Publication Date
Spring 2018
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
Abstract
In software development, there is an absolute requirement to ensure that a system once developed, functions at its best throughout its lifetime. Application log data is critical to maintaining application performance and thus techniques to parse, understand and detect anomalies in application log data are critical to ensuring efficiency in software development. While initially hampered by limited hardware and lack of quality datasets, anomaly detection techniques have recently received a surge of interest with advancements in machine learning technology and especially neural networks. In this paper, we explore anomaly detection, historical techniques to detect anomalies and recent advancements in neural networks, which promise to revolutionize anomaly detection in application log data. Further, we analyze the most promising anomaly detection techniques and propose a hybrid model combining LSTM Neural Network and Auto Encoder which improves upon existing techniques.
Recommended Citation
Grover, Aarish, "Anomaly Detection for Application Log Data" (2018). Master's Projects. 635.
DOI: https://doi.org/10.31979/etd.znsb-bw4d
https://scholarworks.sjsu.edu/etd_projects/635
