How to Implement Network Behavioral Detection in OMNeT++
To implement the network behavioral detection in OMNeT++ requires a network which observes the characteristics of network nodes and traffic patterns to detect anomalies that represents security threats or other unusual activities. This detection is commonly used to detect advanced threats like zero-day attacks or insider threats however we may not be able to identify them through signature-based detection techniques. Use the step-by-step guide on how to implement the behavioral detection in OMNeT:
Steps to Implement Network Behavioral Detection in OMNeT++
- Define the Network Environment:
- Build a network that contains different nodes like workstations, servers, and routers. Contain a Behavioral Detection System (BDS) module that will observe the network for anomalous behavior.
simple WorkstationModule
{
parameters:
@display(“i=block/pc”);
gates:
inout ethg;
}
simple ServerModule
{
parameters:
@display(“i=block/server”);
gates:
inout ethg;
}
simple RouterModule
{
parameters:
@display(“i=block/router”);
gates:
inout ethg;
}
simple BehavioralDetectionModule
{
parameters:
@display(“i=block/shield”);
gates:
inout monitorGate;
}
network BehavioralDetectionNetwork
{
submodules:
workstation: WorkstationModule;
server: ServerModule;
router: RouterModule;
bds: BehavioralDetectionModule;
connections:
workstation.ethg <–> router.ethg[0];
server.ethg <–> router.ethg[1];
router.ethg[2] –> bds.monitorGate; // Mirror traffic to the BDS
}
- Simulate Normal and Malicious Behaviors:
- Simulate both normal network operations and abnormal activities that could be indicative of malicious behavior like unusual access patterns, high traffic volumes, or unexpected communication amongst nodes.
class WorkstationModule : public cSimpleModule {
protected:
virtual void initialize() override {
// Start generating traffic
scheduleAt(simTime() + par(“startTime”), new cMessage(“generateTraffic”));
}
virtual void handleMessage(cMessage *msg) override {
if (strcmp(msg->getName(), “generateTraffic”) == 0) {
generateTraffic();
scheduleAt(simTime() + par(“interval”), msg);
} else {
cPacket *pkt = check_and_cast<cPacket*>(msg);
processPacket(pkt);
delete pkt;
}
}
void generateTraffic() {
// Normal traffic simulation
cPacket *normalPkt = new cPacket(“normalTraffic”);
send(normalPkt, “ethg$o”);
// Simulate abnormal/malicious traffic
if (uniform(0, 1) < par(“anomalousProbability”)) {
cPacket *anomalousPkt = new cPacket(“anomalousTraffic”);
anomalousPkt->addPar(“isAnomalous”) = true;
send(anomalousPkt, “ethg$o”);
EV << “Simulating anomalous behavior” << endl;
}
}
void processPacket(cPacket *pkt) {
EV << “Packet received: ” << pkt->getName() << endl;
}
};
- Implement Behavioral Detection Logic:
- Build the Behavioral Detection Module (BDS) that keep an eye on network traffic and detects anomalies depends on predefined rules, statistical analysis, or machine learning models. The BDS will analyze traffic patterns and compare them against normal behavior baselines.
class BehavioralDetectionModule : public cSimpleModule {
private:
int detectedAnomalies = 0;
std::map<std::string, int> behaviorProfile;
protected:
virtual void handleMessage(cMessage *msg) override {
cPacket *pkt = check_and_cast<cPacket*>(msg);
if (detectAnomaly(pkt)) {
detectedAnomalies++;
EV << “Anomalous behavior detected: ” << pkt->getName() << endl;
} else {
updateBehaviorProfile(pkt);
}
delete pkt;
}
bool detectAnomaly(cPacket *pkt) {
std::string pktType = pkt->getName();
if (pkt->par(“isAnomalous”).boolValue()) {
return true;
}
// Example: Anomaly detection based on frequency of packet types
if (behaviorProfile[pktType] > 5) {
return true;
}
return false;
}
void updateBehaviorProfile(cPacket *pkt) {
std::string pktType = pkt->getName();
behaviorProfile[pktType]++;
EV << “Updated behavior profile for packet type: ” << pktType << endl;
}
virtual void finish() override {
recordScalar(“Detected Anomalies”, detectedAnomalies);
EV << “Total detected anomalies: ” << detectedAnomalies << endl;
}
};
- Train and Update Behavioral Profiles:
- Initially, to establish a baseline of normal network behavior, the BDS may operate in a learning mode. After the baseline is established, the system can shift to detection mode, where deviations from the baseline are flagged as potential anomalies.
void updateBehaviorProfile(cPacket *pkt) {
std::string pktType = pkt->getName();
if (behaviorProfile.find(pktType) == behaviorProfile.end()) {
behaviorProfile[pktType] = 0; // Initialize the profile
}
behaviorProfile[pktType]++;
}
- Implement Response Mechanisms:
- Once an anomaly is detected, the BDS can trigger response mechanisms like notifying administrators, logging the event, or taking automatic actions like blocking suspicious traffic or quarantining affected nodes.
class ResponseModule : public cSimpleModule {
protected:
virtual void handleMessage(cMessage *msg) override {
cPacket *pkt = check_and_cast<cPacket*>(msg);
if (pkt->par(“isAnomalous”).boolValue()) {
// Respond to the detected anomaly
EV << “Blocking anomalous packet: ” << pkt->getName() << endl;
delete pkt;
} else {
send(pkt, “ethg$o”);
}
}
};
- Simulate and Evaluate the Behavioral Detection System:
- Run the simulation with various scenarios to assess how well the BDS identifies anomalies and how it impacts network performance. Analyze metrics like detection accuracy, false positives, and response times.
virtual void finish() override {
// Collect and record metrics about the behavioral detection system’s performance
}
Example Scenario: Detecting Unusual Traffic Patterns
In a natural scenario, the BDS monitors network traffic and creates a baseline of normal action. When a node starts sending unusually high volumes of traffic or communicating with unusual destinations, the BDS detects this deviation and flags it as a potential anomaly.
This approach will help you implement the Network Behavioral Detection in OMNeT++ and makes you understand when to take action in the network and how to detect them. If needed, we can also provide you with another simulation implementing the network behavioral detection.
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