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检索条件"主题词=Negative Selection Algorithm"

共 174 条记录，以下是121-130 订阅

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Using IRP with a Novel Artificial Immune algorithm for Windows Malicious Executables Detection 4

Using IRP with a Novel Artificial Immune Algorithm for Windo...

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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 have already attracted extensive attention due to its effectiveness and robustness. With regards to Windows operating system, the API (Application Program Interface) is chosen to analyze program behavior. However, the API call sequences can be manipulated by a crafty attacker to circumvent detection. In order to handle this problem, we present a novel run-time malware detection strategy based on IRP (I/O Request Package) and develop a tool for capturing IRPs based on kernel driver technology. Some classic approaches are exploited to classify IRP sequences for malware detection, including Naive Bayes, Bayesian Networks, Support Vector Machine, C4.5 Decision Tree, Boosting, and negative selection algorithm. For fast run-time malware detection, we also present a novel artificial immune algorithm (NAIA) by means of short IRP sequences which exist only in malicious IRP sequences. Ultimately, for the whole dataset, boosted decision tree outperforms others with true positive rate 98.3%, and our NAIA outperforms other methods for the first 200 IRPs of each sequence.

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

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The dynamic immune forecasting method of the airdrome flight delay under considering the stochastic factors

The dynamic immune forecasting method of the airdrome flight...

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9th International Conference on Hybrid Intelligent Systems (HIS 2009)

作者： Ding, Jianli Li, Xuesen Tong, Guansheng Civil Aviat Univ China Coll Comp Sci & Technol Tianjin 300300 Peoples R China

ISBN: (纸本)9780769537450

There are many uncertain stochastic factors in the process of the airdrome flight delay forecast, especially the cumulate diversification and propagation effect of the flight delays. The paper is on the base of the immune detection for the airdrome flight delay status, considering the dynamic diversification and stochastic factors of the airdrome flight delays. Propose the dynamic immune forecasting method of the airdrome flight delays under the stochastic factors and establish corresponding collection of self, detecting cells, the information of antigens and the matching model between them through setting up correspondence between the biological immune mechanism and flight operating mechanism. The result shows the method can forecast the count of flight delay in the next period of time accurately and has a good real-time characteristic.

关键词： flight delays stochastic factors negative selection algorithm dynamic forecasting

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Artificial Immune System-based Music Piece Recommendation

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10th Joint Conference on Knowledge-Based Software Engineering (JCKBSE)

作者： Lampropoulos, Aristomenis S. Sotiropoulos, Dionysios N. Tsihrintzis, George A. Univ Piraeus Dept Informat Piraeus 18534 Greece

ISBN: (纸本)9781614990949;9781614990932

In this paper, we address the recommendation process as a one-class classification problem based on content features and a negative selection (NS) algorithm that captures user preferences. Specifically, we develop an Artificial Immune System (AIS) based on a negative selection algorithm that forms the core of a music recommendation system. A NS-based learning algorithm allows our system to build a classifier of all music pieces in a database and make personalized recommendations to users. This is achieved quite efficiently through the intrinsic property of the NS algorithm to discriminate "self-objects" (i.e. music pieces of user's like) from "non self-objects", especially when the class of non self-object is vast when compared to the class of self-objects and the examples (samples) of music pieces come only from the class of self-objects (music pieces of user's like). Our recommender system has been fully implemented and evaluated and found to outperform state of the art recommender systems based on support vector machines-based methodologies.

关键词： One class learning problem Artificial Immune System negative selection algorithm Support Vector Machines Music Recommender System

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Research on Immune Pathology in Artificial Immune System

Research on Immune Pathology in Artificial Immune System

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21st Chinese Control and Decision Conference

作者： Zhou, Xuanwu Engn Coll APF Key Lab Network & Informat Secur APF Xian 710086 Peoples R China

ISBN: (纸本)9781424427239

Artificial immune system (AIS) mimicks the superior properties of biological immune system and provides an effective method in intelligent computing and intelligent system designing. But the disease-causing mechanisms of immune pathology have led to sever security problems in artificial immune system. In the paper, we analyzed the basic principles of immune protection and immune pathology of biological system considering its application in artificial immune system. Then we take artificial immune defending system as an example to analyze the cause and potential influence of immune pathology on AIS. As to the different security problems from immunodeficiency, hypersensitivity and autoimmunity, we put forward corresponding measures to reinforce the security, robustness and stability of artificial immune system and thus effectively avoid these problems.

