Novel Real-Time Anomaly Detection Approach for Data Analysis

With the rapid development of the Internet and computer technology, the volume of data flows in computer networks continues to increase, with diverse and high-dimensional characteristics. Existing anomaly detection algorithms have problems such as low accuracy, long time consumption, and poor robustness when processing large-scale, high-concurrency, and high-dimensional data. To solve these problems, this study proposes a new real-time anomaly detection algorithm based on a three-step method. This algorithm is processed through three steps of data cleaning, data dimensional reduction, and efficient anomaly detection, which can significantly improve the detection accuracy and efficiency of abnormal data. Applying this method can realize abnormal data mining of large-scale high-dimensional data flows in computer networks, and data mining. Experimental results show that compared with traditional neural network and support vector machine algorithms, the algorithm proposed in this paper has significant advantages in accuracy, with an average detection accuracy of more than 99.5%. Its effectiveness in mining abnormal data of large-scale high-dimensional data flows in computer networks is mainly reflected in the following: applying the method in this paper can realize abnormal data mining of large-scale high-dimensional data flows in computer networks, and can mine large-scale high-dimensional data flows existing in the computer networks. All abnormal data in the dimensional data flow are mined, and the abnormal data types of the mined computer network data flow are completely consistent with the actual abnormal data types.