Showing papers on "Artificial immune system published in 2010"

Review: The use of computational intelligence in intrusion detection systems: A review

Abstract: Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.

Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection

Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.

An Adaptive Control Strategy for DSTATCOM Applications in an Electric Ship Power System

Abstract: Distribution static compensator (DSTATCOM) is a shunt compensation device that is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, power quality issues arise due to high-energy demand loads such as pulse loads. This paper presents the application of a DSTATCOM to improve the power quality in a ship power system during and after pulse loads. The control strategy of the DSTATCOM plays an important role in maintaining the voltage at the point of common coupling. A novel adaptive control strategy for the DSTATCOM based on artificial immune system (AIS) is presented in this paper. The optimal parameters of the controller are first obtained by using the particle swarm optimization algorithm. This provides a sort of innate immunity (robustness) to common system disturbances. For unknown and random system disturbances, the controller parameters are modified online, thus providing adaptive immunity to the control system. The performance of the DSTATCOM and the AIS-based adaptive control strategy is first investigated in MATLAB-/Simulink-based simulation platform. It is verified through a real-time ship power system implementation on a real-time digital simulator and the control algorithm on a digital signal processor.

Find multi-objective paths in stochastic networks via chaotic immune PSO

Abstract: Path finding is a fundamental research topic in transportation planning, intelligent transportation system, routine selection, etc It is usually simplified as the shortest path (SP) in deterministic networks However, some parameters in real life are stochastic In this article, a more pragmatic model for stochastic networks was proposed, which not only considers determinist variables but also the mean and variances of random variables In order to fasten the solution of our model, a novel method was proposed, which combines artificial immune system (AIS), chaos operator, and particle swarm optimization (PSO) Numerical experiments were presented to demonstrate that this proposed model is valid, effective, and more close to real-life, and CIPSO outperforms GA and PSO in respect of route optimality and convergence time

Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata

Abstract: Cellular automata (CA) have been increasingly used in simulating urban expansion and land-use dynamics. However, most urban CA models rely on empirical data for deriving transition rules, assuming that the historical trend will continue into the future. Such inertia CA models do not take into account possible external interventions, particularly planning policies, and thus have rarely been used in urban and land-use planning. This paper proposes to use artificial immune systems (AIS) as a technique for incorporating external interventions and generating alternatives in urban simulation. Inspired by biological immune systems, the primary process of AIS is the evolution of a set of 'antibodies' that are capable of learning through interactions with a set of sample 'antigens'. These 'antibodies' finally get 'matured' and can be used to identify/classify other 'antigens'. An AIS-based CA model incorporates planning policies by altering the evolution mechanism of the 'antibodies'. Such a model is capable of generating different scenarios of urban development under different land-use policies, with which the planners will be able to answer 'what if' questions and to evaluate different options. We applied an AIS-based CA model to the simulation of urban agglomeration development in the Pearl River Delta in southern China. Our experiments demonstrate that the proposed model can be very useful in exploring various planning scenarios of urban development.

Design of an Artificial Immune System for fault detection: A Negative Selection Approach

Abstract: This paper presents a methodology that designs a fault detection Artificial Immune System (AIS) based on immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The key fault detection challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of AIS. The SelfxNonself theory inspired an immune-based fault detection approach. This article proposes the AIS Multi-Operational Algorithm based on the Negative Selection Algorithm. The proposed algorithm is used to a DC motor fault model benchmark to compare its relative performance to others fault detection algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection.

Artificial immune systems - models, algorithms and applications

Abstract: 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.

Vaccine-Enhanced Artificial Immune System for Multimodal Function Optimization

Abstract: This paper emulates a biological notion in vaccines to promote exploration in the search space for solving multimodal function optimization problems using artificial immune systems (AISs). In this method, we first divide the decision space into equal subspaces. The vaccine is then randomly extracted from each subspace. A few of these vaccines, in the form of weakened antigens, are then injected into the algorithm to enhance the exploration of global and local optima. The goal of this process is to lead the antibodies to unexplored areas. Using this biologically motivated notion, we design the vaccine-enhanced AIS for multimodal function optimization, achieving promising performance.

