To simulate a Distributed Denial of Service (DDoS) Attack within MATLAB that can help know how these attacks effect network performance and permit to experiment diverse mitigation approaches. In a DDoS attack, several devices or attackers overflow a target server including excessive requests, devastating it and rendering it inaccessible to legitimate users.
To replicate it in MATLAB, we can configure a design with:
- Legitimate Users and Attackers: Replicate both normal user requests and attack requests.
- Server Queue: A queue for managing incoming requests at the server that contains a maximum capacity.
- Attack Detection and Mitigation: Execute a simple mitigation strategy to block extra requests.
Steps to Simulate a Basic DDoS Attack in MATLAB
- Define Simulation Parameters
Describe metrics for the simulation, like the amount of users, attackers, request rates, and server capacity.
% Simulation Parameters
numLegitimateUsers = 10; % Number of legitimate users
numAttackers = 30; % Number of attackers
requestRateUsers = 1; % Request rate per second per legitimate user
requestRateAttackers = 5; % Request rate per second per attacker
simulationDuration = 10; % Duration of the simulation in seconds
serverCapacity = 20; % Maximum number of requests server can handle per second
- Simulate Requests from Legitimate Users and Attackers
Create requests per second of the simulation, including legitimate users transmitting requests at a lower rate and attackers fowarding requests at a higher rate.
% Initialize request counts
requests = zeros(1, simulationDuration); % Total incoming requests at each second
legitRequests = zeros(1, simulationDuration); % Legitimate user requests
attackRequests = zeros(1, simulationDuration); % Attacker requests
for t = 1:simulationDuration
% Legitimate user requests
legitRequests(t) = poissrnd(numLegitimateUsers * requestRateUsers);
% Attacker requests
attackRequests(t) = poissrnd(numAttackers * requestRateAttackers);
% Total requests to server
requests(t) = legitRequests(t) + attackRequests(t);
end
- Simulate Server Processing and Overload Detection
Verify if the entire requests go beyond the server capacity at each time step, which marking overloaded periods and replicating the dropped requests.
% Server handling and overload detection
processedRequests = zeros(1, simulationDuration); % Requests successfully processed
droppedRequests = zeros(1, simulationDuration); % Requests dropped due to overload
overloadFlag = false(1, simulationDuration); % Flag indicating server overload
for t = 1:simulationDuration
if requests(t) <= serverCapacity
% No overload, all requests are processed
processedRequests(t) = requests(t);
else
% Overload: process only up to server capacity
processedRequests(t) = serverCapacity;
droppedRequests(t) = requests(t) – serverCapacity;
overloadFlag(t) = true;
end
end
% Display results for each time step
disp(‘Time | Legitimate Requests | Attack Requests | Total Requests | Processed | Dropped | Overloaded’);
for t = 1:simulationDuration
fprintf(‘%4d | %18d | %14d | %13d | %9d | %7d | %10s\n’, …
t, legitRequests(t), attackRequests(t), requests(t), processedRequests(t), droppedRequests(t), string(overloadFlag(t)));
end
- Plot the Results
Envision the legitimate requests, attack requests, and dropped requests to monitor how the DDoS attack effects the server over time.
% Plot request types and server response
figure;
% Total requests and server capacity
subplot(3, 1, 1);
hold on;
plot(1:simulationDuration, requests, ‘-o’, ‘DisplayName’, ‘Total Requests’);
yline(serverCapacity, ‘–r’, ‘Server Capacity’);
title(‘Total Requests vs. Server Capacity’);
xlabel(‘Time (s)’);
ylabel(‘Requests per Second’);
legend;
grid on;
% Legitimate vs Attack requests
subplot(3, 1, 2);
hold on;
plot(1:simulationDuration, legitRequests, ‘-o’, ‘DisplayName’, ‘Legitimate Requests’);
plot(1:simulationDuration, attackRequests, ‘-x’, ‘DisplayName’, ‘Attack Requests’);
title(‘Legitimate vs. Attack Requests’);
xlabel(‘Time (s)’);
ylabel(‘Requests per Second’);
legend;
grid on;
% Dropped Requests due to Overload
subplot(3, 1, 3);
bar(1:simulationDuration, droppedRequests);
title(‘Dropped Requests Due to Overload’);
xlabel(‘Time (s)’);
ylabel(‘Dropped Requests’);
grid on;
- Implement a Basic Mitigation Strategy
To mitigate the DDoS attack, limit the amount of requests are permitted for each IP (user) per second. Below is a basic method, which limits each user to one request per second, blocking additional requests from attackers.
