To simulate Network Threat Detection projects in MATLAB has includes to designing the network traffic, identifying anomalies, and classifying malicious behaviour using statistical techniques, machine learning approaches, or signature-based methods. MATLAB delivers numerous toolboxes, like the Statistics and Machine Learning Toolbox, for executing and validating these detection approaches.
Here’s a step-by-step guide to simulate network threat detection using MATLAB:
Steps to Simulate Network Threat Detection Projects Using MATLAB
- Define Network Traffic Data
The initial step is to replicate or utilize existing network traffic data. We can generate synthetic network traffic by creating an arbitrary packet flows with normal and anomalous patterns. On the other hand, we can utilize real-world datasets, like the KDD Cup 99 dataset or UNSW-NB15, that are commonly utilized for intrusion detection research.
Example: Simulating normal network traffic
% Simulate normal network traffic (packets per second)
normal_traffic = poissrnd(100, 1, 100); % Poisson distribution for 100 seconds
figure, plot(normal_traffic);
title(‘Normal Network Traffic’);
xlabel(‘Time (seconds)’);
ylabel(‘Packets per second’);
Example: Simulating network traffic with anomalies (e.g., DoS attack)
% Simulate an anomaly (DoS attack)
attack_start = 50; % Time when the attack starts
attack_duration = 10; % Duration of the attack in seconds
normal_traffic(attack_start:(attack_start + attack_duration – 1)) = 1000; % High traffic during attack
figure, plot(normal_traffic);
title(‘Network Traffic with DoS Attack’);
xlabel(‘Time (seconds)’);
ylabel(‘Packets per second’);
- Feature Extraction
For efficient detection, it’s significant to extract characteristics from the network traffic. Common types that contain:
- Packet size
- Packet rate (packets per second)
- Duration of connection
- Source/destination IP addresses and ports
- Protocol type (TCP, UDP)
- Flags (SYN, ACK)
Example: Feature extraction from simulated traffic
% Calculate basic features like packet rate
packet_rate = normal_traffic; % Number of packets per second
% Calculate average and standard deviation of the traffic as features
avg_packet_rate = mean(packet_rate);
std_packet_rate = std(packet_rate);
fprintf(‘Average Packet Rate: %.2f packets/second\n’, avg_packet_rate);
fprintf(‘Standard Deviation of Packet Rate: %.2f packets/second\n’, std_packet_rate);
- Anomaly Detection Using Statistical Methods
Statistical approaches such as z-score, thresholding, or moving average can identify the anomalies in network traffic by classifying outliers which alleviate significantly from normal patterns.
Example: Detecting anomalies using z-score
% Compute z-scores for anomaly detection
z_scores = (packet_rate – mean(packet_rate)) / std(packet_rate);
% Set a threshold for detecting anomalies
threshold = 3; % Anomalies are those points with z-scores > 3
anomalies = find(abs(z_scores) > threshold);
% Plot traffic and mark anomalies
figure, plot(packet_rate);
hold on;
plot(anomalies, packet_rate(anomalies), ‘ro’);
title(‘Anomaly Detection in Network Traffic’);
xlabel(‘Time (seconds)’);
ylabel(‘Packets per second’);
legend(‘Normal Traffic’, ‘Anomalies’);
- Intrusion Detection System (IDS) Using Machine Learning
Machine learning models, like decision trees, SVMs, or neural networks, can be trained to differentiate among normal and malicious network characteristics. We want to labeled data (normal vs. attack) to train and test the design.
Example: Training a basic k-NN classifier for intrusion detection
% Generate synthetic training data (100 normal, 100 attack instances)
normal_data = randn(100, 2); % Normal traffic (2 features)
attack_data = randn(100, 2) + 3; % Attack traffic (shifted mean)
% Combine data and create labels (0 = normal, 1 = attack)
data = [normal_data; attack_data];
labels = [zeros(100, 1); ones(100, 1)]; % 100 normal, 100 attack
% Train a k-NN classifier
knn_model = fitcknn(data, labels, ‘NumNeighbors’, 5);
% Simulate new network traffic and classify it
new_data = randn(10, 2); % New network traffic
predicted_labels = predict(knn_model, new_data);
disp(‘Predicted labels for new traffic (0 = normal, 1 = attack):’);
disp(predicted_labels);
- Signature-Based Threat Detection
Signature-based detection that include detecting known attack patterns (signatures) in the network traffic. For instance, specific port numbers, flags, or packet rates can be suggestive of attacks such as DDoS or port scanning.
Example: Detecting DoS attacks based on packet rate threshold
% Define a packet rate threshold for detecting DoS attacks
dos_threshold = 500; % If traffic exceeds 500 packets/second, consider it a DoS attack
% Detect periods where the packet rate exceeds the threshold
dos_attack_periods = find(packet_rate > dos_threshold);
% Plot traffic and mark DoS attack periods
figure, plot(packet_rate);
hold on;
plot(dos_attack_periods, packet_rate(dos_attack_periods), ‘rx’);
title(‘DoS Attack Detection Based on Packet Rate Threshold’);
xlabel(‘Time (seconds)’);
ylabel(‘Packets per second’);
legend(‘Normal Traffic’, ‘DoS Attack Detected’);
- Advanced Machine Learning (Anomaly Detection using Neural Networks)
We can also utilize more advanced approaches such as autoencoders, deep neural networks, or support vector machines (SVM) to identify anomalies or attacks in real-time network traffic.
