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1. Anomaly Detection via Online Over-Sampling Principal Component
Analysis
ABSTRACT:
Anomaly detection has been an important research topic in data mining and machine learning.
Many real-world applications such as intrusion or credit card fraud detection require an
effective and efficient framework to identify deviated data instances. However, most anomaly
detection methods are typically implemented in batch mode, and thus cannot be easily extended
to large-scale problems without sacrificing computation and memory requirements. In this
paper, we propose an online over-sampling principal component analysis (osPCA) algorithm to
address this problem, and we aim at detecting the presence of outliers from a large amount of
data via an online updating technique. Unlike prior PCA based approaches, we do not store the
entire data matrix or covariance matrix, and thus our approach is especially of interest in online
or large-scale problems. By over-sampling the target instance and extracting the principal
direction of the data, the proposed osPCA allows us to determine the anomaly of the target
instance according to the variation of the resulting dominant eigenvector. Since our osPCA need
not perform eigen analysis explicitly, the proposed framework is favored
for online applications which have computation or memory limitations. Compared with the
well-known power method for PCA and other popular anomaly detection algorithms, our
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2. experimental results verify the feasibility of our proposed method in terms of both accuracy and
efficiency.
EXISTING SYSTEM:
The existing approaches can be divided into three categories:
1. distribution (statistical),
2. distance and
3. density based methods.
Statistical approaches assume that the data follows some standard or predetermined
distributions, and this type of approach aims to find the outliers which deviate form such
distributions.
For distance-based methods, the distances between each data point of interest and its neighbors
are calculated. If the result is above some predetermined threshold, the target instance will be
considered as an outlier.
One of the representatives of this type of approach is to use a density based local outlier factor
(LOF) to measure the outlierness of each data instance. Based on the local density of each data
instance, the LOF determines the degree of outlierness, which provides suspicious ranking
scores for all samples. The most important property of the LOF is the ability to estimate local
data structure via density estimation. This allows users to identify outliers which are sheltered
under a global data structure
DISADVANTAGES OF EXISTING SYSTEM:
Most distribution models are assumed univariate, and thus the lack of robustness for
multidimensional data is a concern. Moreover, since these methods are typically implemented

3. in the original data space directly, their solution models might suffer from the noise present in
the data
PROPOSED SYSTEM:
PCA is a well known unsupervised dimension reduction method, which determines the
principal directions of the data distribution. This will prohibit the use of our proposed
framework for real-world large-scale applications. Although the well known power method is
able to produce approximated PCA solutions, it requires the storage of the covariance matrix
and cannot be easily extended to applications with streaming data or online settings. Therefore,
we present an online updating technique for our osPCA. This updating technique allows us to
efficiently calculate the approximated dominant eigenvector without performing eigen analysis
or storing the data covariance matrix.
ADVANTAGES OF PROPOSED SYSTEM:
Compared to the power method or other popular anomaly detection algorithms, the
required computational costs and memory requirements are significantly reduced, and
thus our method is especially preferable in online, streaming data, or large scale
problems.

4. SYSTEM ARCHITECTURE:
ALGORITHMS USED:
Anomaly Detection via Online Oversampling PCA

5. MODULES
1. Cleaning Data
2. Detecting Outliers
3. Clustering
MODULES DESCRIPTION
MODULE - I
Cleaning Data
The osPCA is applied for the data set for finding the principal direction. In this method the
target instance will be duplicated multiple times, and the idea is to amplify the effect of outlier
rather than that of normal data. After that using Leave One Out (LOO) strategy, the angle
difference will be identified. In which if we add or remove one data instance, the direction will
be changed. For normal data instances this angle difference should be smaller and for outliers
this might be larger.
A set of data instances in the original data set is taken as predefined input. This data may be
contaminated by noise and incorrect data labelling etc., This data might be error free, because
this is going to be used as training data. So the cleaning is done using Over-Sampling Principal
Component Analysis (osPCA) method. And then the score of outlierness St is calculated for
each data instances. The smallest St value is taken as the threshold value.
MODULE - II
Detection

6. This is for detecting the outlierness of the user input. When the user giving the input to the
system, the system calculate the St value for the new input. And then compare that new St value
with the threshold value which is calculated in earlier.
If the St value of the new data instance is above the threshold value, then that input data is
identified as an outlier and that value will be discarded by the system. Otherwise it is
considered as a normal data instance, and the PCA value of that particular data instance is
updated accordingly.
MODULE - III
Clustering
The training data will be selected only by our assumption. So there is a possibility that
some outlier data may be considered as normal data in the previous method due to our training
data. So the clustering method is used to solve this problem. The clusters are formed for input
data instances and then the outlier calculation is applied for each cluster to find the outlier
exactly.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse

7.  Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.
REFERENCE:
Yuh-Jye Lee, Yi-Ren Yeh, and Yu-Chiang Frank Wang, “Anomaly Detection via Online Over-
Sampling Principal Component Analysis”, IEEE TRANSACTIONS ON KNOWLEDGE
AND DATA ENGINEERING 2013.