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Self-adaptive mobile web service discovery framework for Dynamic Mobile Environment

JOURNAL OF SYSTEMS AND SOFTWARE 2022年 184卷 111120-111120页

作者： Garba, Salisu Mohamad, Radziah Saadon, Nor Azizah Univ Teknol Malaysia Fac Engn Sch Comp Skudai 81310 Johor Malaysia

This paper proposes a self-adaptive mobile web service (MWS) discovery framework for a dynamic mobile environment (DME) to deal with MWS proliferation, dynamic context, and irrelevant MWS discovery challenges. The main contribution of this research includes an improvement of the matchmaking algorithm, enhanced MWS categorization approach, and extensible meta-context ontology that represents the context information in DME. This was achieved by enabling the self-adaptive matchmaker to learn MWS relevance using a Modified-negative selection algorithm (M-NSA) and retrieve the most relevant MWS based on the current context of the discovery. To assess the proposed framework, series of experiments was carried out using publicly-available datasets. The performance of the framework is evaluated against the state-of-the-art frameworks. It was found that the proposed framework is more effective and attained better binary and graded relevance when subjected to context variations which are prevalent in DME. This is useful for service-based application designers and other MWS clients. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Self-adaptive Mobile web service Service discovery negative selection algorithm Dynamic Mobile Environment

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Improving damage detection by combining multiple classifiers in different feature spaces

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ENGINEERING STRUCTURES 2024年 299卷

作者： Barontini, Alberto Masciotta, Maria Giovanna Amado-Mendes, Paulo Ramos, Luis F. Lourenco, Paulo B. Univ Minho ISISE ARISE Dept Civil Engn Guimaraes Portugal Univ G Annunzio Chieti Pescara Dept Engn & Geol Viale Pindaro 42 I-65127 Pescara Italy Univ G Annunzio Chieti Pescara Res Ctr UdA TechLab I-66100 Chieti Italy Univ Coimbra ISISE ARISE Dept Civil Engn Coimbra Portugal

Vibration-based damage identification methods have been explored in several engineering fields. However, the effective damage-sensitivity of vibration-based features and the balance between false alarms and readiness of the anomaly detection are still open issues. Here, a cost-effective damage detection methodology based on a Deterministically Generated negative selection algorithm is implemented and tested over a wide dataset generated through the numerical simulation of a three-storey concrete building in different damage scenarios. The approach allows training the classifiers only based on undamaged samples and ensures a prompt response to damage outbreak by fusing the classification conducted in parallel on different feature spaces, exploiting their distinct sensitivity to damage. A strategy to improve the reliability of the alarm is implemented by introducing counters and thresholds on consecutive classification outputs. A satisfactory trade-off between false and true alarms is achieved by comparing their probabilities, obtained through Markov chains, for different values of such thresholds.

关键词： Structural health monitoring Damage detection negative selection algorithm Damage sensitivity Multiple classifiers combination

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DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2017年第11期11卷 5574-5591页

作者： Guo, Weipeng Chen, Yonghong Cai, Yiqiao Wang, Tian Tian, Hui Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China Univ Cincinnati Dept Elect Engn & Comp Syst Cincinnati OH 45221 USA

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based negative selection algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

关键词： WSN Intrusion detection negative selection algorithm Multiobjective optimization problem Differential Evolution

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Towards a Hybrid Immune algorithm Based on Danger Theory for Database Security

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IEEE ACCESS 2020年 8卷 145332-145362页

作者： Said, Wael Mostafa, Ayman Mohamed Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Taibah Univ Coll Comp Sci & Engn Medina 42353 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Sakaka 72314 Saudi Arabia

