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A self-adaptive negative selection algorithm used for anomaly detection

Progress in Natural Science:Materials International 2009年第2期19卷 261-266页

作者： Jinquan Zeng,Xiaojie Liu,Tao Li,Caiming Liu,Lingxi Peng,Feixian Sun Department of Computer Science,Sichuan University,Chengdu 610065,China [a]Department of Computer Science Sichuan University Chengdu 610065 China

A novel negative selection algorithm (NSA),which is referred to as ANSA,is presented. In many actual anomaly detection systems,the training data are just partially composed of the normal elements,and the self/nonself space often varies over time. Therefore,anom-aly detection system has to build the profile of the system based on a part of self elements and adjust itself to adapt those variables. However,previous NSAs need a large number of self elements to build the profile of the system,and lack adaptability. In order to over-come these limitations,the proposed approach uses a novel technique to adjust the self radius and evolve the nonself-covering detectors to build an appropriate profile of the system. To determine the performance of the approach,the experiments with the well-known data-set were performed. Results exhibited that our proposed approach outperforms the previous techniques.

关键词： Artificial immune system Anomaly detection negative selection algorithm

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Re-editing and Censoring of Detectors in negative selection algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2009年第3期2卷 298-311页

作者： Gao, X. Z. Ovaska, S. J. Wang, X. Aalto Univ Dept Elect Engn FI-02150 Espoo Finland

The negative selection algorithm (NSA) is a kind of novelty detection method inspired by the biological self/nonself discrimination principles. In this paper, we propose two new schemes for the detectors re-editing and censoring in the NSA. The detectors that fail to pass the negative selection phase are re-edited and updated to become qualified using the Differential Evolution (DE) method. In the detectors censoring, the qualification of all the detectors is evaluated, and only those appropriate ones are retained. Prior knowledge of the anomalous data is utilized to discriminate the detectors so that their anomaly detection performances can be improved. The effectiveness of our detectors re-editing and censoring approaches is examined with both artificial signals and a practical bearings fault detection problem.

关键词： artificial immune systems negative selection algorithm differential evolution anomaly detection fault detection

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A new fault diagnosis approach for induction motor using negative selection algorithm and its real-time implementation on FPGA

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期34卷 689-701页

作者： Aydin, Ilhan Karakose, Mehmet Karakose, Ebru Akin, Erhan Firat Univ Dept Comp Engn Elazig Turkey Firat Univ Civil Aviat Coll Elazig Turkey

Condition monitoring of induction motors has become an important issue of researchers in recent years. The detection of broken rotor bar faults is one of the most difficult problems and many methods have been proposed for accurate detection of these faults. In recent years, some studies have been proposed to improve the diagnostic performance by combining different signal processing techniques. However, the proposed methods require high computational complexity. The contribution of this study is threefold. The first one is a new feature extraction method to distinguish different motor conditions by analyzing one phase of induction motor steady-state current. The phase space of the feature signal is constructed by using determined time delay and embedding dimension. The second contribution is to optimize the detectors of the negative selection algorithm by clonal selection. The proposed clonal selection algorithm minimizes the overlap between the detectors and maximizes the coverage of the anomalous data. Because the feature extraction method and test stage of the negative selection algorithm have low computational complexity, the last contribution is Field-Programmable-Gate-Array (FPGA) implementation for online detection of rotor related faults. The obtained results indicate that the proposed methodology demonstrates a high performance for diagnosis of rotor faults in induction motors.

关键词： negative selection algorithm clonal selection FPGA induction motors fault detection

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DENSA: An effective negative selection algorithm with flexible boundaries for self-space and dynamic number of detectors

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 62卷 359-372页

作者： Fouladvand, Sajjad Osareh, Alireza Shadgar, Bita Pavone, Mario Sharafi, Siyamack Acad Ctr Educ Culture & Res Lorestan Iran Langara Coll Dept Comp Sci & Informat Syst Vancouver BC Canada Simon Fraser Univ Sch Comp Sci Burnaby BC Canada Univ Catania Dept Math & Comp Sci Catania Italy

The negative selection algorithm is an anomaly detection technique inspired by the self-nonself discrimination behavior observed in the Biological Immune System. The most controversial question of these algorithms is their poor performance on real world applications. To overcome such limitation this research work focuses on generating more efficient detectors through a more flexible boundary for self patterns. Rather than applying conventional affinity measures, the detectors are generated benefiting from a Gaussian Mixture Model (GMM) fitted on normal space. From the GMM capabilities the algorithm is able to dynamically determine efficient subsets of detectors. In order to evaluate the efficiency and robustness of the proposed algorithm, different data sets have been examined as benchmark, including 2D synthesis data sets. Furthermore, for evaluating the capability and effectiveness of the proposed algorithm on real-world problems, it has been performed and tested for detecting anomalies in archaeological sites located in Lorestan, Iran. The experimental results prove how the proposed approach helps the negative selection algorithm to improve its detection capability, because the detectors can be efficiently distributed into the non-self space. It is important to note how this research work presents also a first analysis of the anomaly detection capabilities in the field of archaeology, introducing a novel application method, which can be efficiently used by the archaeologists for interpreting their growing amount of data and draw valuable conclusions about the historical past. Finally, in order to analyse the convergence and the running time of the proposed algorithm, a study has been conducted using the classical Time-To-Target plots, which present a standard graphical methodology for data analysis based on the comparisons between the empirical and theoretical distributions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Anomaly detection Gaussian Mixture Model Artificial Immune System negative selection algorithm DENSA

