Data Mining Anomaly Detection Lecture Notes For Chapter 10 ✓ Solved

Data Mining Anomaly Detection Lecture Notes for Chapter 10

Anomaly/Outlier Detection:

What are anomalies/outliers? The set of data points that are considerably different than the remainder of the data.

Variants of Anomaly/Outlier Detection Problems:

Given a database D, find all the data points x ∈ D with anomaly scores greater than some threshold t.

Given a database D, find all the data points x ∈ D having the top-n largest anomaly scores f(x).

Given a database D containing mostly normal (but unlabeled) data points, and a test point x, compute the anomaly score of x with respect to D.

Applications: Credit card fraud detection, telecommunication fraud detection, network intrusion detection, fault detection.

Importance of Anomaly Detection:

Ozone Depletion History - In 1985 three researchers were puzzled by low ozone levels recorded by the British Antarctic Survey.

Challenges with Anomaly Detection:

How many outliers are there in the data? Validation can be quite challenging. Finding needle in a haystack.

General Steps for Anomaly Detection Schemes:

Build a profile of the “normal” behavior. Use the “normal” profile to detect anomalies.

Types of Anomaly Detection Schemes:

Graphical & Statistical-based, Distance-based, Model-based.

Limitations of Statistical Approaches include difficulty in high-dimensional data, and fewer tests available for single attributes.

Distance-based Approaches:

Compute the distance between data points, and define outliers based on neighboring points' distances.

Density-based: LOF approach computes local outlier factors to identify outlying observations.

Clustering-Based Approach focuses on clusters of different densities to select candidate outliers.

Base Rate Fallacy relates to statistical significance and its relevance in anomaly detection, particularly in intrusion detection.

Conclusion: The study of anomaly detection is vital for applications across several fields, where identifying unusual patterns can prevent fraud and increase security.

Paper For Above Instructions

Anomaly detection, also known as outlier detection, is a crucial task in the field of data mining. It involves identifying data points that significantly differ from the majority of the data. The identification of these anomalies is essential across a range of applications such as credit card fraud detection, network intrusion detection, and fault detection in systems. Understanding the significance of anomaly detection begins with a clear definition of anomalies: they are data points that deviate notably from the expected behavior of the dataset.

In anomaly detection, researchers encounter several challenges, primarily the validation of detected anomalies. Anomaly detection is often an unsupervised learning task, meaning that the model has to determine what is considered ‘normal’ behavior without labeled examples. Therefore, the assumption that there are significantly more normal observations than anomalies becomes fundamental to many detection methods (Hodge & Austin, 2004).

The process of anomaly detection can be broken down into a few general steps. First, it is important to establish a profile of normal behavior within the dataset. This profile may include patterns or summary statistics that represent the overall data population. Once this profile is established, outliers can be detected by comparing observed characteristics against the normal behavior model (Chandola et al., 2009).

Several different schemes for anomaly detection exist, including graphical, statistical, distance-based, and model-based approaches. Graphical methods such as scatter plots or box plots can visually delineate anomalies, but can be time-consuming and subjective (Iglewicz & Hoaglin, 1993). Statistical approaches often rely on assumptions about the data's distribution, such as normality, and may use tests like Grubbs’ Test to identify outliers (Grubbs, 1950). However, these techniques are often limited in their ability to handle high-dimensional data, where traditional statistical tests may falter (Moreira et al., 2016).

Distance-based approaches define anomalies with respect to the proximity between data points. Techniques such as k-nearest neighbor (k-NN) compute distances and identify points that are isolated based on their neighbors (Estivill-Castro, 2002). Additionally, density-based methods, such as the Local Outlier Factor (LOF), assess the density of data points in their neighborhoods, identifying points with significantly lower density as outliers (Breunig et al., 2000).

Another prominent method in anomaly detection is the clustering-based approach. This method involves clustering the dataset and identifying outliers as points that are situated far from the clusters of normal points. The choice of clustering algorithm, such as k-means or DBSCAN, significantly impacts the outcome of this approach (Xu & Wunsch, 2005).

A key concept closely related to anomaly detection is the base rate fallacy, which affects the interpretation of detection results. For example, even if a detection system has a high accuracy rate (e.g., 99%), the actual positive detection can be misleading if the base rate of true anomalies is very low (Axelsson, 2000). In contexts such as network intrusion detection, this can lead to more false alarms, necessitating careful calibration of detection thresholds to balance detection rates against false positives.

In conclusion, the realm of anomaly detection is vast and vital, connecting directly to significant practical implications in areas such as fraud detection and system security. By employing various detection schemes, practitioners can unveil hidden data patterns that represent potential threats or irregularities. With the increasing availability of data across numerous fields, enhancing the effectiveness of anomaly detection techniques will continue to be a pivotal aspect of data mining research.

References

• Axelsson, S. (2000). The base-rate fallacy and its implications for intrusion detection systems. Computers & Security, 19(8), 733-742.
• Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. ACM Sigmod Record, 29(2), 93-104.
• Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.
• Estivill-Castro, V. (2002). Why are outlier detection methods so highly influenced by the dimensionality of the data? Proceedings of the 2002 ACM Symposium on Applied Computing.
• Grubbs, F. E. (1950). Sample Criteria for Testing Outlying Observations. Annals of Mathematical Statistics, 21(1), 27-58.
• Iglewicz, B. & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. Thousand Oaks, CA: Sage Publications.
• Moreira, L. L., de Lima, L. S., & Pinto, R. L. (2016). A statistical approach for the detection of anomalies in high-dimensional data. Computational Statistics, 31(2), 505-529.
• Xu, R., & Wunsch, D. (2005). Clustering. Wiley Encyclopedia of Computer Science and Engineering.
• Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85-126.