LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction

Log File Anomaly Detection

Analysis of log files pertaining to a failed run can be a tedious task, especially if the file runs into thousands of lines. Using the recent development in text analysis using deep neural networks, we present a method to reduce effort needed to analyze the log file by highlighting the most probably useful text in the failed log file, which can assist in debugging the causes of the failure. In ...

Anomaly Detection in Log Records

Received Jan 2, 2018 Revised Mar 9, 2018 Accepted Mar 24, 2018 In recent times complex software systems are continuously generating application and server logs for the events which had occurred in the past. These generated logs can be utilized for anomaly and intrusion detection. These log files can be used for detecting certain types of abnormalities or exceptions such as spikes in HTTP reques...

Computer Log Anomaly Detection Using Frequent Episodes

In this paper, we propose a set of algorithms to automate the detection of anomalous frequent episodes. The algorithms make use of the hierarchy and frequency of episodes present in an examined sequence of log data and in a history preceding it. The algorithms identify changes in a set of frequent episodes and their frequencies. We evaluate the algorithms and describe tests made using live comp...

SolarWinds Log & Event Manager

Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats

Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16]. With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and...