To simulate a botnet attack using MATLAB that has designing a network of compromised devices (bots) under the control of an attacker (botmaster). In a botnet attack, bots execute the coordinated activities, like transmitting spam, implementing distributed denial of service (DDoS) attacks, or stealing data. This replication will concentrate on a DDoS attack utilizing a botnet to devastate a target server.
Steps to Simulate a Botnet DDoS Attack in MATLAB
- Describe Network Parameters and Initialize Botnet Nodes
- Replicate the Normal Traffic and Botnet-Controlled Attack Traffic
- Observe Server Performance Under Load
- Execute Basic Detection and Mitigation Strategy
- Envision Server Load Before and After Mitigation
- Define Network Parameters and Initialize Botnet Nodes
Configure the network parameters with the amount of legitimate users, bots, request rates, and server capacity.
% Network Parameters
numLegitimateUsers = 20; % Number of legitimate users
numBots = 50; % Number of bots in the botnet
requestRateUsers = 1; % Request rate per legitimate user (requests per second)
requestRateBots = 5; % Request rate per bot (requests per second)
simulationDuration = 15; % Duration of the simulation (seconds)
serverCapacity = 100; % Maximum requests server can handle per second
- Simulate Normal Traffic and Botnet-Controlled Attack Traffic
Create the traffic from both legitimate users and bots per second of the simulation. Legitimate users will transmit requests at a normal rate, even though bots will overflow the server with high request rates.
% Initialize request counts
legitRequests = zeros(1, simulationDuration); % Legitimate user requests
botRequests = zeros(1, simulationDuration); % Botnet requests
totalRequests = zeros(1, simulationDuration); % Total incoming requests per second
for t = 1:simulationDuration
% Generate requests from legitimate users
legitRequests(t) = poissrnd(numLegitimateUsers * requestRateUsers);
% Generate requests from bots
botRequests(t) = poissrnd(numBots * requestRateBots);
% Calculate total requests
totalRequests(t) = legitRequests(t) + botRequests(t);
end
- Monitor Server Performance Under Load
Verify the server load per second and find out whether it surpasses the server capacity. If it does then the requests out there the capacity are dropped which replicating the overload.
% Server handling and overload detection
processedRequests = zeros(1, simulationDuration); % Successfully processed requests
droppedRequests = zeros(1, simulationDuration); % Dropped requests due to overload
overloadFlag = false(1, simulationDuration); % Flag indicating server overload
for t = 1:simulationDuration
if totalRequests(t) <= serverCapacity
% No overload, process all requests
processedRequests(t) = totalRequests(t);
else
% Overload: process only up to server capacity, drop the rest
processedRequests(t) = serverCapacity;
droppedRequests(t) = totalRequests(t) – serverCapacity;
overloadFlag(t) = true;
end
end
% Display results for each second
disp(‘Time | Legitimate Requests | Bot Requests | Total Requests | Processed | Dropped | Overloaded’);
for t = 1:simulationDuration
fprintf(‘%4d | %18d | %12d | %13d | %9d | %7d | %10s\n’, …
t, legitRequests(t), botRequests(t), totalRequests(t), processedRequests(t), droppedRequests(t), string(overloadFlag(t)));
end
- Implement Basic Detection and Mitigation Strategy
Execute a simple rate-limiting strategy in which each IP (user or bot) can just transmit a limited amount of requests for each second. It supports to detect and limit bot traffic.
% Mitigation Parameters
maxRequestsPerIP = 2; % Maximum requests allowed per IP per second
% Apply mitigation
for t = 1:simulationDuration
% Limit requests from legitimate users and bots
legitRequests(t) = min(legitRequests(t), numLegitimateUsers * maxRequestsPerIP);
botRequests(t) = min(botRequests(t), numBots * maxRequestsPerIP);
% Recalculate total requests and apply server capacity limit
totalRequests(t) = legitRequests(t) + botRequests(t);
if totalRequests(t) <= serverCapacity
processedRequests(t) = totalRequests(t);
droppedRequests(t) = 0;
overloadFlag(t) = false;
else
processedRequests(t) = serverCapacity;
droppedRequests(t) = totalRequests(t) – serverCapacity;
overloadFlag(t) = true;
end
end
- Visualize Server Load Before and After Mitigation
Design the server load, legitimate traffic, and bot traffic, which indicating the influence of the botnet attack and the impact of mitigation.
% Plot results before and after mitigation
figure;
% Total requests and server capacity
subplot(3, 1, 1);
hold on;
plot(1:simulationDuration, totalRequests, ‘-o’, ‘DisplayName’, ‘Total Requests’);
yline(serverCapacity, ‘–r’, ‘Server Capacity’);
title(‘Total Requests vs. Server Capacity (After Mitigation)’);
xlabel(‘Time (s)’);
ylabel(‘Requests per Second’);
legend;
grid on;
% Legitimate vs Bot requests with mitigation
subplot(3, 1, 2);
hold on;
plot(1:simulationDuration, legitRequests, ‘-o’, ‘DisplayName’, ‘Legitimate Requests’);
plot(1:simulationDuration, botRequests, ‘-x’, ‘DisplayName’, ‘Bot Requests’);
title(‘Legitimate vs. Bot Requests (After 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 (After Mitigation)’);
xlabel(‘Time (s)’);
ylabel(‘Dropped Requests’);
grid on;
Explanation of Key Components
- Network Parameters: The network contains both legitimate users and bots. Bots comprising higher request rates to replicate a DDoS attack.
- Server Load Simulation: The server can only process a limited amount of requests for each second; any excess is dropped that mimicking overload.
- Rate Limiting Mitigation: The mitigation approach limits the amount of requests each IP can transmit that minimizing bot traffic even though permitting legitimate users.
- Visualization: The plots displays server load, legitimate traffic, bot traffic, and dropped requests by reason of overload before and after mitigation.
Possible Extensions
- Advanced Detection Mechanisms: Execute the machine learning methods to differentiate among legitimate and bot traffic more exactly.
- Adaptive Rate Limiting: Adapt the rate limit actively depends on server load and traffic analysis to enhance the resilience.
- Command-and-Control Simulation: Design the botnet’s control mechanism replicating attack coordination and bot activation or deactivation.
- Multiple Attack Vectors: Mimic diverse kinds of attacks such as HTTP flood, UDP flood to learn how a multi-faceted DDoS attack impacts the server.
Through the complete manual, we had shown the general concepts and simple procedure with examples using the tool MATLAB for simulating Botnets Attack projects. Furthermore, we will provide additional data regarding to this subject, if required.
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