What is an artificial immune system?

What is an artificial immune system?

An Artificial Immune System (AIS) is a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. It's a sub-field of biologically inspired computing and natural computation, with interests in machine learning and belonging to the broader field of artificial intelligence.

The concept of AIS began to take shape in the 1980s, and it employs some of the engineering techniques used in biological immune systems. This could include immune system mathematics and computer modeling, as well as the abstraction of some immunology principles into algorithms.

AIS are adaptive systems, inspired by theoretical immunology and observed immune functions, principles, and models, which are applied to problem-solving. They are computational approaches inspired by the biological immune system and are thus categorized as a type of nature-inspired meta-heuristic along with genetic algorithms, ant colony optimization, particle swarm optimization, and others.

Four major AIS algorithms have been under constant development: Negative Selection Algorithms (NSA), Artificial Immune Networks (AINE), Clonal Selection Algorithms (CSA), and Dendritic Cell Algorithms (DCA).

AIS can be used in various applications such as distributed detection, computer security (virus detection, UNIX process monitoring), anomaly detection in time series data, fault diagnosis, pattern recognition, optimization, and memory acquisition.

In essence, an AIS is a sophisticated system that uses the principles of the biological immune system to solve complex computational problems. It's a promising field that continues to evolve and find new applications in various domains.

How do artificial immune systems differ from natural immune systems?

Artificial Immune Systems (AIS) and natural immune systems differ in several ways, primarily in their structure, response type, adaptability, and the way they are acquired.

1. Structure — The natural immune system is a complex network of cells, tissues, and organs that are present in the human body from birth. It includes both innate and adaptive arms, with external defenses like tears and stomach acid, and internal defenses that respond to pathogens. On the other hand, an AIS is a man-made system that mimics the natural immune system. It is a rule-based machine learning system that uses algorithms and mathematical models to identify and respond to threats.

2. Response Type — The natural immune system generates a biological response, such as the production of antibodies, to eliminate invading pathogens. In contrast, the AIS generates a computational response, such as a notification of a security breach, to eliminate the threat.

3. Adaptability — The natural immune system is highly adaptable and can respond to a wide range of pathogens. It is able to learn and evolve over time. The AIS, however, is limited by the algorithms and mathematical models used to design it. While it can adapt to new data patterns, its adaptability is not as broad or dynamic as that of the natural immune system.

4. Acquisition — Natural immunity is acquired from exposure to the disease organism through infection with the actual disease or passed from mother to child at birth or through breastfeeding. Artificial immunity, on the other hand, can be active or passive. Active artificial immunity is acquired through the introduction of a killed or weakened form of the disease organism, typically through vaccination. Passive artificial immunity is provided when a person is given antibodies to a disease rather than producing them, such as through antibody-containing blood products.

While AIS are inspired by natural immune systems and aim to mimic their functionality, they are fundamentally different in their structure, response type, adaptability, and acquisition. They serve different purposes, with natural immune systems protecting biological organisms from diseases, and AIS solving complex computational problems.