关键词: 恶意行为检测, 聚类方法, LSTM分类, 注意力机制, DTW算法

Abstract: With the progress and development of society, the safety requirements for public places have further increased. Malicious behavior detection can monitor and identify potential safety hazards in real time. To solve this problem, the Kmeans clustering method is used to divide the molecular data set and distinguish different forms of malicious behavior. To solve this problem, the Kmeans clustering method is used to divide the subdatasets to distinguish different forms of malicious behaviors. The DTW time warping method solves the problem of inconsistent lengths of malicious behavior time series. In order to solve the problem of image recognition, the excessive amount of data in the malicious behavior frame set makes the model calculation accuracy low, and the Attention mechanism is used to focus on special information points to ensure the accuracy of model training. This method was applied to the malicious behavior data set of UBIFights. The results showed that the final classification accuracy of the subdataset after clustering division by weighted average calculation reached 95.03%. This model effectively identifies malicious behavior videos and improves safety.

Key words: malicious behavior detection, clustering methods, LSTM classification, attention mechanism, dynamic time warping