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检索条件"主题词=Negative selection algorithm"

共 174 条记录，以下是51-60 订阅

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Neural networks-based negative selection algorithm with applications in fault diagnosis

Neural networks-based negative selection algorithm with appl...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Gao, XZ Ovaska, SJ Wang, X Chow, MY Aalto Univ Inst Intelligent Power Elect FIN-02150 Espoo Finland

ISBN: (纸本)0780385667

In this paper we first propose a novel neural networks-based negative selection algorithm (NSA). The principle and structure of our NSA are presented, and its training algorithm is derived Taking advantage of neural networks training, it has the distinguished capability of adaptation, which is well suited for dealing with practical problems under time-varying circumstances. A new fault diagnosis scheme using this NSA is next introduced, Two illustrative simulations of anomaly detection in chaotic time series and inner raceway fault diagnosis of 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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Integrated negative selection algorithm and Positive selection algorithm for Malware Detection 4

Integrated Negative Selection Algorithm and Positive Selecti...

引用

4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Zhang, Fuyong Ma, Ying Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781509034840

Run-time malware detection strategies are efficient and robust, which get more and more attention. In this paper, we use I/O Request Package (IRP) sequences for malware detection. N-gram will be used to analyze IRP sequences for feature extraction. Integrated negative selection algorithm (NSA) and Positive selection algorithm (PSA), through a selection of n-gram sequences which only exist in malware IRP sequences, we have more than 96% true positive rate and 0% false positive rate.

关键词： I/O Request Package artificial immune system negative selection algorithm positive selection algorithm malware detection

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Fault Detection in Robot Sensors Using negative selection algorithm 9

Fault Detection in Robot Sensors Using Negative Selection Al...

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9th IEEE International Conference on Computer Science and Education (ICCSE)

作者： Khan, M. Tahir Hussain, S. Bakhtair, S. Khan, A. Zeb Javed, S. Iqbal, J. Univ Engn & Technol Peshawar Inst Mechatron Engn Peshawar Pakistan Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9781479929511

This paper describes the methodology for fault detection in autonomous mobile robots. In this, artificial immune system based approach;a set of fault detectors is generated using negative selection algorithm which is responsible for detecting faulty behavior of system. The idea is based on the self-non self-discrimination observed in biological immune system. The feasibility of scheme is implemented on sensors of simulated mobile robot.

关键词： negative selection algorithm fault detection Artificial Immune System

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PTS-RNSA: A Novel Detector Generation algorithm for Real-Valued negative selection algorithm

PTS-RNSA: A Novel Detector Generation Algorithm for Real-Val...

引用

International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing

作者： Wang, Yujian Luo, Wenjian Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Dept Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9780769537399

A novel detector generation algorithm for Real-Valued negative selection algorithms, i.e. the PTS-RNSA, is proposed in this paper, which is based on the iterative Partition-Test-Spread process. Different from traditional detector generation algorithms that are randomized algorithms, the PTS-RNSA is a deterministic algorithm. When the number of the detectors is large enough, the PTS-RNSA can ensure to cover the whole non-self space except the boundary area between the self space and the non-self space. Experiments are done to compare the PTS-RNSA with the state-of-the-art algorithm, i.e. the V-detector algorithm. Experimental results demonstrate that the performance of the PTS-RNSA is very competitive. Especially, the time cost of the PTS-RNSA is much better than the V-detector algorithm.

关键词： artificial immune system negative selection algorithm detector generation algorithm

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A negative selection algorithm based on adaptive immunoregulation 5

A negative selection algorithm based on adaptive immunoregul...

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5th International Conference on Computational Intelligence and Applications (ICCIA)

作者： Deng, Hongli Yang, Tao China West Normal Univ Educ & Informat Technol Ctr Nanchong Peoples R China

ISBN: (纸本)9781728160429

negative selection algorithm (NSA) is an important detectors training algorithm in artificial immune system (AIS). In NSAs, the self radius and location of detectors affect the performance of algorithms. However, the traditional NSAs preset the self radius empirically and generate detectors randomly without considering the distribution of antigens resulting in the performance of AIS varies greatly in different applications. To deal with these limitations, an adaptive immunoregulation based real value negative selection algorithm (AINSA) is proposed in this paper. AINSA utilizes the "adaptive immunoregulation" mechanism to calculate the self radius and optimize the location of the candidate detectors. In this way, AINSA can attain the suitable self radius for different application and effectively generate the detectors in the region where antigens distribute densely. The experimental results show, on the artificial dataset and the UCI standard datasets, AINSA can reach the higher detection rate with better detectors generation efficiency compared to the classical RNSA and V-detector algorithm.

