Outlier detection in audit logs for application systemsJason Chen
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This document proposes a process to detect outliers in application system audit logs using data mining techniques. It begins with an introduction to systems auditing and the importance of audit logs. Then it discusses related work using data mining for auditing and different clustering and outlier detection algorithms. The proposed process involves pre-processing data, applying LOF and DBSCAN clustering algorithms to detect outliers, combining the results, applying classification algorithms and rules to determine outlier types. An experiment on a university management database achieved over 66% efficacy and less than 1% false positives. The study concludes the process can help auditors by facilitating outlier detection in real databases.
This document summarizes an electrical grid stability dataset from UCI. It describes 11 predictive attributes like reaction time and power consumed/produced for electricity producers and participants. The goals are stability, a value measuring system instability, and a stability label (stable/unstable). Correlations between attributes and stability are provided. The document also references a related paper on decentralized smart grid control that aims to stabilize the power system through distributed generation and price-based incentives for consumers.
This document discusses support vector machines (SVMs), including:
1) SVMs can handle nonlinear and high-dimensional data through kernel functions that transform data into a higher-dimensional space. Common kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid kernels.
2) The RBF kernel is often the best choice as it can adapt to different learning strategies by adjusting its hyperparameters to create flexible decision boundaries.
3) SVMs are used for classification, regression, and outlier detection tasks. They are robust against overfitting and can efficiently handle large datasets.
Outlier detection in audit logs for application systemsJason Chen
?
This document proposes a process to detect outliers in application system audit logs using data mining techniques. It begins with an introduction to systems auditing and the importance of audit logs. Then it discusses related work using data mining for auditing and different clustering and outlier detection algorithms. The proposed process involves pre-processing data, applying LOF and DBSCAN clustering algorithms to detect outliers, combining the results, applying classification algorithms and rules to determine outlier types. An experiment on a university management database achieved over 66% efficacy and less than 1% false positives. The study concludes the process can help auditors by facilitating outlier detection in real databases.
This document summarizes an electrical grid stability dataset from UCI. It describes 11 predictive attributes like reaction time and power consumed/produced for electricity producers and participants. The goals are stability, a value measuring system instability, and a stability label (stable/unstable). Correlations between attributes and stability are provided. The document also references a related paper on decentralized smart grid control that aims to stabilize the power system through distributed generation and price-based incentives for consumers.
This document discusses support vector machines (SVMs), including:
1) SVMs can handle nonlinear and high-dimensional data through kernel functions that transform data into a higher-dimensional space. Common kernel functions include linear, polynomial, radial basis function (RBF), and sigmoid kernels.
2) The RBF kernel is often the best choice as it can adapt to different learning strategies by adjusting its hyperparameters to create flexible decision boundaries.
3) SVMs are used for classification, regression, and outlier detection tasks. They are robust against overfitting and can efficiently handle large datasets.