Abstract: In recent years, the scale and performance of data platforms and systems have expanded rapidly, making security performance increasingly critical. Existing malicious behavior detection schemes based on deep learning lack optimization algorithms tailored to the models, resulting in a lack of self-optimization capabilities. This paper proposes a malicious behavior detection method called iFA-LSTM (improved firefly algorithm and improved long short-term memory network), which leverages an improved firefly algorithm and an improved LSTM network to effectively perform binary classification detection of malicious behaviors. The proposed method is validated using the UNSW-NB15 dataset. In single-attack binary classification experiments, the method achieves an average recognition accuracy of 99.56%, while in mixed-attack binary classification experiments, the average recognition accuracy reaches 98.79%. Additionally, the iFA fully demonstrates its effectiveness. The proposed method can detect malicious behaviors quickly and effectively, holding great promise for application in security monitoring and recognition of malicious behaviors.

Key words: platform and system security, malicious behavior detection, neural network, algorithm optimization, binary classification ,