In Databases, the most prevalent cause of data breaches comes from insiders who misuse their account privileges. Due to the difficulty of discovering such breaches, an adaptive, accurate, and proactive database security strategy is required. Intrusion detection systems are utilized to detect, as fast as possible, user's account privilege misuse when a prevention mechanism has failed to address such breaches. In order to address the foremost deficiencies of intrusion detection systems, artificial immune systems are used to tackle these defects. The dynamic and more complex nature of cybersecurity, as well as the high false positive rate and high false negative percentage in current intrusion detection systems, are examples of such deficiency. In this paper, we propose an adaptable efficient database intrusion detection algorithm based on a combination of the Danger Theory model and the negative selection algorithm from artificial immune system mechanisms. Experimental results for the implementation of the proposed algorithm provide a self-learning mechanism for achieving high detection coverage with a low false positive rate by using the signature of previously detected intrusions as detectors for the future detection process. The proposed algorithm can enhance detecting insider threats and eliminate data breaches by protecting confidentiality, ensuring integrity, and maintaining availability. To give an integrated picture, a comprehensive and informative survey for the different research directions that are related to the proposed algorithm is performed.

关键词： Immune system Intrusion detection Databases Artificial intelligence Heuristic algorithms Clustering algorithms Danger theory model artificial immune system negative selection algorithm database intrusion detection system

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Combine labeled and unlabeled data for immune detector training with label propagation

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107661-107661页

作者： Chen Wen Wang Changzhi Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China Zhihuiyuntian Inc Artificial Intelligence Ctr Chengdu Peoples R China

Artificial immune detectors are the basic recognition components of immune systems. Traditionally, the candidate non-self detectors are compared with the whole self training set to eliminate self reactive ones in negative selection algorithms (NSAs). However, the training process has low efficiency due to the exhausting comparisons. Furthermore, it can be more efficient if we straightforwardly generate self-detectors based on the available self samples to avoid the overwhelmed comparisons. In the paper, a new detector training algorithm is proposed. Firstly, the self training set is enlarged by the label propagation algorithm (LPA) using both labeled and unlabeled samples;and then the newly labeled samples is evaluated based on noisy learning theory to remove the unqualified ones. Finally self-detectors are directly generated at the locations of self samples. The theoretical analysis demonstrated that the time complexity of our algorithm is much reduced, especially that the exponential relationship between self size and time complexity in traditional NSAs is eliminated. The experimental results showed that: not only the time cost of detector training, but also the detection accuracy is improved. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune systems negative selection algorithm Positive selection algorithm Noisy learning Label propagation algorithm

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A novel sophisticated hybrid method for intrusion detection using the artificial immune system

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 58卷

作者： Farzadnia, Ehsan Shirazi, Hossein Nowroozi, Alireza Malek Ashtar Univ Technol Fac Elect & Comp Engn Tehran Iran IRIB Univ Media Engn Dept Tehran Iran

Artificial immune system (AIS) as a good prototype for developing Machine Learning (ML) is a promising candidate to design Intrusion Detection System (IDS). Two of its prevalent paradigms, the negative selection, and the danger theory, inspired by immunity responses of the powerful human immune system (HIS) are being widely used in this case. In this paper, we proposed a novel sophisticated hybrid method including two defensive lines by using two aforesaid mechanisms. In the proposed method, decentralized cooperation of dendritic cells together with mature detectors acts as a stimulator to generate efficient and accurate detectors and retain memory in the long-term that meant security control via ensuring immunity. Simulating such a system was applicable only by establishing artificial life cycles in the MATLAB environment. Experimental consequences demonstrate that the present schema gradually ameliorates its overall performance during the incipient stages of life cycles and also it is more capable than the previous ones found in the literature.

关键词： Evolutionary computing Intrusion detection Artificial life cycle negative selection algorithm Dendritic cell Polar coordinates

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Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

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SENSORS 2021年第21期21卷 7155-7155页

作者： Barontini, Alberto Masciotta, Maria Giovanna Amado-Mendes, Paulo Ramos, Luis F. Lourenco, Paulo B. Univ Minho Dept Civil Engn ISISE P-4800058 Guimaraes Portugal Univ G dAnnunzio Dept Engn & Geol I-65127 Pescara Italy Univ Coimbra Dept Civil Engn ISISE P-3030788 Coimbra Portugal

Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of 'Santa Maria de Belem' in Lisbon.

