Revolutionizing Cybersecurity: Behavioral Analysis and Automated Incident Response through Predictive Analytics

Predictive behavioral analytics and automated response (PBAAR) concepts have the potential to revolutionize cybersecurity. The main idea of predictive behavioral analytics is to analyze, extract, and automatically apply behavioral patterns to assess whether a particular activity is malicious. To achieve this goal, simple predictive models built by domain experts need to be developed, understood, and digitally implemented in the form of a sequential approximation of the expert's descriptive models. The expert's logic will be embedded into the constructed predictive model by writing a Python function or defining a decision table. Then, after exposure to examples of the relevant behavior, the resulting model becomes an integral part of real-time predictive analytics characterized by a built-in predictive behavioral task and built-in adaptive machine learning.Predictive behavioral analytics imply that predictive analytics can remove the necessity of the labeled training dataset, and then extraction of the feature subset and training of the classification models. The goal of the developed predictive model within PBAAR should be to automate the detection and resolution of cyber incidents. The key question in automated response is understanding how to define trigger conditions to fire an appropriate set of response activities (responder services, interaction, and decision-making), and how to construct decision tables or Python functions.