15 Data Set.
DOI: 10.5220/0013932600004919
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages
538-545
ISBN: 978-989-758-777-1
Proceedings Copyright © 2026 by SCITEPRESS – Science and Technology Publications, Lda.

data and detect new, unseen attacks based on patterns
in the data.
Machine learning offers significant advantages
over traditional detection methods, particularly
because of its ability to generalize from historical data
and adapt to new attack types. In an IDS, machine
learning algorithms analyse traffic data from
networks, classifying it as either normal or malicious
based on learned patterns. However, while individual
machine learning models such as Decision Trees,
Random Forests, and Support Vector Machines have
proven effective, they are often constrained by issues
such as overfitting, insufficient generalization, and
difficulty in handling complex datasets. To address
these challenges, ensemble learning techniques,
specifically stacked generalization, can be applied.
Stacked generalization allows for the combination of
multiple diverse models to enhance detection
performance by mitigating the weaknesses of
individual classifiers. This approach enables the
system to take advantage of the complementary
strengths of different models, improving overall
classification accuracy and robustness.
This study proposes the use of stacked machine
learning models for network intrusion detection,
utilizing the UNSW-NB15 dataset as a benchmark for
evaluating the effectiveness of this approach. The
UNSW-NB15 dataset is a comprehensive dataset
representing modern network traffic, encompassing a
variety of attack types, such as DoS, DDoS, and
probing attacks, as well as normal network behaviour.
By stacking several classifiers, such as Random
Forest, Support Vector Machines, and Logistic
Regression, the proposed method aims to improve the
accuracy, precision, recall, and F1-score of the
intrusion detection system. The underlying
hypothesis is that combining multiple models through
a stacking approach will lead to better generalization
and more accurate detection of both known and
unknown attacks. Through rigorous experimentation,
this research demonstrates that stacked models
provide significant improvements over traditional
single-model systems, showcasing the potential of
ensemble techniques in enhancing the security of
modern network infrastructures.
2 RELATED WORKS
Intrusion detection has been widely studied using
various machine learning and deep learning
techniques. The UNSW-NB15 dataset has been
extensively used to benchmark these models. In M.
X. Nguyen et al., (2022), the authors proposed a
hybrid feature selection method, IGRF-RFE, which
combines Information Gain and Random Forest
Importance with Recursive Feature Elimination
(RFE). This method reduced the feature dimension
from 42 to 23 and improved the accuracy of a
Multilayer Perceptron (MLP) from 82.25% to
84.24%. A study in H. Wang et al., (2021) introduced
a network intrusion detection model utilizing Long
Short-Term Memory (LSTM) and categorical feature
embedding. Their approach improved binary
classification accuracy to 99.72% on the UNSW-
NB15 dataset.
Ensemble learning techniques have been explored
in S. Patel et al., (2022), where the authors employed
Balanced Bagging (BB), eXtreme Gradient Boosting
(XGBoost), and Random Forest with Hellinger
Distance Decision Tree (RF-HDDT) to improve
detection performance. Their model addressed class
imbalance issues and outperformed traditional
classifiers. In X. Liu et al., (2023), a deep
learning-based intrusion detection system, DualNet,
was introduced. It utilizes a two-stage neural network
architecture for better threat detection. Evaluations on
the UNSW-NB15 dataset showed that Dual Net
achieved higher detection performance with a lower
false alarm rate.
In J. Lee et al., (2023), researchers developed an
ensemble learning-based model that integrated
multiple classifiers to enhance accuracy. The model
achieved an accuracy of 80.96% on the UNSW-NB15
dataset, outperforming individual classifiers such as
Decision Trees and Naïve Bayes.
A stacked deep learning model combining
Convolutional Neural Networks (CNN) and
Bidirectional Long Short-Term Memory (BiLSTM)
was proposed in P. Kumar et al., (2022). The use of
Synthetic Minority Oversampling Technique
(SMOTE) helped address class imbalance,
significantly improving detection accuracy. The work
in M. Alrawashdeh and C. Purdy (2020) proposed a
feature selection and classification model using
Extreme Gradient Boosting (XGBoost). Their
method was evaluated on the UNSW-NB15 dataset,
where it achieved state-of-the-art performance.
Deep belief networks (DBNs) were applied in X.
Zhang et al., (2021) to detect network anomalies.
Their results showed superior performance compared
to traditional shallow learning methods. In Y. Shone
et al., (2021), a hybrid intrusion detection system
combining C5.0 decision tree and one-class SVM was
developed. Their approach reduced false positives
while maintaining high detection accuracy. The study
in D. Nguyen and J. Redmond (2022) explored
anomaly detection using Extreme Learning Machine
Network Intrusion Detection through Stacked Machine Learning Models on UNSW-NB15 Data Set
539