关键词： Immune pathology artificial immune system negative selection algorithm immunodeficiency system efficiency

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Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

Artificial Immune Systems Applied to the Analysis of Structu...

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4th International Conference on Mechanical Science and Technology (ICMSE 2014)

作者： dos Anjos Lima, Fernando Parra Chavarette, Fabio Roberto Frutuoso de Souza, Simone Silva dos Santos e Souza, Adriano Martins Lopes, Mara Lucia Univ Estadual Paulista UNESP FEIS Dept Mech Engn Av Brasil 56 BR-15385000 Ilha Solteira SP Brazil Univ Estadual Paulista Dept Math BR-15385000 Ilha Solteira SP Brazil

ISBN: (纸本)9783037859650

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

关键词： Analysis of Structural Integrity Building Artificial Immune Systems negative selection algorithm

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Dealing with Complexity for Immune-Inspired Anomaly Detection in Cyber Physical Systems 9th

Dealing with Complexity for Immune-Inspired Anomaly Detectio...

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9th International Conference on Secure Knowledge Management in Artificial Intelligence Era (SKM)

作者： Reuter, Lenhard Leitner, Maria Smith, Paul Koschuch, Manuel Univ Appl Sci FH Campus Wien Vienna Austria Univ Vienna Fac Comp Sci Vienna Austria AIT Austrian Inst Technol GmbH Ctr Digital Safety & Secur Seibersdorf Austria

ISBN: (纸本)9783030975326;9783030975319

With digitization, critical infrastructures face a higher risk of security incidents and attacks on cyber-physical systems (CPS). In the past 50 years, research and practice have developed various approaches to monitor and detect attacks such as with anomaly detection. While many approaches focuses on artificial neural networks, bio-inspired approaches utilize nature as reference. For example, artificial immune systems (AIS) refer to principles of the natural immune system. In this paper, we investigate the negative selection algorithm (NSA), an algorithm from the domain of AIS for anomaly detection in CPS. Particularly in CPS, datasets can become quite complex and can require a number of detectors for the analysis. Therefore, we will investigate how AIS can be extended to handle and manage complex datasets of CPS. We propose two models that use Principal Component Analysis (PCA) and Autoencoder (AE) to enable dimensionality reduction. Using these models, we are able to show that it is possible to apply the NSA approach to such datasets. Our results indicate that the use of PCA and AE is beneficial for both a better representation of the data and therefore significantly relevant for an improvement of the detection rate, and provides in addition the possibility to add further features to support the identification of anomalies. As the NSA approach allows for distributed computation, it might be possible to allow faster or distributed detection;the extent to which this is possible remains to be investigated and therefore represents future work.

关键词： Anomaly detection negative selection algorithm Cyber physical system Principal component analysis Autoencoder

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A Framework of Intrusion Detection Inspired by Artificial Immune System 1st

A Framework of Intrusion Detection Inspired by Artificial Im...

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1st International Conference on Emerging Networking Architecture and Technologies (ICENAT)

作者： Zhang, Haodi Huang, Zeyuan Wang, Shuai Jin, Huamin Deng, Xiaodong China Telecom Corp Ltd Guangdong Res Inst Guangzhou Guangdong Peoples R China

ISBN: (纸本)9789811996962;9789811996979

The Human Immune System categorizes substances into self and non-self to find intrusions. This idea provides a good model for intrusion detection systems in the computer science field. This model has developed into a research field called Artificial Immune Systems. Such a system uses several algorithms and concepts like negative selection, a classical algorithm in this field. This paper takes an overview of the strengths and weaknesses of negative selection. It proposes the design of Artificial Immune System(AIS) based Intrusion Detection Systems (IDSs), which consists of three layers to attain accommodative immune response and an improved algorithm to decrease false positives and false negatives. Experiments on the same dataset prove the validity of our proposal and some specific indicator values can achieve the performance of the classical Artificial Intelligence(AI) algorithm.