关键词： structural health monitoring damage detection historical buildings negative selection algorithm

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Research on prediction of coal and gas outburst risk based on TIWTD-CNSA

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RESULTS IN ENGINEERING 2023年 18卷

作者： Peng, Ji Shiliang, Shi Xingyu, Shi Hunan Univ Sci & Technol Coll Resource Environm & Safety Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Prov Key Lab Safe Min Tech Coal Mines Taoyuan Rd Xiangtan 411201 Hunan Peoples R China Changsha Univ Sci & Technol Wanjiali Rd Changsha 410114 Hunan Peoples R China

When processing the current coal and gas outburst risk index data, it is observed that the data sequence does not have intuitive regularity, and the acquired data signals are generally mixed with different degrees of noise, which hinders data processing and prediction. To reduce the influence of noise signal on prediction, it is proposed to use Translation-invariant Wavelet Threshold Denoising (TIWTD) to process signal data, and the result shows that the noise signal in the original data is significantly reduced. On this basis, a Compact negative selection algorithm (CNSA) is introduced to detect abnormal signals exceeding the critical value in the data and make predictions. After training the model with the gas hazard index feature subset collected during the excavation process of No. 11192 mining face in a coal mine from Guizhou province in China, input the processed indicator data into the model for prediction. The results show that the model can effectively identify the anomalies in the data and accurately predict the risk of coal and gas outburst, the TIWTD-CNSA model can be used to guide engineering practice.

关键词： Coal and gas outburst Risk prediction Translation-invariant wavelet threshold denoising negative selection algorithm

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Artificial Immune-based algorithm for Academic Leadership Assessment

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第8期10卷 364-370页

作者： Jantan, Hamidah Azemi, Nur Hamizah Syafiqah Che Othman, Zulaiha Ali Univ Teknol MARA UiTM Terengganu Fac Comp & Math Sci Kuala Terengganu Terengganu Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi Malaysia

Artificial Immune-based algorithm is inspired by the biological immune system as computational intelligence approach in data analysis. negative selection algorithm is derived from immune-based algorithm's family that used to recognize the pattern's changes perform by the gene detectors in complementary state. Due to the self-recognition ability, this algorithm is widely used to recognize the abnormal data or non-self especially for fault diagnosis, pattern recognition, network security etc. In this study, the self-recognition performance proposed by the negative selection algorithm been considered as a potential technique in classifying employee's competency. Assessing the employee's performance in organization is an important task for human resource management people to identify the right candidate in job promotion assessment. Thus, this study attempts to propose an immune-based model in assessing academic leadership performance. There are three phases involved in experimental phase i.e. data acquisition and preparation;model development;and analysis and evaluation. The data consists of academic leadership proficiency was prepared as data-set for learning and detection processes. Several experiments were conducted using cross validation process on different model to identify the most accurate model. Therefore, the accuracy of NS classifier is considered acceptable enough for this academic leadership assessment case study. For enhancement, other immune-based algorithm or bio-inspired algorithms, such as genetic algorithm, particle swam optimization, ant colony optimization would also be considered as a potential algorithm for performance assessment.

关键词： Immune-based algorithm negative selection algorithm academic leadership performance assessment

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Fast Mutation Operator Applied in Detector Generating Strategy

Fast Mutation Operator Applied in Detector Generating Strate...

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The 9th International Conference for Young Computer Scientists(第九届国际青年计算机大会)

作者： Xingbao Liu Zixing Cai Chixin Xiao School of Information Science and Engineering Central South University Changsha 410083 China Cent School of Information Science and Engineering Central South University Changsha 410083 China

Inspired by the stimulated-responding mutation process of gene segment, a new mutation operator is proposed. The main characteristic is its mutation method, when one bit is mutated then the following contiguous bits are to be mutated according to the mutated, which make it owns better capability of local search. At the same time a new algorithm, named Contiguous somatic stimulated mutation algorithm (CSSMA), is given based on the novel mutation operator. Experiments showed, the CSSMA outperforms than some classical algorithms in term of the detective rate and time cost.

关键词： artificial immune systems negative selection algorithm detection B-cell algorithm contiguous somatic stimulated mutation.

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