Scalable Cyber Threat Detection with Reward Engineering

Abstract

Scalable cyber threat detection is becoming an indispensable element of modern cybersecurity systems, since the attacks are becoming more sophisticated and frequent. Among the most promising methods for optimizing and improving threat detection systems using machine learning, reward engineering is on top. Through modification of the reward structure in machine learning models, systems learn adaptive and efficient behaviors of how to detect and respond against various cyber threats.

The paper has explained how reward engineering allows developers to scale cyber threat-detecting models by overcoming various challenges: scalability barriers of the model itself, low data quality, the enormous number of computations involved, and poor resistance to security threats/leaks. The above solutions give way to discussing the feasibility detected within scalable cyber threats with reward engineering.