Anomaly detection in network traffic

Anomaly detection in network traffic is an important task in network security, and the use of anomaly scores and random forest trees can be useful in this regard.

For instance, let’s consider a scenario where a network administrator is monitoring the traffic on their organization’s network. They may want to detect anomalous packets that could potentially be a sign of a cyber attack or other malicious activity.

To achieve this, the administrator can use anomaly scores to detect traffic that deviates significantly from the normal traffic pattern. Anomaly scores are calculated based on statistical models that capture the normal behavior of the network traffic. Any packet that deviates significantly from this normal pattern is flagged as an anomaly.

Additionally, the administrator can use random tree forest, which is a machine learning algorithm that can classify network traffic as normal or anomalous based on various features of the packet. Random tree forest creates multiple decision trees using random subsets of the input features and then aggregates the results to make a final prediction. By training the algorithm on a dataset of normal and anomalous packets, it can learn to accurately identify anomalies.

In combination, anomaly scores and random tree forest can provide a powerful tool for network administrators to identify anomalous packets and respond to potential threats in a timely manner.

Here’s a bullet list of the steps required to use a random forest for the network anomaly detection use case:

• Collect and preprocess a dataset of network packets, including both normal and anomalous packets.
  

• Extract relevant features from the packets, such as packet size, source and destination IP addresses, and protocol type.
  

• Split the dataset into training and testing sets, ensuring that both sets have a similar proportion of normal and anomalous packets.
  

• Train a random forest model on the training set using the extracted features as input and the packet labels (normal or anomalous) as output.
  

• Tune hyperparameters of the random forest model, such as the number of trees and the maximum depth of each tree, using cross-validation on the training set.
  

• Evaluate the performance of the trained model on the testing set by computing metrics such as accuracy, precision, recall, and F1 score.
  

• Deploy the trained model in a network environment, where it can continuously monitor incoming packets and classify them as normal or anomalous based on their features.
  

• Set up alerting and response mechanisms to notify network administrators of any detected anomalies and enable them to take appropriate action.
  

Here is a simple C project that collects network packets and sends them to a REST API for anomaly score analysis:

• Set up the project environment by installing the required libraries and dependencies, such as libpcap for packet capture and libcurl for HTTP requests.
  

• Use libpcap to capture network packets in real-time. This can be done by opening a network interface or reading from a packet capture file.
  

• Parse the captured packets to extract the relevant features, such as source and destination IP addresses, packet size, and protocol type.
  

• Serialize the extracted features into a JSON payload that can be sent to the REST API for analysis.
  

• Use libcurl to make a POST request to the REST API endpoint, passing the JSON payload in the request body.
  

• Receive the response from the REST API, which should include the anomaly score for the packet.
  

• Implement a response handling mechanism that takes appropriate action based on the anomaly score. For example, if the score is above a certain threshold, raise an alert or block the packet.
  

• Continuously repeat steps 2-7 to collect and analyze network packets in real-time.
  

The following is a sample of a C application collecting data and detect trought an API the anomaly score :

#include <stdio.h>
#include <stdlib.h>
#include <pcap.h>
#include <curl/curl.h>
#include <jansson.h>

#define MAX_PACKET_SIZE 65535

// Function to handle HTTP response from the REST API
size_t http_response_callback(char *buffer, size_t size, size_t nmemb, void *userdata) {
    // Parse the JSON response and extract the anomaly score
    json_t *root;
    json_error_t error;

    root = json_loads(buffer, 0, &error);
    if (!root) {
        fprintf(stderr, "Failed to parse JSON response: %s\n", error.text);
        return 0;
    }

    json_t *score = json_object_get(root, "anomaly_score");
    if (!score) {
        fprintf(stderr, "Failed to extract anomaly score from JSON response\n");
        json_decref(root);
        return 0;
    }

    // Do something with the anomaly score, e.g. print it or take action based on a threshold
    double score_value = json_number_value(score);
    printf("Anomaly score: %f\n", score_value);

    json_decref(root);
    return size * nmemb;
}

int main(int argc, char **argv) {
    pcap_t *handle;
    char errbuf[PCAP_ERRBUF_SIZE];
    struct pcap_pkthdr header;
    const u_char *packet;
    char *url = "http://localhost:8000/api/v1/analyze";
    CURL *curl;
    CURLcode res;

    // Initialize libcurl
    curl_global_init(CURL_GLOBAL_ALL);
    curl = curl_easy_init();
    if (!curl) {
        fprintf(stderr, "Failed to initialize libcurl\n");
        return 1;
    }

    // Open the pcap capture file or device
    handle = pcap_open_live("eth0", MAX_PACKET_SIZE, 1, 1000, errbuf);
    if (!handle) {
        fprintf(stderr, "Failed to open pcap capture: %s\n", errbuf);
        return 1;
    }

    // Loop over incoming packets and send them to the REST API for analysis
    while (1) {
        packet = pcap_next(handle, &header);
        if (!packet) {
            continue;
        }

        // Extract relevant features from the packet
        // TODO: implement feature extraction code

        // Serialize the features into a JSON payload
        json_t *payload = json_pack("{s:s, s:i, s:i}", "src_ip", "192.168.0.1", "packet_size", 128, "protocol", 6);
        char *payload_str = json_dumps(payload, JSON_COMPACT);
        json_decref(payload);

        // Set up the HTTP request with the JSON payload as the body
        curl_easy_setopt(curl, CURLOPT_URL, url);
        curl_easy_setopt(curl, CURLOPT_POSTFIELDS, payload_str);
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, http_response_callback);

        // Send the HTTP request and wait for the response
        res = curl_easy_perform(curl);
        if (res != CURLE_OK) {
            fprintf(stderr, "Failed to send HTTP request: %s\n", curl_easy_strerror(res));
        }

        // Clean up the JSON payload string
        free(payload_str);
    }

    // Clean up