**Figure 8.**

*The data records clustering.*


**Table 3.** *Evaluation metrics.*

As shown in **Table 4** the total input data is 845 721 records, 243 899 records as normal and 601 822 records as intrusion. After applying FCM algorithm, the result is 231 704 record for normal and 589 785 records for intrusion. Then we calculated the normal and intrusion classification rate by the following equation:

$$\text{Classification rate} = \frac{\text{Number of classified patterns}}{\text{Total number of patterns}} \times 100\tag{5}$$

The simulation results show that the classification rate is 96.4% by the FCM algorithm which means that the false positive rate(returns the rate of instances which are falsely classified) is 0.02%.

*Intrusion Detection Based on Big Data Fuzzy Analytics DOI: http://dx.doi.org/10.5772/intechopen.99636*


**Table 4.**

*Evaluation metrics.*