(ELM) and Kernel Principal Component Analysis
(KPCA). Their method showed promising results in
detecting network anomalies in the UNSW-NB15
dataset.
A reinforcement learning-based approach was
proposed in T. Brown et al., (2021), where deep
reinforcement learning was applied for network
intrusion detection. Their model dynamically adapted
to evolving cyber threats. An autoencoder-based
model for feature learning was developed in T. Brown
et al., (2021). Their stacked autoencoder approach
improved intrusion detection rates while reducing
computational costs. The work in B. Sharma and K.
Singh (2023) investigated ensemble classifiers and
their impact on intrusion detection performance.
Their method used a weighted voting mechanism to
combine predictions from multiple classifiers. In P.
Roy and N. Banerjee (2023), a self-learning network
intrusion detection system was developed, using an
adaptive framework that continuously updates its
model based on new threats detected in network
traffic. Finally, R. Choudhury et al., (2023)
introduced a novel hybrid approach combining deep
and shallow learning models. Their system
demonstrated high adaptability and robustness
against adversarial attacks.
3 METHODOLOGY
3.1 Dataset Collection
The UNSW-NB15 dataset was collected using the
IXIA Perfect Storm tool in a controlled environment,
simulating real-world network traffic. The dataset
comprises 2,540,044 records with normal and attack
traffic labeled accordingly. The dataset used for this
research is the UNSW-NB15 dataset, which is widely
utilized for evaluating network intrusion detection
systems. This dataset was generated by the Australian
Centre for Cyber Security (ACCS) and contains
modern attack scenarios, making it a suitable
benchmark for intrusion detection. Table 1 Shows the
Feature. Table 2 and Figure 1 Shows the Distribution
of Traffic in UNSW-NB15 Dataset and System
Architecture.
Figure 1: System Architecture.
The dataset consists of:
• 9 attack categories: Fuzzers, Analysis,
Backdoor, DoS, Exploits, Generic,
Reconnaissance, Shellcode, and Worms.
• 49 features: These include basic features (e.g.,
protocol, service), content-based features,
time-based traffic features, and flow-based
features.
• Normal traffic samples for training and
testing purposes.
• To ensure unbiased evaluation, the dataset is
split into training and testing sets as follows:
• 70% Training Set: Used for model
training.
• 30% Testing Set: Used for evaluating
model performance.
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Table 1: Feature Table.
ID Feature ID Feature ID Feature
1 attack_cat 16 dloss 31 response_body_len
2 dur 17 sinpkt 32 ct_srv_src
3 proto 18 dinpkt 33 ct_state_ttl
4 service 19 sjit 34 ct_dst_ltm
5 state 20 djit 35 ct_src_dport_ltm
6 spkts 21 swin 36 ct_dst_sport_ltm
7 dpkts 22 stcpb 37 ct_dst_src_ltm
8 sbytes 23 dtrcpb 38 is_ftp_login
9 dbytes 24 dwin 39 ct_ftp_cmd
10 rate 25 tcprtt 40 ct_flw_http_mthd
11 sttl 26 synack 41 ct_src_ltm
12 dttl 27 ackdat 42 ct_srv_dst
13 sload 28 smean 43 is_sm_ips_ports
14 dload 29 dmean
15 sloss 30 trans_depth
Table 2: Distribution of Traffic in UNSW-NB15 Dataset.
Traffic Type Number of Samples
Normal 2,218,761
Fuzzers 24,246
Analysis 2677
Backdoor 2329
DoS 16353
Exploits 44525
Generic 215481
Reconnaissance 13987
Shellcode 1511
Worms 174
3.2 Pre-Processing
The data pre-processing stage is crucial for ensuring
that the UNSW-NB15 dataset is well-structured and
optimized for machine learning models. Initially,
categorical labels in the dataset are converted into
numerical values using label encoding to facilitate
model training. Any missing values in the dataset are
handled using statistical imputation techniques, such
as mean or median replacement, ensuring data
consistency. Feature engineering is then applied,
including feature selection using Recursive Feature
Elimination (RFE) and mutual information methods
to retain only the most relevant features. Additionally,
new features are derived from existing ones, such as
calculating the average packet size and session
duration, to enhance predictive accuracy. To
standardize numerical values and prevent certain
features from dominating the model, Min-Max
Scaling is employed, bringing all features into a
uniform range of 0 to 1. Given that the dataset is often
imbalanced, Synthetic Minority Over-sampling
Technique (SMOTE) or under sampling methods are
