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

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

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Fuyong Zhang Ying Ma School of Computer Science and Network Security Dongguan University of Technology School of Computer and Information Engineering Xiamen University of Technology

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 Na?ve 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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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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The 4th International Conference on Wireless Communications, Networking and Mobile Computing(第四届IEEE无线通信、网络技术及移动计算国际会议)

作者： Haidong Fu Qi Xie Department of Computer Science Wuhan University of Science & Technology Wuhan Hubei China 430081

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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Application of an Information Fusion Method to Compound Fault Diagnosis of Rotating Machinery

Application of an Information Fusion Method to Compound Faul...

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第27届中国控制与决策会议

作者： Qin Hu Aisong Qin Qinghua Zhang Guoxi Sun Longqiu Shao Guangdong provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis Guangdong University of Petrochemical Technology

ISBN: (纸本)9781479970186

Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis,an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating *** multiple fault features were extracted by the genetic *** fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the *** through the negative selection algorithm,the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was *** the fusion result was obtained by utilizing the weighted evidence theory into the decision-making information fusion for the preliminary *** comparing the diagnosis results with other artificial intelligence algorithm,experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.

关键词： Genetic programming negative selection algorithm Weighted evidence theory Fusion decision

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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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2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Jianli Ding Xuesen Li Guansheng Tong College of Computer Science and Technology Civil Aviation University of China Tianjin 300300 China Information Technology Research Base Civil Aviation Administration of China Tianjin 300300 China

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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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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Significant Level in V-detector Generation algorithm

Significant Level in V-detector Generation Algorithm

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2009 Asia-Pacific Conference on Information Processing

作者： Guiyang Li, Tao Li, Jie Zeng, Haibo Li School of Computer Science Sichuan University Chengdu, China

The V-detector generation algorithm is a kind of negative selection algorithm (NSA) inspired by biological immune system (BIS). In this paper, V-detector generation algorithm is simply introduced. The problem that V-detector generation algorithm can not meet the change of parameters in significant level is also pointed out. This problem will cause instable algorithm performance. In the meantime, it will cause the algorithm failed under the circumstance of some parameter values. This paper analyzed the reason that caused the problem theoretically and the relevant solution is proposed. The experiment results showed that the proposed solution solved the problem existed in original algorithm successfully and make the performance more stable and reliable. The improved algorithm can really adapt to different values of significant level and obtain better detection performance.

关键词： artificial immune system negative selection algorithm V-detector hypothesis testing significant level

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A Detector Generating algorithm Based on Interval Partition

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2009年第1期5卷 27-27页

作者： Xia, Huifen Ju, Shiguang Cai, Tao Jiangsu Univ Dept Comp Sci Zhenjiang 212031 Peoples R China

Based on the functions and some relevant theories of the biological immune system, an artificial immune system is established to solve the practical problems for computing systems. At present, the artificial immune system includes two major categories: the mechanism of non-self recognition and immune network, the most important of which is negative selection algorithm. The negative selection algorithm is proposed to simulate the formation and running mechanism of T cells for the immune system in 1994. In this algorithm, one of the key steps is the detector generation. Unfortunately, the current detector generating algorithms have detector generation inefficiencies, holes area, and redundant detector problems to some degree. In this paper, from the perspective of one dimension, a novel detector generating algorithm that is based on interval partition is proposed. At the beginning of this algorithm, we make the maximal interval be the initial detector;second, this detector should experience the training of self-tolerance. According to the matching rule, we let this detector match the given collection of selves;then we remove the points from the interval detector which matches the selves. At the same time, we divide the interval into two parts at this point and have the candidate detectors optimized by the corresponding interval collations and amalgamations. That is to say, the initial detector interval is divided recursively according to the spatial locations of selves. At last, we can get a set of excellent mature detectors, which can be used to protect the system security. To illustrate the advantage of this algorithm, we have given an example. From this example, we can declare that the algorithm improves the current detector generations and matching rules greatly. It also helps to remove the holes area and redundant detectors. Therefore, both the detector generation efficiency and the detecting efficiency are well improved. By the theoretical analysis and compariso

关键词： negative selection algorithm Detector generating algorithm Matching rule

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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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Research on Intrusion Detection System Using Improved Artificial Immune algorithm

Research on Intrusion Detection System Using Improved Artifi...

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Xiang Feng Tianling Zhao Department of Computer Science East China University of Science and Technology

ISBN: (纸本)9781612848334

Computer Security System and Biological Immune System have much comparability, so the Artificial Immune algorithm is applied in Intrusion Detection System to solve various problems in the field of computer security. This paper presents the fundamental and method of Intrusion Detection System based on Artificial Immune algorithm, analyzes and compares the parameters of the Artificial Immune algorithm. After studying the classical algorithm which is called negative selection algorithm, we discovered the match algorithm had caused the examination black hole. This paper proposes an improved algorithm based on the traditional algorithm, and compares the performance and efficiency to the traditional algorithm, then testifies the effectivity and feasibility of the improved algorithm.

关键词： Artificial Immune Intrusion Detection negative selection algorithm

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