# Fuzzy Logic Approach to IoC detection

1. **Define IoC Indicators:**
First, for identified indicators we need to provide a suitable representation to be treated as an element in IoC fuzzy sets.

2. **Determine Membership Functions:**
For each IoC indicator, define a membership function. This function assigns a membership value (between 0 and 1) to each indicator, based on how strongly it suggests a possible intrusion. For example, slight deviations in network traffic might have a low membership value, while detection of known malware signatures might have a high membership value.

3. **Implement Fuzzy Logic for Decision Making**
Use fuzzy logic to combine the membership values of different indicators. This can involve:

* Fuzzy Intersection (AND): Computed as  $P \land Q = min(v(P),v(Q))$
* Fuzzy Union (OR):  Computed as  $P \lor Q = max(v(P),v(Q))$
* Fuzzy Implication (IF-THEN): Computed as  $P \to Q = min(1, 1 - v(P)+ v(Q))$ 