![Existing System:
A data stream provides a more natural representation of a machine
learning problem where the environment is changing as data is
continuously generated. The characteristics of data streams mean the entire
data set is not available at any one time. Subsets of the data set are
used as training sets, with testing sets being drawn from the same
distribution. It is usually the case that subsets are formed from contiguous
data instances.
This requires an update to the model that is being used to classify data. A
batch approach to the problem requires a reconstruction of the model each
time an update is required. The training phase is often the most
computationally costly operation. Incremental learning overcomes this
issue by using the previous model as the basis for an update.
Proposed System:
An adaptive incremental anomaly detection scheme based on kernel
principal component analysis (KPCA) [3] is proposed. An accurate
incremental split to a kernel eigenspace (KES) is proposed that is shown to
be more accurate than state-of-the-art methods.
This is coupled with a KES merge to form a Split-Merge KES algorithm that
allows the addition and removal of data instances to an anomaly detection
model. The aim of the anomaly detector is to identify data in the testing set
that is drawn from a different data distribution than the normal data in the
training set. A non-stationary environment is considered where the data
distribution of the normal data changes with time.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Adaptive anomaly detection with kernel eigenspace splitting and merging
- 1. Adaptive Anomaly Detection with Kernel Eigenspace Splitting and Merging Abstract: Kernel principal component analysis and the reconstruction error is an effective anomaly detection technique for non-linear datasets. In an environment where a phenomenon is generating data that is non- stationary, anomaly detection requires a recomputation of the kernel eigenspace in order to represent the current data distribution. Recomputation is a computationally complex operation and reducing computational complexity is therefore a key challenge. In this paper, we propose an algorithm that is able to accurately remove data from a kernel eigenspace without performing a batch recomputation. Coupled with a kernel eigenspace update, we demonstrate that our technique is able to remove and add data to a kernel eigenspace more accurately than existing techniques. An adaptive version determines an appropriately sized sliding window of data and when a model update is necessary. Experimental evaluations on both synthetic and real-world datasets demonstrate the superior performance of the proposed approach in comparison to alternative incremental KPCA approaches and alternative anomaly detection techniques.
- 2. Existing System: A data stream provides a more natural representation of a machine learning problem where the environment is changing as data is continuously generated. The characteristics of data streams mean the entire data set is not available at any one time. Subsets of the data set are used as training sets, with testing sets being drawn from the same distribution. It is usually the case that subsets are formed from contiguous data instances. This requires an update to the model that is being used to classify data. A batch approach to the problem requires a reconstruction of the model each time an update is required. The training phase is often the most computationally costly operation. Incremental learning overcomes this issue by using the previous model as the basis for an update. Proposed System: An adaptive incremental anomaly detection scheme based on kernel principal component analysis (KPCA) [3] is proposed. An accurate incremental split to a kernel eigenspace (KES) is proposed that is shown to be more accurate than state-of-the-art methods. This is coupled with a KES merge to form a Split-Merge KES algorithm that allows the addition and removal of data instances to an anomaly detection model. The aim of the anomaly detector is to identify data in the testing set that is drawn from a different data distribution than the normal data in the training set. A non-stationary environment is considered where the data distribution of the normal data changes with time.
- 3. An adaptive version determines an appropriate sliding window size and reduces the number of updates that are required by detecting when a change has occured and therefore only updating when necessary. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP. • Front End : - .Net • Back End : - SQL Server