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Multisource Neighborhood Immune Detector Adaptive Model for Anomaly Detection

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第3期25卷 582-594页

作者： Xi, Liang Wang, Rui-Dong Yao, Zhi-Yu Zhang, Feng-Bin Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150080 Peoples R China MediaTek Inc TV Business Unit Shenzhen 518051 Peoples R China

The artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields. The detector set is the core knowledge set, and the AIS application effects are mainly determined by the generation, evolution, and detection of the detectors. Presently, the problem space (shape-space) of AIS mainly applied real-valued representation. But the real-valued detectors have some problems that have not been solved well, such as slow convergence speed of generation, holes in the nonself region, detector overlapping redundancy, dimension curse, etc., which lead to the unsatisfactory detection effects. Moreover, artificial immune anomaly detection is a dynamic adaptive model, needs to be evolved adaptively with the detection environments. Without better adaptive modeling, these problems mentioned before will get worse. In view of this, this article proposes a multisource immune detector adaptive model in neighborhood shape-space and applies it to anomaly detection: based on random, chaotic map and DNA genetic algorithm (DNA-GA), multisource neighborhood negative selection algorithm (MSNNSA), multisource neighborhood immune detector generation algorithm (MS-NIDGA), and neighborhood immune anomaly detection algorithm (NIADA) are proposed, so that the generation and detection of immune detectors can be improved efficiently;introducing immune adaptive and feedback mechanism, multisource neighborhood immune detector adaptive model (MS-NIDAM) is built, so that the detectors can be adaptively evolved in a more targeted search domain, and keep better distribution to the nonself region in real time, so as to solve various problems existing in the real-valued shape-space under dynamic environment mentioned before and improve the overall detection performances. The experimental results show that MS-NIDAM can improve the detector generation/evolution efficiency, keep the up-to-date understanding of the changing environment, so

关键词： Detectors Immune system Artificial intelligence Adaptation models Vaccines DNA Anomaly detection Adaptive evolution anomaly detection artificial immune system (AIS) negative selection algorithm neighborhood shape-space

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Abnormal Detection of Pumping Unit Bearing Based on Extension Theory

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2021年第12期16卷 1647-1652页

作者： Zhang, Zhiming Sch Northeast Petr Univ High Tech Ind Dev Zone 99 Xuefu St Daqing City Heilongjiang Peoples R China

In order to solve the problem that is difficult to extract the fault data of bearing of pumping unit in operation, a negative selection algorithm based on extension theory is proposed to detect the abnormal of bearing. First, a negative selection algorithm detector set model is constructed by using matter element. Synthetic correlation degree function is used as the matching rule of detector generation stage and bearing fault anomaly detection stage. Genetic Particle Swarm Optimization (GPSO) is used to generate detector set. Aiming at the problem of large redundancy of the detector set, the correlation function is used to formulate merging rules to merge the detector set interval. Finally, bearing anomaly detection is carried out by using the bearing fault data of Case Western Reserve University. The results show that the activation rate of the inner ring fault detector is 98.89%, the activation rate of the outer ring fault detector is 98.61%, and the activation rate of the ball fault detector is 99.17%. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： extension theory negative selection algorithm genetic particle swarm optimization abnormal detection

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Detection of anomalies in compiled computer program files inspired by immune mechanisms using a template method

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JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2021年第1期17卷 47-59页

作者： Wawryn, Krzysztof Widulinski, Patryk Koszalin Univ Technol Dept Elect & Comp Sci Ul Sniadeckich 2 PL-75453 Koszalin Poland

An intrusion detection system inspired by the human immune system is described: a custom artificial immune system that monitors a local area containing critical files in the operating system. The proposed mechanism scans the files and checks for possible malware-induced alterations in them, based on a negative selection algorithm. The system consists of two modules: a receptor generation unit, which generates receptors using an original method based on templates, and an anomaly detection unit. Anomalies detected in the files using previously generated receptors are reported to the user. The system has been implemented and experiments have been conducted to compare the effectiveness of the algorithms with that of a different receptor generation method, called the random receptor generation method. In a controlled testing environment, anomalies in the form of altered program code bytes were injected into the monitored programs. Real-world tests of this system have been performed regarding its performance and scalability. Experimental results are presented, evaluated in a comparative analysis, and some conclusions are drawn.

关键词： Artificial immune system Receptor Anomaly negative selection algorithm Template Intrusion detection system Malware Virus

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A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2021年第1期15卷 1-25页

作者： Dung Hoang Le Nguyen Thanh Vu Tuan Dinh Le Vietnam Natl Univ Univ Informat Technol Hanoi Vietnam Ho Chi Minh City Univ Food Ind Ho Chi Minh City Vietnam Long An Univ Econ & Ind Tan An City Long An Vietnam

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system, in which an artificial immune network is used as a pool to create and develop virus detectors that can detect unknown data. Besides, a deep learning model is also used as the main classifier because of its advantages in binary classification problems. This method can achieve a detection rate of 99.08% on average, with a very low false positive rate.