Designing Novel Photonic Devices by Bio-Inspired Computing

Abstract: Three bio-inspired optimization schemes, integrated into a robust frequency domain 2-D finite-element code, have been assessed by the design of two novel nonintuitive photonic crystal-type devices: an unbalanced power splitter and a micro-to-nano waveguide coupler. The bio-inspired schemes were taken from three biological-based classes of algorithms: genetic algorithm, evolution strategy, and artificial immune system. The results show that such schemes constitute valuable and powerful tools for the design of advanced and sophisticated photonic devices, where pixels concept was changed by photonic crytals structures to enable their future fabrication.

A Survey of artificial immune applications

Abstract: The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modeling the natural immune system, to tackling real world applications, using an equally diverse set of immune inspired algorithms. We review the current immune applications of the AIS approach, and propose a number of suggestions to the AIS community that can be undertaken to help move the area forward. Despite many successes of AIS techniques, there remain some open issues which have to be addressed in order to make the AIS a real-world problem solving technique.

Dendritic Cells for SYN Scan Detection

Abstract: Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the the fused data signals with a secondary data stream. Aggregate output of a population of cells, is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

On artificial immune systems and swarm intelligence

Abstract: This position paper explores the nature and role of two bio-inspired paradigms, namely Artificial Immune Systems (AIS) and Swarm Intelligence (SI). We argue that there are many aspects of AIS that have direct parallels with SI and examine the role of AIS and SI in science and also in engineering, with the primary focus being on the immune system. We explore how in some ways, algorithms from each area are similar, but we also advocate, and explain, that rather than being competitors, AIS and SI are complementary tools and can be used effectively together to solve complex engineering problems.

An evolutionary memetic algorithm for rule extraction

Abstract: In this paper, an Evolutionary Memetic Algorithm (EMA), which uses a local search intensity scheme to complement the global search capability of Evolutionary Algorithms (EAs), is proposed for rule extraction. Two schemes for local search are studied, namely [email protected], which uses a micro-Genetic Algorithm-based (@mGA) technique, and EMA-AIS, which is inspired by Artificial Immune System (AIS) and uses the clonal selection for cell proliferation. The evolutionary memetic algorithm is complemented with the use of a variable-length chromosome structure, which allows the flexibility to model the number of rules required. In addition, advanced variation operators are used to improve different aspects of the algorithm. Real world benchmarking problems are used to validate the performance of EMA and results from simulations show the proposed algorithm is effective.

Integrated Framework for Artificial Immunity-Based Aircraft Failure Detection, Identification, and Evaluation

Abstract: This paper presents a novel conceptual framework for an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of failures of aircraft subsystems based on the artificial immune system paradigm. The detection represents the capability to declare that a failure within any of the aircraft subsystems has occurred. The identification process determines which element has failed. The evaluation of the failure addresses three aspects: the type of the failure, its magnitude, and the reassessment of the generalized flight envelope. Failure detection, identification, and evaluation schemes are included using the bioimmune system metaphor combined with other artificial intelligence techniques. The immunity-based fault detection operates in a similar manner as does the immune system when it distinguishes between entities that belong to the organism and entities that do not. The proposed approach directly addresses the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and provides the adequate tools to solve the failure detection problem in an integrated and comprehensive manner. A multiself failure detection and identification scheme is presented for actuator, sensor, engine, and structural failures/damages, which was developed and tested using a motion-based flight simulator. The scheme achieves excellent detection rates and a low number of false alarms and demonstrates the effectiveness of the proposed framework.

Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System Learning Algorithm

Abstract: Load forecasting is very essential to the operation of electric utility. It is a pre-requisite to economic dispatch of electrical power and enhances the efficiency besides ensuring reliable operation of a power system. Electrical energy demand is highly dependent on various independent variables such as the weather, temperature, holidays, and days in a week. The accuracy of the forecast is important to ensure consistent electrical power supply to customer without compromising the economic aspect of the power system operation. In this paper, an Artificial Neural Network (ANN) trained by the Artificial Immune System (AIS) learning algorithm is proposed for short term load forecasting model. Two sets of electrical energy demand data were used to test the capability of the proposed algorithm. Based on the results obtained, it shows that the proposed AIS learning algorithm is capable to provide a comparable forecast to that of Artificial Neural Network with Back Propagation (BP) as the learning algorithm. Hence, this indicates that Artificial Immune System could be implemented as an alternative learning algorithm for an Artificial Neural Network.

IDS: An Immune-Inspired Approach for the Detection of Software Design Smells

Abstract: We propose a parallel between object-oriented system designs and living creatures. We suggest that, like any living creature, system designs are subject to diseases, which are design smells (code smells and anti patterns). Design smells are conjectured in the literature to impact the quality and life of systems and, therefore, their detection has drawn the attention of both researchers and practitioners with various approaches. With our parallel, we propose a novel approach built on models of the immune system responses to pathogenic material. We show that our approach can detect more than one smell at a time. We build and test our approach on Gantt Project v1.10.2 and Xerces v2.7.0, for which manually-validated and publicly available smells exist. The results show a significant improvement in detection time, precision, and recall, in comparison to the state–of–the–art approaches.

Identifying online credit card fraud using Artificial Immune Systems

Abstract: Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag ‘non standard’ transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.

Design of an artificial immune system based on Danger Model for fault detection

Abstract: This paper presents a methodology that enables fault detection in dynamic systems based on recent immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The fault detection central challenge is determining the difference between normal and potential harmful activities at dynamic systems. A promising solution is emerging in the form of Artificial Immune Systems (AIS). The Danger Model (DM) proposes that the immune system reacts not against self or non-self but by threats generated into the organism: the danger signals. DM-based fault detection system proposes a new formulation for a fault detection system. A DM-inspired methodology is applied to a fault detection benchmark provided by DAMADICS to compare its relative performance to others algorithms. The results show that the strategy developed is promising for incipient and abrupt fault detection in dynamic systems.

Detecting interest cache poisoning in sensor networks using an artificial immune algorithm

Abstract: The objective of this paper is to investigate how a Danger Theory based Artificial Immune System--in particular the Dendritic Cell Algorithm (DCA) can detect an attack on a sensor network. The method is validated using two separate implementations: a simulation using J-sim and an implementation for the T-mote Sky sensor using TinyOS. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and investigates how the DCA can be applied to detect this attack in a series of experiments.

Scale invariance of immune system response rates and times: perspectives on immune system architecture and implications for artificial immune systems

Abstract: Most biological rates and times decrease systematically with increasing organism body size. We use an ordinary differential equation (ODE) model of West Nile Virus in birds to show that pathogen replication rates decline with host body size, but natural immune system (NIS) response rates do not change systematically with body size. The scale-invariant detection and response of the NIS is surprising since the NIS has to search for small quantities of pathogens through larger physical spaces in larger organisms, and also respond by producing larger absolute quantities of antibody in larger organisms. We hypothesize that the NIS has evolved an architecture to efficiently neutralize pathogens. We investigate three different hypothesized NIS architectures using an Agent Based Model (ABM). We find that a sub-modular NIS architecture, in which lymph node number and size both increase sublinearly with body size, efficiently balances the tradeoff between local pathogen detection and global response. This leads to nearly scale-invariant detection and response consistent with experimental data. Similar to the NIS, physical space and resources are also important constraints on distributed systems, for example low-powered robots connected by short-range wireless communication. We show that the sub-modular design principles of the NIS can be applied to problems such as distributed robot control to efficiently balance the tradeoff between local search for a solution and global response or proliferation of the solution. We demonstrate that the lymphatic network of the NIS efficiently balances local and global communication, and we suggest a new approach for Artificial Immune Systems (AIS) that uses a sub-modular architecture to facilitate distributed search.