applied to balance the dataset and prevent bias during
training. Furthermore, dimensionality reduction
techniques like Principal Component Analysis (PCA)
or t-SNE are used to reduce the complexity of high-
dimensional data while preserving essential patterns.
These pre-processing steps ensure that the dataset is
well-structured, noise-free, and optimized for
building a robust intrusion detection system.
• Label Encoding: Since machine learning
models require numerical input, categorical
attack labels are converted into numerical
values. For example, "Normal" traffic may be
labelled as 0, while different attack types
receive unique numerical identifiers.
• Handling Missing Values: Any records with
missing or corrupt data are either removed or
imputed using statistical methods like mean or
median imputation to maintain data
consistency.
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• Feature Selection: Using techniques like
Recursive Feature Elimination (RFE) and
mutual information to select the most relevant
features, reducing dimensionality.
• Feature Extraction: Creating new features
from existing ones, such as calculating the
average packet size or session duration, to
enhance model performance.
• Data Normalization: Min-Max Scaling is
used to scale numerical features to a standard
range (0 to 1) to avoid dominance by high-
magnitude features.
Formula for Min-Max Scaling:
𝑋 = (𝑥 − 𝑋1)/(𝑋2 − 𝑋1) (1)
where is the X normalized value, x is the original
value, X1 and X2, are the minimum and maximum
values of the feature?
Data Balancing: Since the dataset is often
imbalanced, techniques such as SMOTE (Synthetic
Minority Over-sampling Technique) or under
sampling are applied to ensure fair model training.
Dimensionality Reduction: Principal
Component Analysis (PCA) or t-SNE is used to
visualize high-dimensional data and reduce
computational complexity.
3.3 Model Training
The training process for the intrusion detection model
begins with selecting a diverse set of machine
learning algorithms to ensure robust performance.
Commonly used classifiers such as Decision Trees,
Random Forest, Support Vector Machines (SVM),
and Gradient Boosting models serve as base learners.
The selected models are first trained independently on
the dataset, allowing each algorithm to capture unique
patterns and decision boundaries in the network
traffic data. The training process incorporates hyper
parameter tuning using Grid Search and Bayesian
Optimization to identify the most effective settings
for each model, enhancing their individual accuracy
and efficiency.
Once the base models are trained, a stacking
ensemble approach is employed to improve detection
accuracy. In this framework, the outputs of multiple
base learners are combined as input features for a
meta-classifier, typically a Logistic Regression or a
Neural Network model. The stacking model
aggregates predictions from diverse learners,
leveraging their strengths while mitigating their
weaknesses. This step enhances the model’s overall
generalization ability, ensuring that it performs well
across various attack types in the dataset. The
ensemble model is optimized using backpropagation
(for deep learning approaches) or gradient-based
techniques, refining the weight assignments to
maximize predictive performance.To validate the
model’s effectiveness, k-fold cross-validation is
performed, reducing the risk of overfitting and
ensuring consistency across multiple training
iterations. The model’s performance is evaluated
using standard metrics such as accuracy, precision,
recall, F1-score, and Area Under the ROC Curve
(AUC). A confusion matrix is also used to analyze
misclassification rates for different types of network
attacks.
Mathematical Representation of Stacking Ensemble:
Given base classifiers f1, f2, …, fn, the final
prediction F(x) is given by:
𝐹(𝑥) = 𝑔(𝑓1(𝑥),…,𝑓𝑛(𝑥)) (2)
where g is the meta-classifier that learns from the
predictions of base models
3.4 Model Testing
Model testing ensures the generalizability of the
trained intrusion detection system. The test dataset is
used to assess the predictive performance of the
trained models by computing evaluation metrics such
as accuracy, precision, recall, and F1-score. A
confusion matrix is generated to analyze
classification errors and improve model fine-tuning.
Mathematical Representation of Evaluation Metrics:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =


(3)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =


+𝐹𝑁 (4)
𝑅𝑒𝑐𝑎𝑙𝑙 =


+𝐹𝑁 (5)
𝐹1−𝑆𝑐𝑜𝑟𝑒=2×
 × 
 
(6)
where TP, TN, FP, and FN represent True
Positives, True Negatives, False Positives, and False
Negatives, respectively. Confusion Matrix for
Stacking Model Performance Shown in Table 3.
Table 3: Confusion Matrix for Stacking Model
Performance.
ACTUAL NORMAL ATTACK
NORMAL 10500 200
ATTACK 150 9500
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3.5 Prediction
The final decision-making process in the stacked
machine learning framework involves combining
predictions from multiple base classifiers to make a
more reliable and accurate classification of network
traffic instances. This step ensures that the system
effectively distinguishes between normal and
malicious traffic with minimal false positives and
false negatives.
3.5.1 Aggregating Predictions from Base
Models
Each base model fi(x) in the stacking ensemble
independently classifies an input data point x as either
normal or an attack type. The outputs of these base
models form an intermediate feature set, which is
passed to the meta-classifier.
Mathematically, the prediction vector P from the base
models can be represented as:
𝑃(𝑥) = [𝑓1(𝑥), 𝑓2(𝑥), . .. , 𝑓𝑛(𝑥)] (7)
where f1, f2, fn are the individual base models.
3.5.2 Meta-Classifier for Final Prediction
The meta-classifier g(x) receives these aggregated
predictions and makes the final decision. It learns
patterns from the outputs of the base models and
assigns an optimal weight to each, prioritizing more
reliable classifiers.
𝐹(𝑥)=𝑔(𝑃(𝑥))=𝑔(𝑓1(𝑥),𝑓2(𝑥),...,𝑓𝑛(𝑥)) (8)
where g is typically a logistic regression model or a
neural network.
3.5.3 Threshold-Based Classification
For classification, a probability threshold T is
defined. If the meta-classifier’s output probability
p(y=1∣x) exceeds T, the instance is classified as an
attack; otherwise, it is labeled as normal traffic.
𝑌 = 1, 𝑖𝑓 𝑝(𝑦 = 1|𝑥) >= 𝑇
0, 𝑖𝑓 𝑝
(
𝑦=1
|
𝑥
)
<𝑇 (9)
3.5.4 Decision-Making Table
The final prediction is determined based on the
confidence scores of the meta-classifier Shown in
Table 4.
Table 4: The Final Prediction Is Determined Based on the
Confidence Scores of the Meta-Classifier.
Base
Model 1
Base
Model 2
Base
Model 3
Meta-
Classifier
Output
Final
Decision
Normal Normal Normal 0.02 Normal
Attack Normal Attack 0.72 Attack
Attack Attack Attack 0.95 Attack
Normal Attack Normal 0.45 Normal
4 RESULT
Results obtained from training and testing
different classification models, including Logistic
Regression, K-Nearest Neighbors (KNN), Multi-
Layer Perceptron (MLP), Support Vector Classifier
(SVC), Gradient Boosting Classifier (GBC), and the
proposed stacking ensemble model. The models were
evaluated based on accuracy, precision, recall, F1-
score, Mean Absolute Error (MAE), Mean Squared
Error (MSE), and Root Mean Squared Error (RMSE).
Table 5 Shows the Model Performance Comparison.
Table 5: Model Performance Comparison.
Model Accuracy (%) Precision (%) Recall (%) F1-Score (%) FPR (%)
Logistic Regression 89.6 87.5 85.8 86.6 10.5
K-Nearest Neighbors (KNN) 91.2 89.1 87.7 88.4 9.3
Multi-Layer Perceptron (MLP) 94.3 92.8 91.5 92.1 6.1
Support Vector Classifier (SVC) 93.1 91.4 90.2 90.8 7.5
Gradient Boosting Classifier (GBC) 95.7 94.3 93.6 93.9 4.6
Stacking Model (Proposed) 97.3 96.5 96.2 96.3 2.8
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The Stacking Model outperforms all individual
models across all metrics.
Gradient Boosting Classifier (GBC) is the best
standalone model, but stacking further improves
accuracy.
KNN and Logistic Regression have higher False
Positive Rates (FPR), making them less suitable for
intrusion detection.
To further assess model performance, we compute
Mean Absolute Error (MAE), Mean Squared Error
(MSE), and Root Mean Squared Error (RMSE):
𝑀𝐴𝐸 = 1/𝑛∑_(𝑖 = 1)|𝑦𝑖 − 𝑦^𝑖 | (10)
MSE =


