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Based on Artificial Immunity Theory to Generate Pseudo Random-Sequence and Research on Encryption in Mobile Sensor

SENSOR LETTERS 2013年第11期11卷 2168-2170页

作者： Yue, Mingyu Sichuan Univ Chengdu 610039 Peoples R China Chongqing City Management Coll Chongqing 404100 Peoples R China

Pseudo random-sequence that is based on artificial immunity theory is a new way. From this way, we can extracts the generation seeds from the plaintext message by itself to generate pseudo random-sequence, and use these seeds to generate a pseudo random-sequence by an improved m-sequences generated way. It has good self-related characteristics and good statistical properties. If it has been for data encryption, it has good performance of resistant to cryptanalysis attack. In addition, the receiver can use the generation seeds to test the integrity of the data.

关键词： negative selection algorithm Longer Period Pseudo Random-Sequence Generation Seeds Stream Cipher Mobile Sensor

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Accuracy Evaluation of a Credit Card Fraud Detection System on Hadoop MapReduce

Accuracy Evaluation of a Credit Card Fraud Detection System ...

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5th Conference on Information and Knowledge Technology (IKT)

作者： Hormozi, Elham Hormozi, Hadi Akbari, Mohammad Kazem Javan, Morteza Sargolzaei Mazandaran Univ Sci & Technol Comp Engn & Informat Technol Babol Sar Iran Arak Univ Comp Engn & Informat Technol Arak Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Tehran Iran

ISBN: (纸本)9781467364904;9781467364898

In digitalization era, credit card fraud detection is of high significance to financial organizations. This paper discussed about credit card fraud detection by parallelizing of negative selection algorithm on the Cloud computing platform. We present performance evaluation of running the algorithm on the cloud by MapReduce framework and show it's dramatically results on real world financial data. We argue that, for the fraud detection rate, False negative rate, fraud catching rate (True Positive rate) and false alarm rate (False Positive rate), Cost and Hit rate that are the best metrics for a desirable credit card fraud detection system.

关键词： Credit card fraud detection Parallelize negative selection algorithm Cloud computing MapReduce

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Artificial Immune Systems Applied to Voltage Disturbance Diagnosis in Distribution Electrical Systems

Artificial Immune Systems Applied to Voltage Disturbance Dia...

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IEEE Grenoble PowerTech (POWERTECH) Conference

作者： Lima, F. P. A. Lotufo, A. D. P. Minussi, C. R. Coll Elect Engn Ilha Solteira Dept Elect Engn Ilha Solteira SP Brazil

ISBN: (纸本)9781467356688

This paper presents a method to voltage disturbance diagnosis in distribution electrical systems. This issue uses three phase voltage measures which are obtained at the substations to realize the system monitoring. The principal application is to aid the operation during faults, as well as to supervise the protection system. To evaluate the performance of the proposed method simulations were executed at EMTP software for two distribution systems containing 84 and 134 busses, respectively.

关键词： Electrical Distribution Systems Voltage Disturbance Diagnosis Artificial Immune Systems negative selection algorithm

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A neural networks-based negative selection algorithm in fault diagnosis

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NEURAL COMPUTING & APPLICATIONS 2008年第1期17卷 91-98页

作者： Gao, X. Z. Ovaska, S. J. Wang, X. Chow, M. Y. Aalto Univ Inst Intelligent Power Elect FIN-02150 Espoo Finland N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.

关键词： artificial immune systems negative selection algorithm neural networks anomaly detection fault diagnosis

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Artificial Immune Systems with negative selection Applied to Health Monitoring of Aeronautical Structures

Artificial Immune Systems with Negative Selection Applied to...

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2013 International Conference on Applied Mechanics,Fluid and Solid Mechanics(AMFSM 2013)

作者： Fernando Parra dos Anjos Lima Fábio Roberto Chavarette Adriano dos Santos e Souza Simone Silva Frutuoso de Souza Mara Lúcia Martins Lopes Mechanical Engineering Department Faculty of Engineering of Ilha Solteira (FEIS) Mathematics Department Faculty of Engineering of Ilha Solteira (FEIS) UNESP – Univ.Estadual Paulista Av. Brasil Electrical Engineering Department Faculty of Engineering of Ilha Solteira (FEIS)

In this paper we present a system for aircraft structural health monitoring based on artificial immune systems with negative *** by a biological process,the principle of discrimination proper/non-proper,identifies and characterizes the signs of structural *** main application of this method is to assist in the inspection of aircraft structures,to detect and characterize flaws and decision making in order to avoid *** proposed a model of an aluminum beam to perform the tests of the *** results obtained by this method are excellent,showing robustness and accuracy.

关键词： Health Monitoring Aeronautical structures Artificial Immune Systems negative selection algorithm

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A POLYMORPHIC CONVEX HULL SCHEME FOR negative selection algorithmS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第5A期8卷 2953-2964页

作者： Majd, Mahshid Hamzeh, Ali Hashemi, Sattar Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn & Informat Technol Shiraz Iran

Artificial Immune System (AIS) is known as the collection of all researches including computational models and knowledge discovery algorithms inspired by the natural immune system. One of the most widely used techniques in the field of artificial immune system is negative selection algorithm (NSA). The principle of this algorithm is finding a set of detectors which can discriminate the self and non-self areas. Each detector in NSA defines a subspace of problem space where no self data is located. As the obtained set of detectors paves the way of discriminating the self and non-self data, NSA finds its main application in anomaly detection problems. What significantly affects the detection performance of NSA is choosing a proper shape/representation for detectors to model the regularities of non-self area precisely in one hand, and on the other hand, number of generated detectors should be tuned in a delicate manner. Using a general and polymorphic representation scheme for covering non-self area can elegantly alleviate the above mentioned issues. This paper presents a novel representation method based on convex hulls, named CH-NSA, which is general enough to support polymorphic shapes with variable properties. Convex hull is a general form representation which supports not only regular symmetric shapes such as rectangles, spheres and ellipse but also irregular asymmetric shapes. The experimental results show that applying this new representation to well-known benchmark problems in the literature enhances the accuracy of NSA by significantly decreasing the number of needed detectors compared with the other common representation methods.