关键词： component Artificial immune negative selection algorithm evolution immunoregulation

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Smart Intrusion Detection Method using negative selection algorithm based on Maximum Entropy Model

Smart Intrusion Detection Method using Negative Selection Al...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Geng, Li-zhong Jia, Hui-bo Tsinghua Univ Dept Precis Instruments & Mechanol Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Precis Measurement Technol & Instru Beijing Peoples R China

ISBN: (纸本)9780769538167

Intrusion detection systems could rely on short sequences of system calls to distinguish between legitimate and illegitimate activities. We found that the frequencies of system calls in a particular process generally follow the Zipf's law. It means that there are many sequences which are meaningless to differentiate the ongoing behavior but generate lots of computing waste. Due to improve the performance of existing intrusion detection methods which are implemented in the kernel of operating system, this paper focuses on the negative selection algorithm using maximum entropy model to avoid the degeneration caused by the Valueless repetition of system calls. The improved scheme uses negative selection method to remove the useless computing which is predicted by maximum entropy model. Experimental results demonstrate that the computing cost has a reduction of 50 similar to 80% with the same Detection Rate

关键词： Intruison Deteciton System (IDS) negative selection algorithm Maximum Entruopy Model

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dnyNSA: A Novel Real-Value Based negative selection algorithm 8

dnyNSA: A Novel Real-Value Based Negative Selection Algorith...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Zhang Fan Yang Jin 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)9781538692769

Real-value based negative selection algorithm (NSA) is an important detector generation algorithm of Artificial Immune System. Traditional real-value based negative selection algorithm generates immune detectors randomly and does not consider the sample distribution, therefore too many candidate detectors overlap in the feature space resulting in excessive redundancy. This leads to a low efficiency of detector generation and decreases the performance of NSA. To aim at this problem, the paper proposes a novel real-value negative selection algorithm based on Delaunay triangulation (dnyNSA). Firstly, dnyNSA uses Delaunay triangulation method to analyze the topological structure of training dataset and produces a set of simplicial cells (triangle in 2-dimensional space and tetrahedron in 3-dimensional space, etc.). Then, based on the simplicial cell circum-circle(-sphere) property, dnyNSA generates detectors in more reasonable position and size. Therefore, it only needs a small number of detectors to cover the same feature space, avoiding the generation of redundant detectors in traditional random mode, which greatly improves the efficiency of NSA. Theoretical analysis showed the time complexity of dnyNSA is in logarithmic level. The experimental results showed that, compared with the RNSA and V-Detector, dnyNSA can raise the detector generation efficiency over ten times while maintain similar detection performance.

关键词： negative selection algorithm Artificial immune system Delaunay triangulation Detector generation

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An Outlier Robust negative selection algorithm

An Outlier Robust Negative Selection Algorithm

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2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

作者： Li, Guiyang Li, Tao Li, Haibo Zeng, Jie Sichuan Univ Sch Comp Sci Chengdu 610064 Peoples R China

ISBN: (纸本)9781424442461

Traditional negative selection algorithms do not perform any differentiation for training self dataset and only use the mechanism of negative selection. They will generate excessive invalid detectors and have poor detection performance when the training selves contain noisy data. In this paper, an outlier robust algorithm is proposed. The new algorithm will divide the training selves into internal selves, boundary selves and outlier selves. At the same time, the information hiding in different kind of selves is fully utilized. Furthermore, by combining negative selection mechanism with positive selection mechanism, the new algorithm can cover the non-self region more effectively. The experiment results show that no matter the training self data is clean or not, the new algorithm can obtain better detection performance by using fewer detectors.

关键词： negative selection algorithm Outlier self Boundary self Hypothesis testing ROC

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Detecting Denial of Service Attacks using a Combination of Dendritic Cell algorithm and the negative selection algorithm 2

Detecting Denial of Service Attacks using a Combination of D...

引用

2nd IEEE International Conference on Smart Cloud (SmartCloud)

作者： Igbe, Obinna Ajayi, Oluwaseyi Saadawi, Tarek CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781538636848

As one of the most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on computing systems and networks. This paper presents a system for detecting denial of service (DoS) attacks in a network using a combination of the dendritic cell algorithm (DCA) and the negative selection algorithm (NSA). The proposed system classifies incoming network traffic into either of two classes: "normal" or "DoS attack." Experimentally, our approach follows a majority voting technique by creating multiple instances of the DCA and the NSA algorithm and assigning weights to their respective output. The effectiveness of our proposed detection system is evaluated using an in-house generated signal dataset. Our results show that our system is very effective in detecting DoS/DDoS attacks with very high accuracy. Analysis of the proposed DoS detection system is also presented.

关键词： DoS Artificial Immune System negative selection algorithm Dendritic Cell algorithm IDS

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negative selection algorithm with Variable-Sized r-Contiguous Matching Rule

Negative Selection Algorithm with Variable-Sized r-Contiguou...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Yuebing Chen,Chao Feng,Quan Zhang,Chaojing Tang School of Electronic Science and Technology National University of Defense Technology Changsha Hunan 410073,China

We proposes a negative selection algorithm with variable-sized r-contiguous matching *** value of r can be used to balance between more generalization or more specification.A fixed r reaches a rough *** expect a variable-sized r to reach a refined *** are performed to test the *** to traditional negative selection algorithm and one-class SVM are conducted. The results demonstrate that the new scheme enhances the negative selection algorithm in efficiency for anomaly detection.

关键词： artificial immune system negative selection algorithm r-contiguous matching rule anomaly detection oneclass SVM

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