关键词： Artificial Immune System negative selection algorithm Intrusion Detection

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An artificial immune method for stock market avoiding control of hedge fund

An artificial immune method for stock market avoiding contro...

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3rd International Conference on Natural Computation (ICNC 2007)

作者： Wu, Ze-Jun Wang, Xu Liang, Yi-Wen Wuhan Univ Sch Comp Luojia Mt Wuhan 430072 Peoples R China Zhongnan Univ Econ & Law Coll Informat Wuhan 430072 Peoples R China

ISBN: (纸本)0769528759

With the increasing opening of Chinese Financial Market to the world, the international venture capital, especially the hedge fund, will make big profits through various operational means in this faulty environment. To ensure the stability of stock market, a newly method should be urgently proposed to participate in detecting abnormity of stock market. This paper introduces an artificial immune method establishes the immune detecting system framework by mining the features of single stock's abnormal fluctuation and matching with macroeconomic indexes, uses negative selection algorithm recognizing "Self" and "Non-self" at the same time, while builds up detectors with alarm mechanism, providing a new idea for the management of stock risk.

关键词： hedge fund stock market immune defense negative selection algorithm

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A Constructional algorithm of Principal and Subordinate Structure Self-set Based on Decision Tree

A Constructional Algorithm of Principal and Subordinate Stru...

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Fu, Haidong Xie, Qi Wuhan Univ Sci & Technol Dept Comp Sci Wuhan 430081 Hubei Peoples R China

ISBN: (纸本)9781424421077

Aiming at solving the problem of "self-set incomplete" that exists in intrusion detection system based on computer immune, this paper designs a construction algorithm of principal and subordinate structure self-set based on decision trees. The decision trees are introduced to traditional negative-selection algorithm and the candidate detectors which have been eliminated by the immune tolerance are reclassified by the decision trees, and the candidate detectors that meet the setting conditions compose the "subordinate self-set" so as to achieve the dynamic expansion of the self-set. The unqualified elements in "subordinate self-set" are eliminated according to the "conflict of matching" method. Experimental results show that this algorithm is effective and can improve the recognition performance of the detectors.

关键词： artificial immune system intrusion detection negative selection algorithm decision trees self-set

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Artificial Immune system based approach to fault diagnosis for wireless sensor networks

Artificial Immune system based approach to fault diagnosis f...

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2nd International Conference on Smart Materials and Nanotechnology in Engineering

作者： Chen, Yongjun Yuan, Shenfang Wang, Yang Wu, Jian Nanjing Univ Aeronaut & Astronaut Aeronaut Key Lab Smart Mat & Struct Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9780819478047

Fault diagnosis has been recognized as one of the key issues in wireless sensor networks. Considering distribution feature of sensor node, however, the fault happened in wireless sensor networks is usually random and unpredictable. The conventional diagnosis approaches become increasingly difficult to deal with. As a result, the application is limited seriously. To solve the problem, a new approach based on artificial immune system for fault diagnosis is proposed. The normal and abnormal character patterns generated by a network simulator for wireless sensor networks, respectively, are regarded as the self and antigen of artificial immune system. According to a real-valued negative selection algorithm, the detectors are generated to improve the covering ability of non- self space. Taking detector as antibody, an immunity calculation is executed by the distribution zones of antibody and evolution learning mechanism of artificial immune system. The type of antigen is decided based on the clustering distribution of cloned and matured antibody. The example shows that the approach has better accuracy and the capability of self-adaptive for the fault diagnosis in wireless sensor networks.

关键词： artificial immune system wireless sensor networks negative selection algorithm fault diagnosis OPNET

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