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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Rakotomamonjy, Thomas Le Boulanger, Morgane Off Natl Etud & Rech Aerosp French Aerosp Lab FR-13300 Salon De Provence France Ecole Air FR-13300 Salon De Provence France

ISBN: (纸本)9781479967735

In order to detect the failure of an actuator of a helicopter swashplate, a simple detection method based on a negative selection algorithm (NSA) is developed. This technique, inspired by existing rules found in the biological immunity, rely on a set of elemental detectors which have been trained in order to recognize only the non-self (i.e. faulty) behaviors of the system, without needing an explicit model of the system dynamics. A simple model for the actuator failure is developed and coupled with a helicopter flight dynamics model, and some trajectories for the algorithm training and validation are simulated. A procedure for improving the efficiency of the algorithm by introducing weighting coefficients to the detection function is proposed and successfully tested. Moreover, the performance of the algorithm is slightly improved by observing the covariance of the flight dynamics states, instead of the variables themselves.

关键词： actuators aircraft control failure (mechanical) fault diagnosis helicopters NSA algorithm efficiency improvement algorithm training algorithm validation biological immunity detection function elemental detectors flight dynamics states helicopter actuator fault detection helicopter flight dynamics model helicopter swashplate actuator failure detection negative selection algorithm nonself faulty system behavior recognition weighting coefficients Actuators Blades Detectors Fault detection Helicopters Rotors Trajectory Helicopters actuators Defect detection Weighting coefficients Aircraft control trajectory Rotors Blades Fault diagnosis negative selection Flight dynamics detector

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Towards Efficient and Effective negative selection algorithm: A Convex Hull Representation Scheme

Towards Efficient and Effective Negative Selection Algorithm...

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8th International Conference of Simulated Evolution and Learning

作者： Majd, Mahshid Shoeleh, Farzaneh Hamzeh, Ali Hashemi, Sattar Shiraz Univ Dept Comp Sci & Engn Shiraz Iran

ISBN: (纸本)9783642172977

negative selection algorithm (NSA) is one of several algorithms inspired by the principles of natural immune system. The algorithm received the researchers attention due to its applicability in various research areas and a number of valuable efforts are made to increase the effectiveness and efficiency of it. The heart of NSA is to somehow find rules called detectors to discriminate self and anomaly areas. Each detector in NSA defines a subspace of problem space where no self data is located. One of the major issues in NSA is detector's shape or representation of detectors which can affect the detection performance significantly. This paper for the first time proposes a new representation for detectors based on convex hull. Since convex hull is a general form of other geometric shapes, it retains the benefits of other shapes meanwhile it provides some new features like the asymmetric shape. Experimental results show a significant enhancement in the accuracy of negative selection algorithm compared to other common representation shapes.

关键词： negative selection algorithm Convex hull Representation Detector Representation

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Augmented negative selection algorithm with Complete Random Subspace Technique for Anomaly Detection 2

Augmented Negative Selection Algorithm with Complete Random ...

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2nd IEEE International Conference on Information and Computer Technologies (ICICT)

作者： Wang, Yi Li, Tao Zhu, Fangdong Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Sichuan Univ Coll Cybersecur Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728133232

negative selection algorithm is an important algorithm in the artificial immune system, inspired by the biological immune system. Traditional negative selection algorithms lack adaptive learning ability in high-dimensional space due to data sparsity and meaningless distance measurement. To solve these problems, an improved negative selection algorithm called negative selection algorithm with Complete Random Subspace Technique (RS-NSA), is proposed in this paper. It adopts a bootstrap method to reduce the rate of misclassification resulting from the anomalies covered by the regions of normal samples. By using the complete random subspace technology, it reduces dimensionality to alleviate the curse of dimensionality. In addition, the ensemble learning technique is introduced to improve accuracy, in which component classifiers can be replaced by any negative selection algorithm. Empirical evaluation on UCI datasets reveals that, compared with V-detector, our proposed method can not only achieve a higher detection rate and a lower false alarm rate, but also shorten the training time.

关键词： negative selection algorithm artificial immune system anomaly detection ensemble learning complete random subspace

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Enhanced negative selection algorithm for Malicious Node Detection in MANET 9

Enhanced Negative Selection Algorithm for Malicious Node Det...

