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A real-time anomaly-based IDS for cyber-attack detection at the industrial process level of Critical Infrastructures

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION 2018年 23卷 11-20页

作者： Clotet, Xavier Moyano, Jose Leon, Gladys Aplicac Informat Avanzada SL Sant Cugat Del Valles Spain

This work presents a real time anomaly-based detection system designed to work at the industrial process level of Critical Infrastructures (CI). The system's core algorithm is based on negative selection and works in two phases: it first learns from the normal behaviour of the process, and then performs detection and raises alarms each time an abnormal behaviour is found. The main goal of the proposed tool is the detection of attacks targeting the physical components or devices composing the industrial process level of CI such as electric, gas or water utilities. The proposed IDS uses a multi-agent approach to tackle the complex problem of monitoring large amounts of data coming from measurements recorded by Industrial Control Systems. It was built on an open source distributed computation system for real time analysis. This tool was developed, tested, and validated during the EU-funded project PREEMPTIVE. Detection results obtained on a water treatment plant laboratory are presented and discussed. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Critical Infrastructure Protection Cyber security negative selection algorithm Intrusion detection system Anomaly detection Industrial process level of critical infrastructures

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Damage diagnosis in an isotropic structure using an artificial immune system algorithm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2019年第11期41卷 1-11页

作者： Oliveira, Daniela C. Chavarette, Fabio R. Lopes, Mara L. M. UNESP Univ Estadual Paulista Fac Engn Ilha Solteira Dept Mech Engn Sao Paulo Brazil UNESP Univ Estadual Paulista Fac Engn Ilha Solteira Dept Math Brasil Ave 56 BR-15385000 Ilha Solteira SP Brazil

This work proposes a recent methodology for developing structural health monitoring based on intelligent computing techniques, with the purpose of detecting structural damages in aircrafts using artificial immune systems with negative selection. To assess this methodology, an experimental setup was built with piezoelectric transducers attached to an aluminum plate (which represents a wing of an airplane), which work both as actuators and sensors, where signals were acquired in normal and damage situations. The results show robustness and accuracy for the new methodology proposed.

关键词： Structural health monitoring Artificial immune systems negative selection algorithm

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NKA: a pathogen dose-based natural killer cell algorithm and its application to classification

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JOURNAL OF SUPERCOMPUTING 2022年第5期78卷 7016-7037页

作者： Wang, Dongmei Liang, Yiwen Yang, Xinmin Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China

Inspired by the self/non-self discrimination theory, Forrest et al. proposed a distance-based negative selection algorithm (NSA). However, in the detector generation phase, NSA creates specific antibodies for each antigen based on the mechanism of specific antigen-antibody binding reactions, which lead to an excessive and redundant number of detectors. Moreover, during the detection phase, NSA needs to calculate the matching degree of the antigen with all the detectors, which has a high computational cost and low detection efficiency. This letter presents a novel natural killer cell algorithm (NKA) inspired by the mechanism of NK cells constructing phenotype detectors based on pathogen dose, which is a non-specific immune mechanism. NKA defines dose and phenotype detector, optimizes the detectors based on the memory evolution mechanism of phenotype, then establishes k-d tree for the optimized phenotype, and pathogens only need to match the dose with the nearest phenotype detector. Experimental results reveal that NKA can not only achieve the best performance through fewer detectors but also has a higher efficiency in the training and detection phase, compared with three versions of the distance-based NSA algorithms. Meanwhile, NKA generates comparable results compared with six popular machine learning algorithms.

关键词： Innate immune memory NK cell algorithm Artificial immune system Classification negative selection algorithm

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Multisource Neighborhood Immune Detector Adaptive Model for Anomaly Detection

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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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Artificial immunity-based induction motor bearing fault diagnosis

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第1期21卷 1-25页

作者： Calis, Hakan Cakir, Abdulkadir Dandil, Emre Suleyman Demirel Univ Fac Technol Dept Elect Elect Engn TR-32200 Isparta Turkey Bilecik Univ Vocat Sch Higher Educ Dept Comp Technol Bilecik Turkey Bilecik Univ Vocat Sch Higher Educ Programming Educ Program Bilecik Turkey

In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of activated detectors. Anomaly detection in the frequency domain is implemented by monitoring the fault indicator bearing frequencies and harmonics, calculated using the bearing dimensions and number of rotor revolutions. Due to the constant fault location and closeness to the accelerometer, the outer race fault in the bearing is the easiest fault type to determine. However, the most difficult fault type to detect is the ball defect. By verification of the detection results, the motor load has very little effect on the fault.

