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The Negative Selection Algorithm (NSA) is a fundamental component of Artificial Immune Systems (AIS) that mimics the negative selection process in the immune system. It is primarily used for anomaly detection and intrusion detection applications. The NSA operates by generating a set of self-antigens and then comparing them against a set of non-self (potentially anomalous) antigens.

Here is a general overview of the Negative Selection Algorithm:

1. Antigen Representation: An antigen is typically represented as a binary string or a feature vector that describes its characteristics or attributes.

2. Generation of Self-Antigens: The NSA begins by creating a set of self-antigens, which are representative of normal or non-anomalous patterns in the system. These self-antigens are typically generated randomly or derived from a training dataset of known non-anomalous examples.

3. Generation of Detectors (Antibodies): Antibodies are generated based on the self-antigens, and they are designed to recognize and bind to non-self antigens.

4. Detection Phase: In this phase, the NSA examines a set of non-self antigens to determine if they are recognized as non-self by the generated detectors (antibodies). Each non-self antigen is compared against the self-antigens using a matching or similarity function to measure the degree of recognition or binding.

5. Clonal Selection and Mutation: Non-self antigens that are recognized or bound by the generated detectors undergo clonal selection, where new detectors (clones) are generated with slight variations (mutations) based on the recognized antigens. This process helps to improve the system's ability to detect different types of non-self antigens.

6. Thresholding and Classification: After clonal selection and mutation, a threshold is applied to determine whether an antigen is classified as non-self (anomalous) or self (normal). Antigens that are recognized or bound by a sufficient number of detectors above the threshold are considered non-self.

The Negative Selection Algorithm operates on the principle of generating a diverse set of detectors that can distinguish self from non-self antigens. It leverages the concept of "self" to create a reference for what is considered normal, allowing it to identify deviations or anomalies from this normal behavior.

It's important to note that the specifics of the algorithm, such as the matching function, mutation operators, thresholding, and other parameters, can vary depending on the implementation and the specific application domain.