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1 – 10 of over 1000Anomaly detection of network attacks has become a high priority because of the need to guarantee security, privacy and reliability. This work aims to describe both intelligent…
Abstract
Purpose
Anomaly detection of network attacks has become a high priority because of the need to guarantee security, privacy and reliability. This work aims to describe both intelligent immunological approaches and traditional monitoring systems for anomaly detection.
Design/methodology/approach
Author investigated different artificial immune system (AIS) theories and proposes how to combine different ideas to solve problems of network security domain. An anomaly detection system that applies those ideas was built and tested in a real time environment, to test the pros and cons of AIS and clarify its applicability. Rather than building a detailed signature based model of intrusion detection system, the scope of this study tries to explore the principle in an immune network focusing on its self-organization, adaptive learning capability, and immune feedback.
Findings
The natural immune system has its own intelligent mechanisms to detect the foreign bodies and fight them and without it, an individual cannot live, even just for several days. Network attackers evolved new types of attacks. Attacks became more complex, severe and hard to detect. This results in increasing needs for network defense systems, especially those with ability to extraordinary approaches or to face the dynamic nature of continuously changing network threats. KDD CUP'99 dataset are used as a training data to evaluate the proposed hybrid artificial immune principles anomaly detection. The average cost of the proposed model was 0.1195 where that the wining of KDD99 dataset computation had 0.233.
Originality/value
It is original to introduce investigation on the vaccination biological process. A special module was built to perform this process and check its usage and how it could be formulated in artificial life.
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Hongwei Mo, Dongmei Fu and Lifang Xu
The purpose of this paper is to verify that improved immune network can be used to design new controller for engineering.
Abstract
Purpose
The purpose of this paper is to verify that improved immune network can be used to design new controller for engineering.
Design/methodology/approach
First, the definition of artificial immune controller is given out. Second, the disadvantage of Varela immune network which is not fit for control system is pointed out. Third, based on the analysis, the Varela immune network is modified for the purpose of designing controller with the mechanisms of immune network. And an immune controller based on improved Varela immune network (improved Varela immune network model (IVINM)‐AIC) is designed out. Its theoretic background is described in detail.
Findings
Based on the theoretic analysis and experiment of motor speed control, it is found that Varela immune work can be used to design immune controller. The experiments results show that IVINM‐AIC is much more robust, stable and anti‐delay and less overshoot than classical proportion, integration, and differentiation controller. It is good at controlling nonlinear system which is single input single output (SISO) system. The limitation of IVINM‐AIC is that it is used for simple SISO system.
Originality/value
The theoretic analysis of improved Varela immune network controller is original and it is useful for the analysis and design of new and complex immune controller. The experiment design is useful for comparison of new test in future.
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Adil Togayev, Mario Perhinschi, Hever Moncayo, Dia Al Azzawi and Andres Perez
This paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system…
Abstract
Purpose
This paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system failures/damages.
Design/methodology/approach
The approach is based on building an artificial memory, which represents self- (nominal conditions) and non-self (abnormal conditions) within the artificial immune system paradigm. Self- and non-self are structured as a set of memory cells consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight. The accommodation algorithm is based on the cell in the memory that is the most similar to the in-coming measurement. Once the best match is found, control commands corresponding to this match are extracted from the memory and used for control purposes.
Findings
The results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and capability of the artificial-immune-system-based scheme to accommodate an actuator malfunction, maintain control and complete the task.
Research limitations/implications
This paper concentrates on investigation of the possibility of extracting compensatory pilot commands. This is a preliminary step toward a more comprehensive solution to the aircraft abnormal condition accommodation problem.
Practical implications
The results demonstrate the effectiveness of the proposed approach using a motion-based flight simulator for actuator and sensor failures.
Originality/value
This research effort is focused on investigating the use of the artificial immune system paradigm for control purposes based on a novel methodology.
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A strong and fast‐cycle innovation system has been developed to counter the ongoing threat of computer viruses within computer systems employing vulnerable operating systems…
Abstract
Purpose
A strong and fast‐cycle innovation system has been developed to counter the ongoing threat of computer viruses within computer systems employing vulnerable operating systems. Generally, however, the innovative applications that develop in response to each generation of computer virus can be seen as a reactive, rather than proactive, critical response. The paper seeks to present a critique of the innovation system that has emerged to combat computer viruses by comparing it with its natural system namesake, the human anti‐viral immune system. It is proposed that the relevance of this analogy extends beyond this case to innovation systems more generally.
Design/methodology/approach
This paper discusses the biological theory related to the human body's immune system and how immune systems might be mimicked in the development of security systems and anti‐virus software. The paper then outlines the biomimicry framework that can be used for scoping the development and features of the security systems and software, including the population of the framework segments. The implications of biomimetic approaches in the wider innovation management literature are discussed.
Findings
Some commercial security products that are undergoing evolutionary development and current research and development activities are used to augment the biomimetic development framework and explicate its use in practice. The paper has implications for the manner in which the objectives of innovation systems are defined. There is implicit criticism of linear models of innovation, that by their nature ignore the recursive and/or adaptive processes evident in natural systems.
Originality/value
This is the first paper, to the best of the authors' knowledge, that discusses the application of natural systems and biomimetics to broaden the scope of innovation process design, and link its findings back to the wider innovation literature.
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Roberto Outa, Fabio Roberto Chavarette, Vishnu Narayan Mishra, Aparecido C. Gonçalves, Luiz G.P. Roefero and Thiago C. Moro
In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of…
Abstract
Purpose
In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people’s lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment; however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM).
Design/methodology/approach
The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detection.
Findings
The present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.
Originality/value
This study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.
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M.V.A. Raju Bahubalendruni, B.B.V.L. Deepak and Bibhuti Bhusan Biswal
The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates.