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NUMERICAL negative selection algorithm

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2010年第5期19卷 703-712页

作者： Cai Tao Ju Shiguang Niu Dejiao Jiangsu Univ Dept Comp Zhenjiang 212013 Jiangsu Peoples R China

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

关键词： negative selection algorithm artificial immune algorithm

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A negative selection algorithm with online adaptive learning under small samples for anomaly detection

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NEUROCOMPUTING 2015年第PartB期149卷 515-525页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

The training stage and testing stage of traditional negative selection algorithm (NSA) are mutually independent, and NSA lacks continuous learning ability. Its detector cannot completely cover the non-self space. A new NSA with online adaptive learning under small training samples, OALI-detector, was proposed in this paper. I-detector can fully separate the self space from the non-self space with an appropriate self radius. It can adapt itself to real-time change of self space during the testing stage. The experimental comparison among I-detector, V-detector, and other anomaly detection algorithms in two artificial and Iris datasets shows that the I-detector can obtain the highest detection rate in most cases. The experimental comparison between OALI-detector and V-detector on Iris datasets shows that when overfitting does not occur, the OALI-detector can obtain the highest and lowest false alarm rates, even if only one self sample is used for training. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Interface detector Online adaptive learning

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Hybrid email spam detection model with negative selection algorithm and differential evolution

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期28卷 97-110页

作者： Idris, Ismaila Selamat, Ali Omatu, Sigeru Univ Teknol Malaysia Knowledge Econ Res Alliance SERG Johor Baharu 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Johor Malaysia Dept Elect Informat & Commun Engn Asahi Ku Osaka 5358585 Japan

Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA;code named NSA-DE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSA-DE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： negative selection algorithm Differential evolution email Spam Non-spam Detector generation

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Anomaly process detection using negative selection algorithm and classification techniques

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EVOLVING SYSTEMS 2021年第3期12卷 769-778页

作者： Hosseini, Soodeh Seilani, Hossein Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Mahani Math Res Ctr Kerman Iran Bahmanyar Univ Kerman Sch Comp Engn Kerman Iran

Artificial immune system is derived from the biological immune system. This system is an important method for generating detectors that include self-adaption, self- regulation and self-learning which have self/non-self-detection features. This method is used in anomaly process detection where the anomaly is non-self in the system. We present a new combining technique for anomaly process detection. This combined technique is a unification of both negative selection and classification algorithm. The main aim of the proposed techniques is to increase the accuracy in this system while decreasing its training time. In this research, CICIDS 2017 and NSL-KDD dataset with different sets of features and the same number of detectors are used. This paper presents a framework for detecting anomaly processes on a host base computer system which is established on the artificial immune system. We evaluate our technique using machine learning algorithms such as: logistic regression, random forest, decision tree and K-neighbors. Moreover, we use WEKA tool classification to perform a correlation based feature selection on the dataset.

关键词： Artificial immune system negative selection algorithm Anomaly detection Intrusion detection Machine learning

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Parameter analysis of negative selection algorithm

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INFORMATION SCIENCES 2017年 420卷 218-234页

作者： Wen, Chen Tao, Li Sichuan Univ Coll Cybersecur Chengdu 610065 Sichuan Peoples R China

The performance of negative selection algorithm (NSA) is affected by several parameters such as its self radius and expected coverage. Traditionally, most of the parameters are selected based on experience, resulting in varying performances of NSAs. In the present study, the NSA parameters were analyzed based on a new set of evaluation criteria. The criteria were used to calculate the self radius by an iterative algorithm using some non self samples as reference points for the nonself boundary. The difficult problem of estimating the overlap volume between immune hyperspheres was solved by the Monte Carlo method. It was found that the error of the estimated nonself coverage was the primary cause of the poor performance of some existing artificial immune systems. A confidence estimation method was thus developed for improving the estimation precision. Experiments were performed in which both fixed- and variable-radius detectors were generated using different parameter value combinations. The results revealed that a significantly higher NSA performance could be achieved by the proposed parameter calculation method. Statistical analysis of the experimental results further confirmed the effectiveness and practicability of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.

关键词： negative selection algorithm Parameter estimation Artificial immune system Detector Coverage

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A negative selection algorithm for classification and reduction of the noise effect

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APPLIED SOFT COMPUTING 2009年第1期9卷 431-438页

作者： Igawa, K. Ohashi, H. Univ Tokyo Sch Engn Dept Syst Innovat Tokyo Japan

Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. In the last decade, applications of AIS have been studied in various fields. In the application of change/anomaly detection, negative selection algorithms of AIS have been successfully applied. However, negative selection algorithms are not appropriate for multi-class classification problems, because they do not have a mechanism to minimize the danger of overfitting and oversearching. In this paper, we propose a new algorithm to overcome this drawback and to extend the application area of negative selection algorithms to multi-class classification. The algorithm we propose is named Artificial negative selection Classifier (ANSC). We investigate the tolerance of ANSC against noise, and introduce a method to reduce the effect of noise into ANSC. The accuracy and data reduction are compared with those from the Artificial Immune Recognition System (AIRS), which is a well known and effective classifier of AIS. The results show that our algorithm is useful for classification problems and the reduction of the noise effect. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Artificial immune systems negative selection algorithm Artificial negative selection classifier Classification Noise AIRS

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