Difference between revisions of "Anomaly Detection"
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PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect [https://www.linkedin.com/pulse/part-2-keep-simple-machine-learning-algorithms-big-dr-dinesh/ Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar] | PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect [https://www.linkedin.com/pulse/part-2-keep-simple-machine-learning-algorithms-big-dr-dinesh/ Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar] | ||
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| + | Principal Component Analysis (PCA) is a widely used statistical technique for dimensionality reduction and data analysis. However, PCA can also be employed for anomaly detection, which involves identifying data points that deviate significantly from the expected patterns or normal behavior of a dataset. Here, we will explore PCA-based anomaly detection and how it works. | ||
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| + | 1. Dimensionality Reduction with PCA: | ||
| + | PCA aims to capture the most important features or patterns in a dataset by transforming the data into a new set of variables called principal components. These principal components are linear combinations of the original features, and they are arranged in decreasing order of their explained variance. By reducing the dimensionality of the data while retaining as much information as possible, PCA enables more efficient data analysis. | ||
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| + | 2. Anomaly Detection using PCA: | ||
| + | Anomalies or outliers can be identified using PCA by leveraging the reconstruction error. The reconstruction error measures the dissimilarity between the original data points and their reconstructions based on the reduced-dimensional representation obtained through PCA. | ||
| + | |||
| + | The steps involved in PCA-based anomaly detection are as follows: | ||
| + | |||
| + | a. Data Preprocessing: The dataset is preprocessed by normalizing or standardizing the features to ensure that they have similar scales. This step is important for PCA as it assumes that the data is centered and has unit variance. | ||
| + | |||
| + | b. PCA Transformation: PCA is applied to the preprocessed data to obtain the principal components. The number of principal components selected depends on the desired level of dimensionality reduction. | ||
| + | |||
| + | c. Reconstruction: The original data is reconstructed using the reduced-dimensional representation obtained from PCA. The reconstruction process involves transforming the principal components back into the original feature space. | ||
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| + | d. Calculation of Reconstruction Error: The reconstruction error is computed as the Euclidean distance or other distance metric between the original data points and their reconstructions. The reconstruction error represents how well the original data can be represented using the reduced-dimensional representation obtained from PCA. | ||
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| + | e. Anomaly Threshold: An anomaly threshold is defined to distinguish between normal and anomalous data points. Data points with reconstruction errors above the threshold are considered anomalies, indicating that they deviate significantly from the expected patterns or normal behavior of the dataset. | ||
| + | |||
| + | 3. Advantages of PCA-based Anomaly Detection: | ||
| + | - Unsupervised Approach: PCA-based anomaly detection is an unsupervised technique, meaning it does not require labeled data for training. It identifies anomalies based solely on the inherent patterns and structures present in the dataset. | ||
| + | |||
| + | - Dimensionality Reduction: PCA reduces the dimensionality of the data, which can be beneficial for detecting anomalies in high-dimensional datasets. By capturing the most important features, PCA can highlight anomalies that might be hidden or difficult to identify in the original feature space. | ||
| + | |||
| + | - Robust to Noise: PCA is known to be robust to noise in the data. It focuses on the major trends and patterns, minimizing the impact of noise on the anomaly detection process. | ||
| + | |||
| + | 4. Limitations of PCA-based Anomaly Detection: | ||
| + | - Assumes Gaussian Distribution: PCA assumes that the data follows a Gaussian distribution. If the data contains non-Gaussian distributions or complex patterns, PCA may not be the most appropriate technique for anomaly detection. | ||
| + | |||
| + | - Linear Relationships: PCA assumes linear relationships between variables. It may not effectively detect anomalies that arise from non-linear relationships or interactions between features. | ||
| + | |||
| + | - Difficulty with Contextual Anomalies: PCA-based anomaly detection focuses on identifying data points that deviate significantly from the overall patterns in the dataset. It may struggle with detecting anomalies that are context-specific or dependent on specific subsets of features. | ||
| + | |||
| + | - Selection of Anomaly Threshold: Determining the appropriate threshold for classifying anomalies can be challenging. It requires careful consideration and domain knowledge to strike a balance between false positives and false negatives. | ||
| + | |||
<youtube>ExoAbXPJ7NQ</youtube> | <youtube>ExoAbXPJ7NQ</youtube> | ||
Revision as of 05:56, 6 July 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
- Case Studies
- Cybersecurity ... OSINT ... Frameworks ... References ... Offense ... NIST ... DHS ... Screening ... Law Enforcement ... Government ... Defense ... Lifecycle Integration ... Products ... Evaluating
- ...find outliers
- Immersive Reality ... Metaverse ... Digital Twin ... Internet of Things (IoT) ... Transhumanism
- Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang
Anomalies data points which do not conform to an expected pattern of the other items in the data set
Anomaly Detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
Principal Component Analysis (PCA) Anomaly Detection
PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar
Principal Component Analysis (PCA) is a widely used statistical technique for dimensionality reduction and data analysis. However, PCA can also be employed for anomaly detection, which involves identifying data points that deviate significantly from the expected patterns or normal behavior of a dataset. Here, we will explore PCA-based anomaly detection and how it works.
