The Anomaly Detection Algorithm Based on Random Matrix Theory and Machine Learning

This study focuses on anomaly detection algorithms. Aiming at the limitations of traditional methods in complex data processing, an innovative algorithm that integrates random matrix theory and machine learning is proposed. First, different types of data, such as numerical values, texts, and images,...

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Vydáno v:International journal of advanced computer science & applications Ročník 16; číslo 6
Hlavní autor: Lu, Yongming
Médium: Journal Article
Jazyk:angličtina
Vydáno: West Yorkshire Science and Information (SAI) Organization Limited 2025
Témata:
Accuracy
Algorithms
Anomalies
Classification
Computer science
Data processing
Datasets
Decomposition
Machine learning
Matrix theory
Methods
Recall
Support vector machines
ISSN:2158-107X, 2156-5570
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Shrnutí:This study focuses on anomaly detection algorithms. Aiming at the limitations of traditional methods in complex data processing, an innovative algorithm that integrates random matrix theory and machine learning is proposed. First, different types of data, such as numerical values, texts, and images, are preprocessed, and random matrices are constructed. Hidden abnormal features are mined through specific transformations and then classified by optimized machine learning models. In the experimental stage, multiple data sets, such as KDD Cup 99, are selected to compare with classic algorithms such as DBSCAN and Isolation Forest. The results show that the innovative algorithm has a detection accuracy of 95%, a recall rate of 93%, and an F1 value of 94% on the KDD Cup 99 data set, which is significantly improved compared with the comparison algorithm. It also performs well on other data sets, with an average accuracy increase of seven percentage points and a recall rate increase of eight percentage points. The results demonstrate that the proposed algorithm can effectively mine data anomaly patterns, achieve efficient and accurate anomaly detection in complex data sets, and provide strong support for applications in related fields.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160611