Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.Comment: 8 pages, 10 tables, 4 figures, IEEE Congress on Evolutionary Computation (CEC2006), Vancouver, Canad

Evolutionary techniques in a constraint satisfaction problem: Puzzle Eternity II

Proceeding of: IEEE Congress on Evolutionary Computation (CEC 2009), May 18-21 (Monday - Thursday), 2009, Trondheim, Norway.This work evaluates three evolutionary algorithms in a constraint satisfaction problem. Specifically, the problem is the Eternity II, a edge-matching puzzle with 256 unique square tiles that have to be placed on a square board of 16 times 16 cells. The aim is not to completely solve the problem but satisfy as many constraints as possible. The three evolutionary algorithms are: genetic algorithm, an own implementation of a technique based on immune system concepts and a multiobjective evolutionary algorithm developed from the genetic algorithm. In addition to comparing the results obtained by applying these evolutionary algorithms, they also will be compared with an exhaustive search algorithm (backtracking) and random search. For the evolutionary algorithms two different fitness functions will be used, the first one as the score of the puzzle and the second one as a combination of the multiobjective algorithm objectives. We also used two ways to create the initial population, one randomly and the other with some domain information.This work was supported in part by the University Carlos III of Madrid under grant PIF UC3M01-0809 and by the Ministry of Science and Innovation under project TRA2007- 67374-C02-02

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

A Self-adaptive Multipeak Artificial Immune Genetic Algorithm

Genetic algorithm is a global probability search algorithm developed by simulating the biological natural selection and genetic evolution mechanism and it has excellent global search ability, however, in practical applications, premature convergence occurs easily in the genetic algorithm. This paper proposes an self-adaptive multi-peak immune genetic algorithm (SMIGA) and this algorithm integrates immunity thought in the biology immune system into the evolutionary process of genetic algorithm, uses self-adaptive dynamic vaccination and provides a downtime criterion, the selection strategy of immune vaccine and the construction method of immune operators so as to promote the population develop towards the optimization trend and suppress the degeneracy phenomenon in the optimization by using the feature information in a selective and purposive manner. The simulation experiment shows that the method of this paper can better solve the optimization problem of multi-peak functions, realize global optimum search, overcome the prematurity problem of the antibody population and improve the effectiveness and robustness of optimization

An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing

In this paper, a systematic multi-objective fuzzy modeling approach is proposed, which can be regarded as a three-stage modeling procedure. In the first stage, an evolutionary based clustering algorithm is developed to extract an initial fuzzy rule base from the data. Based on this model, a back-propagation algorithm with momentum terms is used to refine the initial fuzzy model. The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency. To tackle the problem of simultaneously optimizing the structure and parameters, a variable length coding scheme is adopted to improve the efficiency of the search. The proposed modeling approach is applied to a real data set from the steel industry. Results show that the proposed approach is capable of eliciting not only accurate but also transparent fuzzy models

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system