Anomaly detection (also known as outlier detection) is the process of identifying unusual things, events, or observations that stand out from the rest of the data. Typically, the anomalous things will point to a problem such as a bank fraud, a structural flaw, medical issues, or textual mistakes. Outliers, novelties, noise, deviations, and exceptions are all terms used to describe anomalies.
What Does Anomaly Detection Mean?
Anomaly detection is the process of identifying data points, things, observations, or occurrences that do not fit within a group’s expected pattern. These abnormalities are uncommon, but they might indicate a huge and serious threat, such as cyber-attacks or fraud.
Techniques for anomaly detection methods include:
- One-class support vector machines
- Determination of records that deviate from learned association rules
- Distance-based techniques
- Replicator neural networks
- Cluster analysis-based anomaly detection
Let’s get into each one from the above anomaly detection list:
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One-class support vector machines
One-Class SVMs (OC-SVM) train a decision boundary that produces the greatest separation between known-class samples and the origin. Only a tiny percentage of data points are permitted to cross the decision border, and those data points are referred to be outliers. The identification of anomalies in network traffic is very essential. The apparent increase in the amount of data and economic damage caused by malicious or inadvertent assaults, malfunctions, and anomalies has fueled attempts to guarantee that network monitoring systems can identify and classify anomalous behavior. Because of the limited computer and storage resources available, it takes skill and inventiveness to accurately define ever-changing network traffic trends.
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Determination of records that deviate from learned association rules
The rule-based machine learning approach of association rule learning is used to identify interesting relationships between variables in big databases. Its goal is to use some interesting metrics to find strong rules identified in databases.
In most cases, association rules must simultaneously meet user-specified minimum support and user-specified minimum confidence. The creation of association rules is generally separated into two steps:
To find all frequent itemsets in a database, a minimum support threshold is used. To form rules, these frequently occurring itemsets are subjected to a minimum confidence constraint. The second step is simple, but the first phase requires more care.
It’s tough to find all frequent itemsets in a database since it requires searching all potential itemsets (item combinations).
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Distance-based techniques
The distance-based outlier identification approach looks for outliers in an object’s neighborhood, which is specified by a radius. If there aren’t enough other points in the vicinity of an item, it’s classified as an outlier.
A distance is a criterion that may be defined as the object’s reasonable neighborhood. We can identify a suitable number of objects’ neighbors for each object o.
Algorithms for finding outliers based on distance:
- Index-based algorithm
- Nested-loop algorithm
- Cell-based algorithm
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Replicator neural networks
Data is squeezed via a hidden layer using a staircase-like activation function by replicator neural networks. The network compresses data by allocating it to a fixed number of clusters using a staircase-like activation function (depending on the number of neurons and number of steps).
RNNs were first introduced in the data compression industry. It was proposed by Hawkins et al. for outlier modeling. Both publications suggest a 5-layer layout with a linear output layer and a staircase-like activation function in the intermediate layer. This activation function’s job is to divide the vector of middle hidden layer outputs into grid points, allowing the data points to be organized into clusters.
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Cluster analysis-based anomaly detection
K-mean clustering is a well-known and easy method. It is less computationally demanding than many other methods, making it a better choice when the dataset is huge. The following are the steps in K-mean clustering:
- Choose a value for K, the total number of clusters to be determined.
- Choose K instances (data points) within the dataset at random. These are the initial cluster centers.
- Use simple Euclidean distance to assign to remaining instances to their closet cluster center.
- Use the instances in each cluster to calculate a new mean for each cluster.
- If the new mean values are identical to the mean values of the previous iteration the process terminates. Otherwise, use the new means as cluster centers and repeat steps 3-5.
A large number of clusters are investigated. The number of clusters is set so that adding another does not result in a substantial improvement in the model. Checking the percentage of variation explained as a function of cluster number determines the stopping point. When the first few clusters are introduced, they will add a lot of variance explanation. The marginal increase in variance explained will eventually be decreased. The optimal number of clusters to choose is when the marginal benefit starts to decrease.
According to DeepAI, anomaly detection allows organizations to track “security errors, structural defects, and even bank fraud” by identifying “rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data.” Anomaly detection is divided into three types: unsupervised, supervised, and semi-supervised. Analysts at the Security Operations Center (SOC) employ each of these methods in Cybersecurity applications to different degrees of success. Artificial intelligence (AI) and its use in anomaly detection solutions are frequently touted by cybersecurity companies. Although these vendors claim that their AI improvements can detect abnormalities on their own, the reality frequently falls short. Even if these systems can detect abnormalities (with or without AI), detecting anomalies is a long way from batting down threats.