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9th International Conference on Advanced Computing (ICoAC)

作者： Raja, Kathiroli Natarajan, Indra Anna Univ Dept Comp Technol Madras 600044 Tamil Nadu India

ISBN: (纸本)9781538643495

Intrusion detection in MANETS is inevitable as these networks are dynamic, open and no central authority to monitor the nodes' activities. Since the network is open, it allows the node to enter and exit at any point time which provides ease of access to all nodes. Misbehaving nodes utilize these opportunity to enter into the network and cause disruption. This requires building appropriate IDS to monitor the activities of nodes in the network. An Artificial Immune System (AIS) analogous to Human Immune System (HIS) is presented to provide appropriate IDS. The major role of AIS is to classify the samples as self (which are specific to the system) and non-self (which are the foreign body to the system) by means of proposed Enhanced negative selection algorithm (ENSA). The non-self patterns act as a defense mechanism to detect the anomalies caused by invaders. ENSA regards the immune system as a classification system for matching patterns. The proposed ENSA optimizes the detector generation process and performs accurate and precise classification of the network traffic. The ENSA includes two operation detector generation and classification systems. ENSA adapts Particle Swarm Optimization (PSO) technique to enhance the random detector generation to achieve maximum coverage in the non-self space. The classification involves matching the bit strings of the antigen with the generated detectors. The strings which match with the defined detector set are classified as intruders (non-self). The performance result shows that ENSA significantly outperforms other traditional classification algorithms in terms of classification accuracy, detection rate and classification time.

关键词： Artificial immune system Anomaly detection Mobile ad hoc networks negative selection algorithm

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A Novel Approach of Detector Generation for Real-Valued negative selection algorithm

A Novel Approach of Detector Generation for Real-Valued Nega...

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Hui, Ronghua Lou, Peihuang Zhao, Peng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9783037852828

The detector sets generated by Real-Valued negative selection algorithm (RNSA) are usually numerous, without optimization, and can not work under real-time condition. Thus, a novel approach of detector generation for RNSA based on Clonal selection and Neighborhood Search (CSNS-RNSA) is proposed. Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence. The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search. The resulting detector sets achieved a good coverage of non-self space, and also significantly reduced the number of detector sets, thus overcome the limitations of original RNSA. Finally, experiments verify the effectiveness of the algorithm.

关键词： negative selection algorithm Detector Generation Clonal selection Neighborhood Search

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Ransomware Detection Based on V-detector negative selection algorithm

Ransomware Detection Based on V-detector Negative Selection ...

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International Conference on Security, Pattern Analysis, and Cybernetics (ICSPAC)

作者： Lu, Tianliang Zhang, Lu Wang, Shunye Gong, Qi Peoples Publ Secur Univ China Inst Informat Technol & Network Secur Beijing Peoples R China

ISBN: (纸本)9781538630167

As a new type of malicious software, ransomw are is one of the biggest security threats in recent years. Inspired by biological immune system, a ransomware detection method based on V-detector negative selection algorithm with mutation optimization is proposed, which is referred to op-RDVD. The behavioral features of ransomware are extracted through dynamic analysis, such as hard disk reading and writing, the document encryption and deletion, etc. Some of benign samples are used to build the self space. The variable-sized detectors are generated both randomly and extracted from ransomware. To improve the ransomware detection accuracy and efficiency, optimize the space distribution of detectors through clone and mutation, achieving maximized coverage of non-self space and minimized overlapping among detectors. The experimental results show that our algorithm has better detection ability than that of the previous method.

关键词： ransomware detection artificial immune system V-detector negative selection algorithm

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Detection of Induction Motors Rotor Faults by Using negative selection algorithm Based on Park's Vector Approach 21

Detection of Induction Motors Rotor Faults by Using Negative...

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International Conference on Electrical Machines (ICEM)

作者： Bilgin, Osman Ogut, Murat Arabaci, Hayri Selcuk Un Fac Engn Dept Elect Elect Engn TR-42075 Konya Turkey Selcuk Un Akoren Vocat Sch Dept Elect & Automat TR-42460 Konya Turkey

ISBN: (纸本)9781479943883

The stator current signals of induction motors are often used to detect the broken rotor bar faults. However, the detection work becomes rather tedious owing to the fact that the fundamental supply frequency is much greater and quite close to the fault frequency. To get rid of this difficulty, the use of filters is often employed. Besides the fundamental supply frequency, the filter methods used tend to press on the fault frequency and necessitate a lot of calculations. Several studies on methods detecting rotor faults exist in the literature. Unlike literature, this study applies the negative selection algorithm on training and testing schemes of the a, b, c stator phase currents subjected to Park transformation after changing them into the id and iq magnitudes. The use of Park transformation eliminates the need of filter designs and lessens calculation load for the algorithm whereas application of the negative selection algorithm helps with detection of both normal and fault values in the system. Backed with the experimental results, the method proposed in this study has shown the effects, accuracy and applicability of the algorithm in detecting broken rotor bar faults in induction motors.