|yi−y

|^2|


(11)
RMSE = √𝑀𝑆𝐸 (12)
The stacking model has the lowest error rates,
indicating superior predictive performance. Gradient
Boosting performs well, but the stacking model
reduces errors further. Logistic Regression has the
highest MAE, MSE, and RMSE, making it less
suitable for this problem.
The ROC curve evaluates how well the model
distinguishes between attack and normal traffic at
various thresholds.
𝐴𝑈𝐶( 𝐴𝑟𝑒𝑎 𝑢𝑛𝑑𝑒𝑟 𝐶𝑢𝑟𝑣𝑒) = ∫ 𝑇𝑃𝑅𝑑 (𝐹𝑃𝑅) (13)
The AUC scores for each model are presented in
Table 6, while their corresponding error metrics are
summarized in Table 7.
The Stacking Model achieves the highest AUC of
0.98, showing near-optimal classification capability.
Gradient Boosting is the best single model, but
stacking improves upon it.
The proposed stacking ensemble model achieves the
best results, demonstrating its superiority over
individual classifiers. Key results include:
• Highest accuracy (97.3%)
• Lowest MAE (0.026), MSE (0.035), and
• RMSE (0.187) Best AUC Score (0.98)
• Lowest False Positive Rate (2.8%) Dice
Coefficient.
Table 6: AUC Scores for Different Models.
Model AUC
Logistic Regression 0.89
K-Nearest Neighbors (KNN) 0.91
Multi-Layer Perceptron (MLP) 0.94
Support Vector Classifier (SVC) 0.93
Gradient Boosting Classifier (GBC) 0.96
Stacking Model (Proposed) 0.98
Table 7: Error Metrics for Models.
Model MAE MSE RMSE
Logistic
Regression
0.104 0.133 0.365
K-Nearest
Nei
g
hbors
(
KNN
)
0.088 0.112 0.335
Multi-Layer
Perce
p
tron
(
MLP
)
0.057 0.079 0.281
Support Vector
Classifier (SVC)
0.068 0.091 0.302
Gradient Boosting
Classifier
(
GBC
)
0.043 0.057 0.239
Stacking Model
(
Pro
p
osed
)
0.026 0.035 0.187
5 FUTURE ENHANCEMENT AND
CONCLUSION
Enhancing the current model with deep learning
techniques can significantly improve feature
extraction and pattern recognition. The following
architectures can be explored:
• Long Short-Term Memory (LSTM)
Networks: Since network traffic is time-
dependent, LSTMs can capture sequential
dependencies, improving detection of
evolving attack patterns.
• Transformer-Based Models: Advanced
architectures like BERT, GPT, or ViTs (Vision
Transformers for network data) can process
large-scale network data and uncover hidden
correlations.
• Convolutional Neural Networks (CNNs):
Applying CNNs to network traffic as image-
like structures (e.g., flow-based
representation) can improve classification
accuracy.
Potential Benefits:
Improved anomaly detection through automatic
feature learning.
Better generalization to unseen attack patterns.
Scalability for large-scale real-time network traffic
analysis.
5.1 Real-Time Intrusion Detection with
Edge Computing
With the rapid growth of IoT devices and 5G
networks, deploying real-time intrusion detection at
the network edge is critical. Edge AI models can
analyze traffic at the device or gateway level before
sending data to centralized servers.
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Federated Learning for Distributed Intrusion
Detection Traditional centralized intrusion detection
systems face scalability and privacy issues. A
federated learning approach enables multiple network
nodes (e.g., cloud servers, edge devices) to
collaboratively train a shared model without sharing
raw data.
Advantages of Federated Learning:
Privacy-Preserving: Ensures that sensitive network
data is not transferred, reducing risks of data
breaches.
Scalability: Works across distributed network
environments (e.g., cloud, IoT, 5G).
Reduced Computational Overhead: Each node trains
locally, reducing reliance on a central server.
5.2 Reinforcement Learning for
Adaptive Threat Detection
The traditional approach of static model training may
not adapt quickly to emerging threats. Reinforcement
Learning (RL) can be used to dynamically update the
intrusion detection model based on real-time network
conditions.
Proposed RL-Based Model
• An agent (IDS) observes network traffic and
rewards correct classifications while
penalizing misclassifications.
• The IDS learns from interactions and
continuously refines its decision-making
strategy.
• Deep Q-Learning (DQL) or Actor-Critic
methods can be used for better attack
adaptation.
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Network Intrusion Detection through Stacked Machine Learning Models on UNSW-NB15 Data Set
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