Abstract
Purpose
The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates.
Design/methodology/approach
This proposed study is carried out by using two artificial immune system-based models, namely, Bone Marrow Model and Negative Selection Algorithms, to achieve the following objectives: to obtain the possible number of assembly sequences; to obtain the feasible assembly sequences while considering different assembly predicates; and to obtain an optimal feasible assembly sequence.
Findings
Proposed bone-marrow model determines the possible assembly sequences to ease the intricacy of the problem formulation. Further evaluation has been carried out through negative-selection censoring and monitoring models. These developed models reduce the overall computational time to determine the optimal feasible assembly sequence.
Originality/value
In this paper, the novel and efficient strategies based on artificial immune system have been developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product using assembly attributes. The introduced methodology has proven its effectiveness in achieving optimal assembly sequence with less computational time.
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Pablo A.D. Castro and Fernando J. Von Zuben
The purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the…
Abstract
Purpose
The purpose of this paper is to apply a multi‐objective Bayesian artificial immune system (MOBAIS) to feature selection in classification problems aiming at minimizing both the classification error and cardinality of the subset of features. The algorithm is able to perform a multimodal search maintaining population diversity and controlling automatically the population size according to the problem. In addition, it is capable of identifying and preserving building blocks (partial components of the whole solution) effectively.
Design/methodology/approach
The algorithm evolves candidate subsets of features by replacing the traditional mutation operator in immune‐inspired algorithms with a probabilistic model which represents the probability distribution of the promising solutions found so far. Then, the probabilistic model is used to generate new individuals. A Bayesian network is adopted as the probabilistic model due to its capability of capturing expressive interactions among the variables of the problem. In order to evaluate the proposal, it was applied to ten datasets and the results compared with those generated by state‐of‐the‐art algorithms.
Findings
The experiments demonstrate the effectiveness of the multi‐objective approach to feature selection. The algorithm found parsimonious subsets of features and the classifiers produced a significant improvement in the accuracy. In addition, the maintenance of building blocks avoids the disruption of partial solutions, leading to a quick convergence.
Originality/value
The originality of this paper relies on the proposal of a novel algorithm to multi‐objective feature selection.
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Helder Ken Shimo and Renato Tinos
– The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems (AISs).
Abstract
Purpose
The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems (AISs).
Design/methodology/approach
The proposed operators are applied in substitution to the suppression and mutation operators used in AISs. The proposed mechanisms were tested in the opt-aiNet, a continuous optimization algorithm inspired in the theories of immunology. The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population. This procedure is computationally expensive, as the Euclidean distances between every possible pair of candidate solutions must be computed. This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality (SOC) phenomenon, which is less dependent on parameter selection. This work also proposes the use of the q-Gaussian mutation, which allows controlling the form of the mutation distribution during the optimization process. The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem: the rigid docking of proteins.
Findings
The proposed suppression operator presented some limitations in unimodal functions, but some interesting results were found in some highly multimodal functions. The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark, and also in the docking problem.
Originality/value
First, the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet. Second, the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and, as a consequence, a better performance when compared to the traditional Gaussian mutation.
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Cun‐Cen Li, Ming Yang, Ya‐Fei Pang and Shi‐Yang Li
The purpose of this paper is to propose an optimization method by combining artificial immune algorithm and finite element analysis to find the optimal exciting electrode of a…
Abstract
Purpose
The purpose of this paper is to propose an optimization method by combining artificial immune algorithm and finite element analysis to find the optimal exciting electrode of a piezoceramic plate type ultrasonic motor vibrator.
Design/methodology/approach
The artificial immune algorithm is selected as optimizer for its merit of fast convergence to global optimal solution. The finite element analysis is used to calculate the motion trajectory of contact point. The objective function is the work that the vibrator does to rotor. The design variables are the boundaries of exciting electrode on piezoceramic plate vibrator surface.
Findings
The calculated results and the experimental results show that using this method, both the position and the size of optimal exciting electrode of this ultrasonic motor can be quickly and accurately determined.
Originality/value
In order to successfully design an ultrasonic motor, both the position and the size of the exciting electrode must be investigated, so as to change more electric energy into mechanical energy. In this paper, an optimization method by combining artificial immune algorithm and finite element analysis is proposed for the exciting location optimization of a piezoceramic plate type ultrasonic motor to obtain large power output.
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Hamid Reza Golmakani and Ali Namazi
In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way…
Abstract
Purpose
In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way that the completion time of jobs and preventive maintenance tasks is minimized.
Design/methodology/approach
An heuristic approach based on artificial immune algorithm is proposed for solving the multiple‐route job shop‐scheduling problem subject to fixed periodic and age‐dependent preventive maintenance tasks. Under fixed periodic assumption, the time between two consecutive preventive maintenance tasks is assumed constant. Under age‐dependent assumption, a preventive maintenance task is triggered if the machine operates for a certain amount of time. The goal is to schedule the jobs and preventive maintenance task subject to makespan minimization.
Findings
In addition to presenting mathematical formulation for the multiple‐route job shop‐scheduling problem, this paper proposes a novel approach by which one can tackle the complexity that is raised in scheduling and sequencing the jobs and the preventive maintenance simultaneously and obtain the required schedule in reasonable time.
Practical implications
Integrating preventive maintenance tasks into the scheduling procedure is vital in many manufacturing systems. Using the proposed approach, one can obtain a schedule that defines the production route through which each part is processed, the time each operation must be started, and when preventive maintenance must be carried out on each machine. This, in turn, results in overall manufacturing cost reduction.
Originality/value
Using the approach proposed in this paper, good solutions, if not optimal, can be obtained for scheduling jobs and preventive maintenance task in one of the most complicated job shop configurations, namely, multiple‐route job shop. Thus, the approach can dominate all other simpler configurations.
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