# Negative Selection Algorithm #

## Name #

Negative Selection Algorithm (NSA)

## Taxonomy #

The Negative Selection Algorithm is a technique in the field of Artificial Immune Systems (AIS), which is a subfield of Computational Intelligence and Biologically Inspired Computation. It is closely related to other AIS algorithms such as the Clonal Selection Algorithm and the Immune Network Algorithm.

- Computational Intelligence
- Biologically Inspired Computation
- Artificial Immune Systems
- Negative Selection Algorithm

- Artificial Immune Systems

- Biologically Inspired Computation

## Strategy #

The Negative Selection Algorithm is inspired by the T-cell maturation process in the biological immune system. The algorithm generates a set of detectors that can distinguish between self (normal) and non-self (abnormal) patterns in a given dataset.

### Detector Generation #

The first step in the Negative Selection Algorithm is to generate a set of candidate detectors. These detectors are typically represented as binary strings or real-valued vectors. The generation process can be random or guided by heuristics to ensure a diverse and well-distributed set of detectors.

### Self-Tolerance #

After generating the candidate detectors, the algorithm undergoes a self-tolerance phase. In this phase, each detector is compared against a set of self samples, which represent the normal patterns in the dataset. If a detector matches any of the self samples, it is discarded. This process ensures that the remaining detectors do not react to normal patterns.

### Anomaly Detection #

Once the self-tolerant detectors are obtained, they can be used for anomaly detection. Given a new pattern, each detector is compared against the pattern using a matching function (e.g., Euclidean distance, Hamming distance). If any detector matches the pattern, it is classified as an anomaly (non-self). Otherwise, it is considered normal (self).

## Procedure #

- Initialize the algorithm parameters:
- Define the representation of detectors (e.g., binary strings, real-valued vectors)
- Set the detector generation mechanism (e.g., random generation, heuristic-based generation)
- Determine the matching threshold for the matching function

- Generate a set of candidate detectors:
- Create a pool of detectors based on the chosen representation and generation mechanism
- Ensure that the detectors cover the non-self space adequately

- Perform self-tolerance:
- For each candidate detector:
- Compare the detector against each self sample using the matching function
- If the detector matches any self sample, discard it

- Retain the detectors that do not match any self samples

- For each candidate detector:
- Use the self-tolerant detectors for anomaly detection:
- For each new pattern:
- Compare the pattern against each detector using the matching function
- If any detector matches the pattern, classify it as an anomaly (non-self)
- Otherwise, classify the pattern as normal (self)

- For each new pattern:

### Data Structures #

- Detectors: A set of binary strings or real-valued vectors representing the detectors
- Self Samples: A set of patterns representing the normal (self) class
- Matching Function: A function that determines the similarity between a detector and a pattern (e.g., Euclidean distance, Hamming distance)

### Parameters #

- Detector Representation: The choice of representation for the detectors (e.g., binary strings, real-valued vectors)
- Detector Generation Mechanism: The method used to generate candidate detectors (e.g., random generation, heuristic-based generation)
- Matching Threshold: The threshold value used in the matching function to determine if a detector matches a pattern

## Considerations #

### Advantages #

- Ability to detect previously unseen anomalies: The Negative Selection Algorithm can identify anomalies that were not present in the training data, making it suitable for scenarios where anomalies are rare or unknown.
- Adaptability: The algorithm can adapt to changes in the normal behavior of a system by updating the self samples and regenerating the detectors.
- Biological inspiration: The Negative Selection Algorithm is inspired by the immune system, which provides a strong theoretical foundation and insights into anomaly detection.

### Disadvantages #

- Scalability: The algorithm may suffer from scalability issues when dealing with high-dimensional data or a large number of self samples, as the number of detectors required to cover the non-self space can grow exponentially.
- Parameter sensitivity: The performance of the algorithm is sensitive to the choice of parameters, such as the detector representation, generation mechanism, and matching threshold. Careful parameter tuning is necessary to achieve optimal results.
- Difficulty in handling evolving anomalies: If the characteristics of anomalies change over time, the algorithm may need to be retrained with updated self samples to maintain its effectiveness.

## Heuristics #

### Detector Generation #

- Use a diverse set of detectors to cover the non-self space effectively. This can be achieved by using random generation or incorporating heuristics to ensure diversity.
- Consider domain-specific knowledge when generating detectors. Incorporating prior knowledge about the normal behavior of the system can help generate more targeted detectors.
- Experiment with different detector representations (e.g., binary strings, real-valued vectors) to find the most suitable representation for the given problem.

### Matching Function #

- Choose a matching function that is appropriate for the data type and problem domain. For example, Euclidean distance is commonly used for real-valued data, while Hamming distance is suitable for binary data.
- Adjust the matching threshold based on the desired trade-off between sensitivity and specificity. A lower threshold will result in more detectors matching the patterns, increasing the sensitivity but potentially leading to more false positives.

### Self-Tolerance #

- Ensure that the self samples adequately represent the normal behavior of the system. Including a diverse and representative set of self samples will help the algorithm learn the normal patterns effectively.
- Consider updating the self samples over time to adapt to changes in the normal behavior of the system. This can be done by periodically adding new self samples and removing outdated ones.

### Anomaly Detection #

- Analyze the anomalies detected by the algorithm to gain insights into the system’s behavior. Investigating the characteristics of the detected anomalies can help identify potential issues and improve the overall system performance.
- Consider combining the Negative Selection Algorithm with other anomaly detection techniques to enhance the overall detection accuracy. Ensemble methods or hybrid approaches that leverage multiple algorithms can provide more robust anomaly detection.