Example: Anomaly detection using an autoencoder
% Generate training data (normal traffic)
normal_traffic = randn(100, 2); % 2 features (e.g., packet rate, packet size)
% Create and train an autoencoder for anomaly detection
autoenc = trainAutoencoder(normal_traffic, 5, ‘MaxEpochs’, 100);
% Simulate new traffic (with anomalies)
new_traffic = [normal_traffic; randn(10, 2) + 5]; % Add anomalies to the normal traffic
% Test the new traffic using the autoencoder
reconstructed = predict(autoenc, new_traffic);
% Compute reconstruction error (higher error indicates anomaly)
reconstruction_error = sum((new_traffic – reconstructed).^2, 2);
anomaly_threshold = mean(reconstruction_error) + 2 * std(reconstruction_error);
anomalies = find(reconstruction_error > anomaly_threshold);
% Plot the results
figure, plot(reconstruction_error);
hold on;
plot(anomalies, reconstruction_error(anomalies), ‘ro’);
title(‘Anomaly Detection Using Autoencoder’);
xlabel(‘Instance’);
ylabel(‘Reconstruction Error’);
legend(‘Normal Traffic’, ‘Anomalies’);
- Performance Evaluation
After executing the detection mechanism, we should measure its performance using parameters like:
- Accuracy
- Precision
- Recall
- False Positive Rate (FPR)
- Confusion Matrix
Example: Compute accuracy, precision, and recall
% Simulate true labels and predicted labels (0 = normal, 1 = attack)
true_labels = [zeros(50, 1); ones(50, 1)];
predicted_labels = [zeros(45, 1); ones(5, 1); zeros(5, 1); ones(45, 1)];
% Compute confusion matrix
confusion_mat = confusionmat(true_labels, predicted_labels);
disp(‘Confusion Matrix:’);
disp(confusion_mat);
% Calculate precision, recall, and accuracy
TP = confusion_mat(2, 2); % True Positives
FP = confusion_mat(1, 2); % False Positives
FN = confusion_mat(2, 1); % False Negatives
TN = confusion_mat(1, 1); % True Negatives
accuracy = (TP + TN) / sum(confusion_mat(:));
precision = TP / (TP + FP);
recall = TP / (TP + FN);
fprintf(‘Accuracy: %.2f\n’, accuracy);
fprintf(‘Precision: %.2f\n’, precision);
fprintf(‘Recall: %.2f\n’, recall);
Example Project Ideas for Network Threat Detection
- Anomaly-Based Intrusion Detection System (IDS): Improve an IDS using machine learning techniques such as k-NN, SVM, neural networks to identify anomalous behaviour in network traffic.
- DoS/DDoS Attack Detection: Replicate DoS or DDoS attacks and execute detection mechanisms using statistical evaluation or machine learning techniques.
- Signature-Based Malware Detection: Improve a signature-based detection system which detects malware or threats according to known attack patterns such as port scanning, SYN flooding.
- Real-Time Threat Detection Using Deep Learning: Execute a real-time anomaly detection system using deep learning approaches like autoencoders or LSTM networks.
- Network Forensics Tool: Generate a tool which measures network traffic logs to identify malevolent activities and delivers forensic evidence for network security incidents.
- Feature Selection for Network Intrusion Detection: Evaluate the most significant characteristics in network traffic for classifying intrusions using feature selection approaches such as PCA (Principal Component Analysis) or mutual information.
- Botnet Detection: Replicate a botnet attack in a network and improve machine learning designs to identify botnet traffic according to communication patterns.
Datasets for Network Threat Detection:
- KDD Cup 99 Dataset: A broadly utilized dataset for intrusion detection research.
- UNSW-NB15 Dataset: A modern dataset for network intrusion detection encompassing numerous attacks.
- CICIDS2017: An up-to-date dataset encompassing different kinds of network attacks.
Tools and Libraries:
- MATLAB Statistics and Machine Learning Toolbox: For executing machine learning techniques and statistical approaches.
- Deep Learning Toolbox: For executing deep learning models such as autoencoders and neural networks.
- Signal Processing Toolbox: helpful for evaluating and processing network traffic data.
In this report, we had thorough the entire simulation process which understand the concepts and approaches of Network Threat Detection project that were visualized the results and simulated in the MATLAB environment. If you need further details regarding this process we will provide it.
Feel free to reach out to us, and we will ensure you receive exceptional services. We specialize in offering project ideas and conducting performance analyses tailored to your specific interests. At phdprime.com, we are committed to being your premier partner for simulating network threat detection projects using MATLAB. Our dedicated team utilizes a variety of toolboxes, including the Statistics and Machine Learning Toolbox, to deliver the most relevant research topics and ideas.
