An enhanced test case prioritisation with cyber-security detection using CMSANN for banking or finance software

In the software development process, software testing executes a program to detect errors. During this process, security attacks may occur, posing significant threats. Incorporating AI into cybersecurity testing processes can help organisations protect sensitive financial data from unauthorised access. So, this research methodology proposed a CMSANN-based cybersecurity prediction for software testing. Initially, the test cases are generated and prioritised using the WW-ArCa-PA approach in the banking or finance application. Then, it enters the data structuring phase. Data are mapped using AKNMHC and reduced using the HS approach in this phase. Then, the features are extracted and given to the cyber-security detection phase. The anomaly dataset is given as input, and pre-processing is performed using MVI, numeralisation, and normalisation. After pre-processing, the features are extracted, and then data are balanced using the ACA-CTSYN approach. The balanced data is given to the CMSANN classifier for cyber security detection.