Synthetic Data for Cyber Security

As we all know, bad data can significantly impact the performance of AI models and result in a range of issues, including bias, overfitting, and reduced accuracy. Synthetic data can serve as a solution to this problem. As stated by syntheticus.ai, one of the major benefits of using synthetic data in cybersecurity is that it provides real-time, highly accurate insights into potential threats. By modeling potential threat scenarios with synthetic data, organizations can proactively detect suspicious activity and protect their IT systems from malicious attacks.

Pros and cons

Some use cases of synthetic data in cybersecurity include cracking passwords, product testing, intrusion detection, security awareness training, and steganography resistance.

Despite its benefits, there are also several downsides to using synthetic data, such as limited availability of high-quality data sets, difficulty in estimating accuracy, potential for bias in generated data, potential for misuse or malicious manipulation, lack of skilled professionals, and resource limitations.

How to generate synthetic data?

Synthetic data can be generated through various methods, including Monte Carlo simulation, GANs, and decision tree-based methods. However, real-world data is still necessary for generating, verifying, and validating synthetic data.