关键词： Artificial immune system negative selection algorithm Convex hull representation

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Performance Evaluation of a Fraud Detection System based Artificial Immune System on the Cloud

Performance Evaluation of a Fraud Detection System based Art...

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International Conference on Computer Science & Education

作者： Elham Hormozi Morteza Sargolzaei Javan Mohammad Kazem Akbari Hadi Hormozi Computer Engineering and Information Technology Mazandaran University of Science and Technology Babol Computer Engineering and Information Technology Amirkabir University of Technology (Tehran Polytechnic) Tehran Computer Engineering and Information Technology Islamic Azad University of Arak

ISBN: (纸本)9781467344647

Fraud is defined as the unlawful and intentional misrepresentation which can lead to actual or potential disadvantage to another individual or group. Fraud detection is a topic applicable to many industries including banking and financial sectors, insurance, law enforcement, and more. Credit card fraud is a major problem in the financial industry [1]. Therefore, real time fraud detection is a vital issue. Indeed, we implement the progress of negative selection algorithm an anomaly detection approach in AIS on the cloud. As a result of our experiments, in serial NSA the time of training phase is around 23800s whereas parallel NSA training phase time is 78s. Also, in parallel algorithm the detection rate increased around%50 compared to the serial algorithm. But we concluded false positive rate a little raised that compared to increase the detection rate is almost negligible. Tests are done with real data sets, and all executions are run using mapreduce and apache hadoop.

关键词： Fraud Fraud detection Credit card fraud Artificial immune system Cloud computing negative selection algorithm

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Novel design concepts for network intrusion systems based on dendritic cells processes

Novel design concepts for network intrusion systems based on...

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第八届全国青年岩土力学与工程会议暨青年华人岩土工程论坛

作者： RICHARD M R TAN Guan-zheng ONGALO P N F CHERUIYOT W School of Information Science and Engineering Central South University Changsha 410083 China

An abstraction and an investigation to the worth of dendritic cells (DCs) ability to collect, process and present antigens are ***, this ability is shown to provide a feature reduction mechanism that could be used to reduce the complexity of a search space, a mechanism for development of highly specialized detector sets as well as a selective mechanism used in directing subsets of detectors to be activated when certain danger signals are *** is shown that DCs, primed by different danger signals, provide a basis for different anomaly detection *** antigen-peptides are developed based on different danger signals present, and these peptides are presented to different adaptive layer detectors that correspond to the given danger *** are then undertaken that compare current approaches, where a full antigen structure and the whole repertoire of detectors are used, with the proposed *** results indicate that such an approach is feasible and can help reduce the complexity of the problem by significant *** also improves the efficiency of the system, given that only a subset of detectors are involved during the detection *** several different sets of detectors increases the robustness of the resulting *** developed based on peptides are also highly discriminative, which reduces the false positives rates, making the approach feasible for a real time environment.

关键词： artificial immune systems network intrusion detection anomaly detection feature reduction negative selection algorithm danger model

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Experimental Platform for Faults Detection in Fuzzy Controllers based on Artificial Immune Systems

Experimental Platform for Faults Detection in Fuzzy Controll...

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IEEE International Instrumentation and Measurement Technology Conference

作者： Robson Pacifico Guimaraes Lima Bruno Willian de Souza Arruda Cleonilson Protasio de Souza Department of Electroeletronic Federal Institute of Pernambuco IFPE Afogados da Ingazeira Pernambuco Brazil Department of Electrical Engineering Federal University of Paraiba UFPB Joao Pessoa Paraiba Brazil

ISBN: (纸本)9781467346214

An Artificial-Immune-System (AIS) based anomaly detection system applied to Water Supply System (WSS) is presented. At normal working, the pressure level into the WSS is controlled by a Fuzzy Control System. As the WSS is composed of pressure sensors, valves, pumps, and other devices, faults in these devices causing abnormal disturbances can occur. An algorithm of AIS, namely, the negative selection algorithm (NSA), is the base of the proposed anomaly detection system. The NSA verifies abnormal system conditions based on the normal system conditions. Experimental results show that the proposed system is effective in order to detect anomaly.

关键词： negative selection algorithm Fuzzy Controller Fault Detection

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A New negative selection algorithm and its Application in the Fault Diagnosis of the Pump-jack

A New Negative Selection Algorithm and its Application in th...

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7th World Congress on Intelligent Control and Automation

作者： Ren, Weijian Wang, Qiong Daqing Petr Inst Daqing 163318 Heilongjiang Pr Peoples R China

ISBN: (纸本)9781424421138

The fault diagnosis of the pump-jack is as the background in the paper. A new negative selection algorithm is proposed combining the advantage of genetic algorithm and simulating annealing algorithm. The initial values of the detectors are initialized by genetic algorithm, thus the diversity of the detectors is retained, the scope of detecting is enlarged. The variable radius of detectors is introduced to cover non-self space efficiently. The redundancy of detectors is reduced and the efficiency is improved by using simulating annealing. The method is used to diagnosis the faults of the pump-jack. The results are better. Especially the method can diagnosis unknown faults. It has great potentiality.

关键词： negative selection algorithm fault diagnosis simulating annealing algorithm variable radius

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