1. Dimensionality Reduction with PCA: PCA aims to capture the most important features or patterns in a dataset by transforming the data into a new set of variables called principal components. These principal components are linear combinations of the original features, and they are arranged in decreasing order of their explained variance. By reducing the dimensionality of the data while retaining as much information as possible, PCA enables more efficient data analysis.
2. Anomaly Detection using PCA: Anomalies or outliers can be identified using PCA by leveraging the reconstruction error. The reconstruction error measures the dissimilarity between the original data points and their reconstructions based on the reduced-dimensional representation obtained through PCA.
The steps involved in PCA-based anomaly detection are as follows:
a. Data Preprocessing: The dataset is preprocessed by normalizing or standardizing the features to ensure that they have similar scales. This step is important for PCA as it assumes that the data is centered and has unit variance.
b. PCA Transformation: PCA is applied to the preprocessed data to obtain the principal components. The number of principal components selected depends on the desired level of dimensionality reduction.
c. Reconstruction: The original data is reconstructed using the reduced-dimensional representation obtained from PCA. The reconstruction process involves transforming the principal components back into the original feature space.
d. Calculation of Reconstruction Error: The reconstruction error is computed as the Euclidean distance or other distance metric between the original data points and their reconstructions. The reconstruction error represents how well the original data can be represented using the reduced-dimensional representation obtained from PCA.
e. Anomaly Threshold: An anomaly threshold is defined to distinguish between normal and anomalous data points. Data points with reconstruction errors above the threshold are considered anomalies, indicating that they deviate significantly from the expected patterns or normal behavior of the dataset.
3. Advantages of PCA-based Anomaly Detection: - Unsupervised Approach: PCA-based anomaly detection is an unsupervised technique, meaning it does not require labeled data for training. It identifies anomalies based solely on the inherent patterns and structures present in the dataset.
- Dimensionality Reduction: PCA reduces the dimensionality of the data, which can be beneficial for detecting anomalies in high-dimensional datasets. By capturing the most important features, PCA can highlight anomalies that might be hidden or difficult to identify in the original feature space.
- Robust to Noise: PCA is known to be robust to noise in the data. It focuses on the major trends and patterns, minimizing the impact of noise on the anomaly detection process.
4. Limitations of PCA-based Anomaly Detection: - Assumes Gaussian Distribution: PCA assumes that the data follows a Gaussian distribution. If the data contains non-Gaussian distributions or complex patterns, PCA may not be the most appropriate technique for anomaly detection.
- Linear Relationships: PCA assumes linear relationships between variables. It may not effectively detect anomalies that arise from non-linear relationships or interactions between features.
- Difficulty with Contextual Anomalies: PCA-based anomaly detection focuses on identifying data points that deviate significantly from the overall patterns in the dataset. It may struggle with detecting anomalies that are context-specific or dependent on specific subsets of features.
- Selection of Anomaly Threshold: Determining the appropriate threshold for classifying anomalies can be challenging. It requires careful consideration and domain knowledge to strike a balance between false positives and false negatives.
Intruder
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