% Mitigation Strategy Parameters
maxRequestsPerUser = 1; % Maximum requests per user per second
% Apply mitigation strategy
for t = 1:simulationDuration
% Limit legitimate requests
legitRequests(t) = min(legitRequests(t), numLegitimateUsers * maxRequestsPerUser);
% Limit attacker requests
attackRequests(t) = min(attackRequests(t), numAttackers * maxRequestsPerUser);
% Update total requests and re-evaluate server overload
requests(t) = legitRequests(t) + attackRequests(t);
% Re-evaluate server capacity with mitigation
if requests(t) <= serverCapacity
processedRequests(t) = requests(t);
droppedRequests(t) = 0;
overloadFlag(t) = false;
else
processedRequests(t) = serverCapacity;
droppedRequests(t) = requests(t) – serverCapacity;
overloadFlag(t) = true;
end
end
% Re-plot after mitigation
figure;
% Plot requests with mitigation
subplot(3, 1, 1);
hold on;
plot(1:simulationDuration, requests, ‘-o’, ‘DisplayName’, ‘Total Requests with Mitigation’);
yline(serverCapacity, ‘–r’, ‘Server Capacity’);
title(‘Total Requests vs. Server Capacity (with Mitigation)’);
xlabel(‘Time (s)’);
ylabel(‘Requests per Second’);
legend;
grid on;
% Legitimate vs Attack requests with mitigation
subplot(3, 1, 2);
hold on;
plot(1:simulationDuration, legitRequests, ‘-o’, ‘DisplayName’, ‘Legitimate Requests’);
plot(1:simulationDuration, attackRequests, ‘-x’, ‘DisplayName’, ‘Attack Requests with Mitigation’);
title(‘Legitimate vs. Attack Requests (with Mitigation)’);
xlabel(‘Time (s)’);
ylabel(‘Requests per Second’);
legend;
grid on;
% Dropped requests due to overload with mitigation
subplot(3, 1, 3);
bar(1:simulationDuration, droppedRequests);
title(‘Dropped Requests Due to Overload (with Mitigation)’);
xlabel(‘Time (s)’);
ylabel(‘Dropped Requests’);
grid on;
Explanation of Key Components
- Legitimate Users vs Attackers: The replication differentiates among the legitimate users and attackers, each with diverse request rates.
- Server Overload Detection: The server including a capacity limit; any requests ahead of this limit are dropped, which replicating overload.
- Mitigation Strategy: General rate limiting caps the amount of requests for each user to minimize overload from attackers.
- Visualization: The plots indicate how the DDoS attack influences the server and how the mitigation approach supports handle the traffic.
Possible Extensions
- Advanced Mitigation: Execute more sophisticated DDoS defense methods like IP filtering, machine learning-based anomaly detection, or CAPTCHA.
- Different Attack Types: Replicate other DDoS kinds such as amplification attacks or layer 7 attacks, by changing the attack characteristics.
- Traffic Analysis: Calculate the influence of DDoS attacks on server response time, throughput, and packet loss.
- Adaptive Rate Limiting: Execute dynamic rate limiting according to the real-time traffic analysis for enhanced attack detection and mitigation.
In this setup, we aggregated the innovative information regarding the Distributed Denial of Service (DDoS) Attack projects, which were simulated and extended using MATLAB environment. We furnished to share the additional data regarding this process in further setup.
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