关键词： Broken rotor bar fault detection induction motors artificial immune system negative selection algorithm Park vector

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A negative selection algorithm with hypercube interface detectors for anomaly detection

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APPLIED SOFT COMPUTING 2024年 154卷

作者： Gu, Ming Li, Dong Liu, Jia Shan, Wangweiyi Liu, Shulin Changzhou Univ Sch Petr & Nat Gas Engn Changzhou 213164 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters.

关键词： Anomaly detection Artificial immune algorithm negative selection algorithm Biological immune system Hypercube interface detectors

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HD-NSA: A real-valued negative selection algorithm based on hierarchy division

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APPLIED SOFT COMPUTING 2021年 112卷 107726-107726页

作者： He, Junjiang Chen, Wen Li, Tao Li, Beibei Zhu, Yongbin Huang, Meng Sichuan Univ Coll Cybersecur 24 South Sect 1Yihuan Rd Chengdu Peoples R China

The negative selection algorithm (NSA) is an important algorithm for generating immune detectors in artificial immune systems. However, the original NSA randomly generates candidate detectors that produce a large number of redundant detectors, and it is difficult to cover the entire antibody space. Moreover, the randomly generated candidate detectors have to be compared with all the self-sets;therefore, the inefficient generation of the detector seriously influences the application of NSA. To overcome these defects, a real-valued NSA based on hierarchy division (HD-NSA) is proposed. First, the feature space is divided into self and non-self subgrids, and the center point of the non-self subgrid is specified as the candidate detector, and the specified candidate detector is compared with the self-antigens located in adjacent subgrids rather than with all the self-sets. Theoretical analysis demonstrated that the HD-NSA can effectively reduce the time complexity of the NSA algorithm. Furthermore, experiments on the Abalone data set show that the detector training time of HD-NSA decreased by 97.9%, 71.2%, 56.9% and 90.1%, respectively, compared with the classical RNSA, V-Detector, GF-RNSA and BIORV-NSA, whereas the detector detection rate increased by 50%, 25.8%, 13.8% and 10.5%, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Hierarchical division Specified candidate detector Hole repair

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Deterministically generated negative selection algorithm for damage detection in civil engineering systems

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ENGINEERING STRUCTURES 2019年 197卷 109444-000页

作者： Barontini, Alberto Perera, Ricardo Masciotta, Maria Giovanna Amado-Mendes, Paulo Ramos, Luis Lourenco, Paulo Univ Minho Dept Civil Engn ISISE Guimaraes Portugal Tech Univ Madrid Dept Mech Engn Madrid Spain Univ Coimbra Dept Civil Engn ISISE Coimbra Portugal

In the framework of Structural Health Monitoring, vibration-based methods are commonly used to assess the condition of a structural system, being the dynamic properties sensitive to damage-induced changes. Within this context, negative selection, a bin-inspired classification algorithm, can be exploited to distinguish anomalous from normal behaviours by comparing the monitored system features with a set of detectors appropriately trained to spot any possible anomaly inside the unitary feature space. Such method results particularly convenient due to its easy implementation, low computational cost and capability to carry out the classification based on a training set of data belonging only to a healthy-state condition. This circumstance is extremely common in real civil engineering applications where no knowledge might exist about different structural conditions over time. In this paper, a negative-selection algorithm with a non-random strategy for detector generation is developed and tested on a numerical case study, namely a model simulating the 1-40 Bridge over the Rio Grande in Albuquerque, New Mexico (USA). The work carried out proves that the algorithm is suitable for the purpose of damage detection (a binary classification problem) and, by introducing the anomaly score as a qualitative measure of the level of damage, provides a sound analysis of the method multiclass classification skills, aiming at the quantification of the damage.

关键词： Structural health monitoring Anomaly detection Damage identification Classification problems negative selection algorithm

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