# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### Threat Simulation Results  
To carry out the simulations, we used a data set that consisted of network traffic logs, security event logs, and user behavior data. The data represent a realistic sample of cyber activity in business environments. The data included network traffic logs that span multiple protocols and data streams, including packet data, session information, and connection logs. The total volume of network traffic data considered was approximately 10 GB. In addition, logs of security events generated by security systems and devices, such as firewalls, intrusion prevention systems, and web servers, were incorporated. These logs included access events, authentication attempts, and alert logs. The total volume of security event logs analyzed was around 5 GB. Logs of user activities within a system were used to simulate threats based on user behavior, including login logs, user actions, and browsing patterns. The total volume of the data was approximately 1 GB. To carry out cyber threat simulations in the cybersecurity test environment, a rigorous methodology was followed that involved several stages:  
* Selection of Threat Types: Various cyber threat types relevant to our analysis were identified. This included DDoS attacks, phishing, malware, SQL injection, and Crosssite Scripting (XSS) attacks. These types represent a diverse sample of threats organizations may face in today's cyber environment. * Simulation Data Collection: To simulate these attacks, we collected and generated simulation data that represented malicious activities and network traffic associated with each type of threat. The data included traffic patterns, malicious network requests, and security event logs that reflected realistic attack behaviors. * Test Environment Configuration: We prepared the cybersecurity test environment, which included detection and response systems, security event logs, network systems, and monitoring tools. We ensured the environment was isolated and controlled to avoid impacts on production systems. * Simulation Execution: Cyber threat simulations used the generated simulation data. During this phase, \"attacks\" were controlled to evaluate the system's ability to detect and respond to these threats. * Recording and Analysis of Results: Each simulation was carefully recorded, including details about attempted attacks, successful or failed detection, and responses implemented by the cybersecurity system. Results were collected and analyzed to evaluate the effectiveness of detection and response tools. * Calculation of Detection and Response Rates: From the recorded data, the detection and successful response rates were calculated for each type of threat. These rates are expressed as percentages and reflect the system's performance in identifying and mitigating hazards. Importantly, all simulations were carried out in a controlled and isolated environment, without affecting production systems or data. This methodology allowed us to effectively evaluate the effectiveness of our cybersecurity tools and obtain the results presented in the previous section. Table 1 summarizes the key results of our cyber threat simulations in our cybersecurity test environment. These simulations were carried out to evaluate the effectiveness of our system's detection and response tools. The results are presented by type of threat and include both the detection rate and the successful response rate. In the Distributed Denial of Service (DDoS) attack simulations, 1000 attack attempts were made. Of these, 950 attempts were successfully detected, resulting in a detection rate of 95%. Furthermore, a successful response was achieved in 94.74% of cases. The Phishing simulations involved 900 attempted phishing attacks. Surprisingly, 880 of these attempts were successfully detected and blocked, leading to an outstanding detection rate of 97.78%. Successful response was achieved in 90.91% of cases. For the Malware simulations, 900 infection attempts were made. Of these, 860 attempts were detected, which represented a detection rate of 95.56%.