【摘要】 近年来,数据平台与系统的规模飞速扩张,性能快速提升,安全性能也随之越发重要。现有的基于深度学习的恶意行为检测方案缺少与模型契合的优化算法,导致模型缺乏自优化能力。提出了一种基于改进萤火虫算法与改进长短期记忆网络的恶意行为检测方法iFA-LSTM,该方法可以有效地进行恶意行为的二分类检测。通过UNSW-NB15数据集来验证所提出的方法,方法在单攻击二分类实验中的平均识别准确率达到了99.56%,且在混合攻击二分类实验中平均识别准确率也达到了98.79%,同时也充分证明了iFA的有效性。所提出的方法可以快速有效地进行恶意行为检测,非常有希望应用于恶意行为的安全监控和识别。更多还原

【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 iFALSTM(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.更多还原