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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Hui, Ronghua Lou, Peihuang Zhao, Peng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9783037852828

The detector sets generated by Real-Valued negative selection algorithm (RNSA) are usually numerous, without optimization, and can not work under real-time condition. Thus, a novel approach of detector generation for RNSA based on Clonal selection and Neighborhood Search (CSNS-RNSA) is proposed. Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence. The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search. The resulting detector sets achieved a good coverage of non-self space, and also significantly reduced the number of detector sets, thus overcome the limitations of original RNSA. Finally, experiments verify the effectiveness of the algorithm.

关键词： negative selection algorithm Detector Generation Clonal selection Neighborhood Search

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Fault Detection Using negative selection and Genetic algorithms

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Instrumentation 2019年第3期6卷 39-51页

作者： Anam ABID Zia Ul HAQ Muhammad Tahir KHAN University of Engineering and Technology PeshawarPakistan

In this paper,negative selection and genetic algorithms are combined and an improved bi-objective optimization scheme is presented to achieve optimized negative selection algorithm *** main aim of the optimal detector generation technique is maximal nonself space coverage with reduced number of diversified ***,researchers opted clonal selection based optimization methods to achieve the maximal nonself coverage milestone;however,detectors cloning process results in generation of redundant similar detectors and inefficient detector distribution in nonself *** approach proposed in the present paper,the maximal nonself space coverage is associated with bi-objective optimization criteria including minimization of the detector overlap and maximization of the diversity factor of the *** the proposed methodology,a novel diversity factorbased approach is presented to obtain diversified detector distribution in the nonself *** concept of diversified detector distribution is studied for detector coverage with 2-dimensional pentagram and spiral ***,the feasibility of the developed fault detection methodology is tested the fault detection of induction motor inner race and outer race bearings.

关键词： Detector Coverage Diversity Factor Fault Detection Genetic algorithm negative selection algorithm

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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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Detecting RTL Trojans Using Artificial Immune Systems and High Level Behavior Classification

Detecting RTL Trojans Using Artificial Immune Systems and Hi...

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作者： Zareen, Farhath University of South Florida

学位级别：M.S.

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need fora golden reference model. We explore the use of negative selection and Clonal selection algorithms, which have historically been applied to malware detection on software binaries, to detect potentially unsafe or malicious behavior in hardware. We present a software tool which analyzes Trojan-inserted benchmarks, extracts their control and data-flow graphs (CDFGs), and uses this to train an AIS behavior model, against which new hardware descriptions may be tested.

关键词： Clonal selection algorithm Control/Data-Flow Graphs Hardware Security Hardware Trojans negative selection algorithm

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Based on negative selection algorithm to generate long-period pseudo-random sequence

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Research Journal of Applied Sciences, Engineering and Technology 2012年第14期4卷 2167-2170页

作者： Yue, Mingyu Department of Electronic Information Sichuan University Chengdu 610065 China Chongqing City Management College Information Center Chongqing 400055 China

This study presents a method of generating long-period pseudo-random sequence based on a plaintext and negative selection algorithm for real applications. It is a new method that extracts the generated factor from the plaintext. Moreover, the generated factor as a group is expanded by the way of the m-sequence and generates pseudo-random. The autocorrelation and statistical properties of the method are better. If it used for data encrypted, it has a good anti-attack performance. In addition, the generate factor can also be used for immune identification in receiver end, to verify data integrity. So there will be a good prospect. © Maxwell Scientific Organization, 2012.

关键词： Generated factor Long-period negative selection algorithm Pseudo-random sequence Simulation

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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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negative selection algorithm Using Natural Frequency for Novelty Detection under Temperature Variations

Negative Selection Algorithm Using Natural Frequency for Nov...

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International Conference on Structures and Building Materials

作者： Li, Miao Ren, Wei-Xin Cent S Univ Dept Civil Engn Changsha 410075 Hunan Peoples R China

ISBN: (纸本)9780878492060

The vibration features are affected by damage in structure and environmental conditions while the bridges are in the operation. Environment effects should not be ignored in making correct diagnoses of structures. negative selection algorithm inspired by immune system has the capability for self-nonself discrimination. Temperature effect on natural frequency is analyzed in the paper, and the algorithm based on Euclidean distance is applied to natural frequencies of structures under temperature variations. The results indicate that negative selection algorithm using natural frequency passes the false-positive tests, and effectively detect the anomalous condition of structure under varying temperature.

关键词： Artificial Immune System negative selection algorithm Temperature Variatio Temperature Effect Novelty Detection Natural Frequency

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Detecting RTL Trojans using Artificial Immune Systems and High Level Behavior Classification

Detecting RTL Trojans using Artificial Immune Systems and Hi...

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3rd Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

作者： Zareen, Farhath Karam, Robert Univ S Florida Dept CSE Tampa FL 33620 USA

ISBN: (纸本)9781538674710

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development, or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need for a golden reference model. We explore the use of negative selection and Clonal selection, which have historically been applied to malware detection on software binaries, to detect potentially unsafe or malicious behavior in hardware. We present a software tool which analyzes Trojan-inserted benchmarks, extracts their control and data-flow graphs (CDFGs), and uses this to train an AIS behavior model, against which new hardware descriptions may be tested. The proposed model is capable of detecting the specified (Trojan or Trojan-like) behavior with an accuracy of -85% and an average false negative rate of 12.6% for negative selection and 12.8% for Clonal selection.

关键词： Hardware Trojans Hardware Security Artificial Immune Systems CDFG negative selection algorithm Clonal selection algorithm

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Insider Threat Detection using an Artificial Immune system algorithm 9

Insider Threat Detection using an Artificial Immune system A...

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9th Annual IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Igbe, Obinna Saadawi, Tarek CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781538676936

Insider threats result from legitimate users abusing their privileges, causing tremendous damage or losses. Malicious insiders can be the main threats to an organization. This paper presents an anomaly detection system for detecting insider threat activities in an organization using an ensemble that consists of negative selection algorithms (NSA). The proposed system classifies a selected user activity into either of two classes: "normal" or "malicious." The effectiveness of our proposed detection system is evaluated using case studies from the computer emergency response team (CERT) synthetic insider threat dataset. Our results show that the proposed method is very effective in detecting insider threats.

关键词： Insider Threat Anomaly Detection Ensemble Malicious Insider negative selection algorithm

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Optimization and Application of Real-valued negative selection algorithm

Optimization and Application of Real-valued Negative Selecti...

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International Conference on Power Electronics and Engineering Application (PEEA)

作者： Xu Aiqiang Liu Yong Zhao Xiuli Yang Chunying Li Tingjun Naval Aeronaut & Astronaut Univ Yantai 264001 Peoples R China

Concerned with the problem of lacking fault samples of complex equipments, it studies the principle and application of negative selection algorithm of artificial immune system. The detectors generation mechanism of real-valued negative selection algorithm is introduced. Intuitively, to maximize the covering produced by a set of detectors, it is necessary to reduce their overlapping and not covering the self set. This paper presents an optimization strategy base on re-heating simulated annealing algorithm to modify the position of detectors, not changing their number. This method can improve the covering effect of non-self space. The triangle training data are used to demonstrate the properties of optimized VRNS. Detection rate is improved and false alarming rate is decreased. It is used for fault detection in analog circuit;result demonstrates that the proposed algorithm is better than artificial neural network in fault detection of this circuit. (C) 2011 Published by Elsevier Ltd. selection and/or peer-review under responsibility of [name organizer]

关键词： negative selection algorithm re-heating simulated annealing optimization fault detection

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