关键词： Artificial Immune Network Artificial Immune System Clonal selection algorithm Deep Belief Network negative selection algorithm Portable Executable

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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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A Study of Detection Probabilities and Real-World Testing of a Human Immunity Inspired Intrusion Detection System 28

A Study of Detection Probabilities and Real-World Testing of...

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28th International Conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Widulinski, Patryk Wawryn, Krzysztof Koszalin Univ Technol Fac Elect & Comp Sci Koszalin Poland

ISBN: (纸本)9788363578190

In the paper, a study of detection probabilities and experimental tests of an intrusion detection system (IDS) utilizing the artificial immune system (MS) based on the negative selection approach are presented. The algorithm uses binary strings called receptors to detect intrusions in programs in the operating system. In the work, a statistical approach is used to calculate the probabilities of detecting the intrusions and the results are compared with the experimental tests. New experiments are conducted using a virtual machine as the environment to check the effectiveness of the proposed IDS. For this purpose, real-world malware samples arc launched in the virtual machine. The research of the IDS is then presented, analyzed and concluded.

关键词： virus malware intrusion detection system artificial immune system negative selection algorithm statistics virtual machine templates

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

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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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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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Prognosis and fail detection in a dynamic rotor using artificial immunological system

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ENGINEERING COMPUTATIONS 2020年第9期37卷 3127-3145页

作者： Outa, Roberto Chavarette, Fabio Roberto Mishra, Vishnu Narayan Goncalves, Aparecido C. Roefero, Luiz G. P. Moro, Thiago C. FATEC Aracatuba Fac Technol Aracatuba Dept Biofuels Aracatuba Brazil Univ Estadual Paulista Fac Engn Ilha Solteira Dept Math Ilha Solteira Brazil Indira Gandhi Natl Tribal Univ Dept Math Amarkantak India Univ Estadual Paulista Fac Engn Ilha Solteira Dept Mech Engn Ilha Solteira Brazil

Purpose In recent years, the mechanical industries began to apply many investments in research and technological development to obtain efficient methods to analyze the integrity of structures and prevent disasters and/or accidents, ensuring people's lives and preventing economic losses. Any structure, whether mechanical or aeronautical, before being put into use undergoes a structural integrity assessment and testing. In this case, non-destructive evaluations are performed, aiming to estimate the degree of safety and reliability of the structure. For this, there are techniques traditionally used such as ultrasonic inspection, X-ray, acoustic emission tests, among other techniques. The traditional techniques may even have a good instrumental apparatus and be well formulated for structural integrity assessment;however, these techniques cannot meet growing industrial needs, even more so when structures are in motion. The purpose of this paper is to demonstrate artificial immune systems (AISs), ate and strengthen the emergence of an innovative technological tool, the biological immune systems and AISs, and these are presented as computing methods in the field of structural health monitoring (SHM). Design/methodology/approach The concept of SHM is based on a fault detection mechanism used in industries, and in other applications, involving the observation of a structure or a mechanical system. This observation occurs through the dynamic response of periodic measurements, later related to the statistical analysis, determining the integrity of the system. This study aims to develop a methodology that identifies and classifies a signal in normal signals or in faults, using an algorithm based on artificial immunological systems, being the negative selection algorithm, and later, this algorithm classifies the failures in probabilities of failure and degree of fault severity. The results demonstrate that the proposed SHM is efficient and robust for prognosis and failure detect

关键词： Structural health monitoring Artificial immune systems negative selection algorithm 68W40 42C40

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An immune -based risk assessment method for digital virtual assets

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COMPUTERS & SECURITY 2021年 102卷 102134-102134页

作者： He, Junjiang Li, Tao Li, Beibei Lan, Xiaolong Li, Zhiyong Wang, Yunpeng Sichuan Univ Coll Cybersecur 24 South Sect 1Yihuan Rd Chengdu Peoples R China

Digital virtual assets are playing an increasingly important role and have become an indispensable part of people's lives. However, due to the characteristics of network, virtuality, and openness, digital virtual assets are extremely vulnerable to attack. Meanwhile, it is difficult to guarantee the security of digital virtual assets based on advanced encryption technology and game theory-based asset security recognition and protection methods. Therefore, how to identify the possible attacks of digital virtual assets and timely accurate assessment the risks are the urgent problems to be solved. In this paper, we design an immune-based risk assessment method for digital virtual assets by simulating the mechanism of the human immune system "synchronous dynamic evolution of antibody concentration with invasion virus". First, we propose a negative selection algorithm based on the "Designated + Random" mode by hierarchical division (HD-NSA), which can efficiently generate high-performance immune detectors to identify attack risks. Then a threat risk assessment model for digital virtual assets by simulating the concentration change of antibody in the immune system is established to assess the risk of attacks. Experiments on bitcoin dusting attack show that, the method proposed in this paper can detect attacks more quickly and accurately, and can also assess the risk of different users being attacked in real time. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Digital virtual assets Artificial immune system negative selection algorithm Antibody concentration Risk assessment

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