Artificial Immune Systems
378 Dunn Hall
Memphis, TN 38152-3240
phone: (901) 678-4147
fax: (901) 678-2480
dasgupta@memphis.edu
- Professor, Department of Computer Science, University of Memphis
- Director, Center for Information Assurance
- Director, Intelligent Security Systems Research Laboratory
Research
Biological systems such as human beings can be regarded as sophisticated information processing systems, and can be expected to provide inspiration for various ideas to science and engineering. Biologically motivated information processing systems can be classified into: brain-nervous systems (neural networks), genetic systems (evolutionary algorithms), and immune systems (artificial immune systems). Among these, nervous systems and genetic systems have been widely applied to various fields. However, there have been relatively few applications of the immune system.
The natural immune system is a very complex system with several mechanisms for defense against pathogenic organisms. The main purpose of the immune system is to recognize all cells (or molecules) within the body and categorize those cells as self or nonself. The nonself cells are further categorized in order to induce the appropriate type of defense mechanism. The immune system learns, through evolution, to distinguish between dangerous foreign antigens (e.g., bacteria, viruses, etc.) and the body's own cells or molecules.
From an information-processing perspective, the immune system is a remarkable parallel and distributed, adaptive system. It uses learning, memory, and associative retrieval to solve recognition and classification tasks. In particular, it learns to recognize relevant patterns, remember patterns that have been seen previously, and use combinatorics to construct pattern detectors efficiently. Also, the overall behavior of the system is an emergent property of many local interactions. These remarkable information-processing abilities of the immune system provide several important aspects in the field of computation. This field is sometimes referring to as Immunological Computation or Artificial Immune Systems. There are a few groups that are working in the field.
Anomaly Detection
The objective of this research is to develop an efficient detection algorithm that can be used for noticing any changes in steady-state characteristics of a system or process. In these experiments, the notion of self is considered to be the normal behavior patterns of the monitored system. It may be assumed that the normal behavior of a system or a process can be often characterized by a series of observations over time. Furthermore, the normal system behavior generally exhibits stable patterns when observed over a period of time. So, any deviation that exceeds an allowable variation in the observed data is considered an anomaly in the behavior pattern. This approach relies on sufficient samples of normal data (that can capture the semantics of the data patterns) to generate a diverse set of detectors that probabilistically detect changes without requiring prior knowledge of anomalous (or faulty) patterns.
Germinal Center Dynamics
The Germinal Center is one of the functional modules of natural immune systems, which evolves in some organs and plays a major role in immune response. The development of a GC is a complex process, which is formed dynamically when antigen-activated B-Cells migrate into the primary follicles of the peripheral lymphoid organs. The formation of the GC requires activation of B Cells, Migration of B Cells, T and B Cell Interactions, and the availability of the network of follicular denritic cells (FD). From an information processing point of view, the role of the germinal center can be used as a pattern matching model, particularly, to distinguish between known patterns and novel patterns. We are currently developing a multistage pattern-matching algorithm where different features of the pattern are extracted and compared in phases.