关键词： Induction motor fault diagnosis bearing defects artificial immunity negative selection algorithm

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An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 56卷 131-142页

作者： Lima, Fernando P. A. Lopes, Mara L. M. Lotufo, Anna Diva P. Minussi, Carlos R. Univ Estadual Paulista UNESP FEIS Dept Elect Engn Ave Brasil 56POB 31 BR-15385000 Ilha Solteira SP Brazil

This paper presents a new artificial immune algorithm with continuous-learning, which is inspired by the biological immune system, to realize the voltage diagnosis in electrical distribution systems. This conception allows one to compose a diagnosis system that can continuously learn without reinitialization when new disturbances occur due to the evolution of the electrical system. Two artificial immune algorithms, which are the negative selection algorithm and the clonal selection algorithm, are used for the pattern recognition process and the learning process, respectively. The principal application of this new method aids the operation during failures, supervises the protection system, and can evolve with the power systems to continuously acquire new knowledge. This new methodology has a direct impact in the area of diagnosis in electrical systems, as well as, in the pattern recognition problem, because the main contribution and novelty of this method is the continuous learning capability, which enables the system to learn unknown patterns without having to restart the knowledge. This is the major advantage of this methodology. To evaluate the efficiency and performance of this new method, failure simulations were performed in a real distribution system with 134 buses using the EMTP software. The results show robustness and efficiency. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Diagnosis Voltage disturbances Artificial immune systems negative selection algorithm Clonal selection algorithm Continuous-learning Electrical distribution systems

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Artificial immune system for fault detection and localization in a composite material plate with temperature variation

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第12期46卷 1-14页

作者： de Almeida, Estevao Fuzaro Chavarette, Fabio Roberto Merizio, Igor Feliciani Goncalves, Aparecido Carlos Sao Paulo State Univ Sch Engn Ilha Solteira Dept Mech Engn Ave Brasil56 BR-15385000 Ilha Solteira SP Brazil Sao Paulo State Univ Inst Chem Dept Engn Phys & Math Rua Prof Francisco Degni55 BR-14800060 Araraquara SP Brazil

Modern society is strongly reliant on structural and mechanical systems, and the usage of composite materials in this context has grown dramatically in recent decades. Because of some better qualities over metallic materials, they have been driven by various industry sectors. However, composites often have complex and anisotropic internal structures, which eventually lead to various types of damage, making possible structural failures difficult to diagnose and anticipate. In this context, structural health monitoring (SHM) refers to the process of identifying damage to engineering structures. One of the proposed solutions for SHM is so-called artificial immune systems (AISs), which replicate the human immune system, and this is a field of study that integrates immunology, computer science, and engineering to address complicated computational problems. As a result, the goal of this work is to implement an SHM approach for damage identification and localization based on impedance data from a composite material plate subjected to temperature variations and progressive damage growth. An optimized methodology involving signal analysis in the time domain was achieved through reading and processing based on the label of signals, which presented an F1-score equal to 1.0 and a 100% probability of damage detection is even capable of locating the path in which the damage is inserted. As a result of its average processing speed of 4.8 s and substantial memory capacity, an application for continuous monitoring of composite structures subjected to temperature variations was developed.

关键词： Structural health monitoring Artificial immune system negative selection algorithm Composite materials

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Improved V-detector algorithm based on bagging for earthquake prediction with faults

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JOURNAL OF SUPERCOMPUTING 2024年第16期80卷 24605-24637页

作者： Peng, Lu Liang, Yiwen Yang, He Wuhan Univ Sch Comp Sci 299 Bayi Rd Wuhan 430072 Hubei Peoples R China Wuchang Shouyi Univ Coll Informat Sci & Engn 22 Nanli Rd Wuhan 430064 Hubei Peoples R China Hubei Univ Educ Sch Comp Sci 129 Gaoxin 2nd Rd Wuhan 430205 Hubei Peoples R China

With the highly nonlinear relationship between various seismic feature indicators and earthquakes, the researchers can hardly construct an earthquake model. Meanwhile, the lack of samples for destructive earthquakes also leads to inaccurate medium-to-short-term earthquake magnitude predictions. Therefore, this study proposes a novel model for earthquake prediction, named variant detector bagging algorithm (V-detector-bagging). First, we adopt the geological distribution of earthquakes and faults as a criterion to select the appropriate history catalog data area, and the seismic indicators are calculated through the Gutenberg-Richter laws and Panakkat indicators. Then, we propose the V-detector-bagging algorithm, which combines the V-detector algorithm with the bagging method. The proposed algorithm converts the self-tolerance process into a cycle process, during this process, different selves guide samples to spread widely, generate various detectors covering more wide nonself areas, fill holes that are not covered by detectors in nonself areas, and reduce the false negative rate. Thus, the V-detector-bagging algorithm improves the detection performance of the V-detector. Finally, through experimental validation analysis, the proposed algorithm ranked first in the detection rate on Sichuan and Xinjiang catalog data compared to the popular machine learning methods used in predicting earthquakes and the original V-detector algorithm, with Xinjiang yielding the best results.

关键词： V-detector-bagging algorithm Earthquake prediction Fault zone Artificial immune system negative selection algorithm

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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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Based on Artificial Immunity Theory to Generate Pseudo Random-Sequence and Research on Encryption in Mobile Sensor

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SENSOR LETTERS 2013年第11期11卷 2168-2170页

作者： Yue, Mingyu Sichuan Univ Chengdu 610039 Peoples R China Chongqing City Management Coll Chongqing 404100 Peoples R China

Pseudo random-sequence that is based on artificial immunity theory is a new way. From this way, we can extracts the generation seeds from the plaintext message by itself to generate pseudo random-sequence, and use these seeds to generate a pseudo random-sequence by an improved m-sequences generated way. It has good self-related characteristics and good statistical properties. If it has been for data encryption, it has good performance of resistant to cryptanalysis attack. In addition, the receiver can use the generation seeds to test the integrity of the data.

关键词： negative selection algorithm Longer Period Pseudo Random-Sequence Generation Seeds Stream Cipher Mobile Sensor

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