Negative Selection Algorithm Research Articles

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

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.

AiMRS: A feature extraction method from MRS signals based on artificial immune algorithms for classification of brain tumours

Precise diagnosis of brain tumour by experienced radiologists involves a complex set of processes including magnetic resonance imaging, magnetic resonance spectroscopy (MRS) data and histopathological evaluations. In this study, a new hybrid feature extraction method, called as aiMRS, based on the negative selection algorithm and clonal selection algorithm of artificial immune systems is developed on MRS data for the detection and classification of brain tumours. In the study, differentiation of benign and malignant brain tumours, classification of normal brain tissue and brain tumour, and detection of metastasis and primary brain tumours are performed with high precision using pattern recognition methods based on the proposed aiMRS method. According to the experimental results performed on a large data set created with the MRS data obtained from INTERPRET database, when the proposed feature extraction method applied, classification of normal brain tissue and brain tumours, benign and malignant brain tumours and metastasis and primary brain tumours is achieved with 100, 98.58 and 98.94% accuracy, respectively. These results show that this proposed system can be used as a secondary tool in physicians' decision-making processes for the classification of brain tumours.

Fault diagnosis in wireless sensor network using negative selection algorithm and support vector machine

AbstractIn this article, an improved negative selection algorithm (INSA) has been proposed to identify faulty sensor nodes in wireless sensor network (WSN) and then the faults are classified into soft permanent, soft intermittent, and soft transient fault using the support vector machine technique. The performance metrics such as fault detection accuracy, false alarm rate, false positive rate, diagnosis latency (DL), energy consumption, fault classification accuracy (FCA), and false classification rate (FCR) are used to evaluate the performance of the proposed INSA. The simulation result shows that the INSA gives better result as compared to the existing algorithms in terms of performance metrics. The fault classification performance is measured by FCA and FCR. It has also seen that the proposed algorithm gives less DL and consumes less energy than that of existing algorithms proposed by Mohapatra et al, Zhang et al, and Panda et al for WSN.

Interface Detector Based on Vaccination Strategy for Anomaly Detection

Interface detector is an enhanced negative selection algorithm with online adaptive learning under small training samples for anomaly detection. It has better detection performance when it has an appropriate self-radius. Otherwise, overfitting or underfitting would occur. In the present paper, an improved interface detector, which is based on vaccination strategy, is proposed. During the testing stage, negative vaccine can overcome overfitting to improve the detection rate and positive vaccine can overcome underfitting to reduce the false alarm rate. The experimental results show that under the same dataset, self-radius, and training samples condition, the detection rate of the interface detector with negative vaccine is much higher than that of interface detector, SVM, and BP neural network. Moreover, the false alarm rate of the interface detector with positive vaccine is much lower than that of the interface detector and PSA.

An Improved Negative Selection Algorithm-Based Fault Detection Method

Fault detection has been an active research field and increasingly important for the safety of technical processes and systems. A variety of fault detection methods have been developed that are generally designed for a specific system; therefore, they target limited types of fault. An efficient approach that could monitor the degradation conditions and be less problem-specific is necessary for improved reliability and efficiency. This paper presents an improved negative selection algorithm using specialized detectors that is model-free and independent of prior knowledge about fault types. An artificial immune system employs a negative selection algorithm and requires only normal (self) patterns for detector generation. In the training phase, uncovered gaps are identified and covered with new detectors to improve nonself (faulty) space coverage. Moreover, to alleviate the online detection, cost reshaping process of nonself space with detector clusters is performed. Theoretical analysis shows that the nonself space coverage with a specialized detector is effectively improved. In experimental analysis, three data sets are used for training and testing, including two-dimensional patterns (cross-mid, ring-mid), Fisher Iris, and KDD CUP99. The experimental results show that the accuracy of the developed method is better than that of the standard negative selection algorithm and other established machine learning algorithms with reduced online anomaly detection time.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Image Characteristics Extraction and Actual Value Negative Selection Algorithm

With the development of information theory and image analysis theory, the studies on fault diagnosis methods based on image processing have become a hot spot in the recent years in the field of fault diagnosis. The gearbox of wind turbine generator is a fault-prone subassembly. Its time frequency of vibration signals contains abundant status information, so this paper proposes a fault diagnosis method based on time-frequency image characteristic extraction and artificial immune algorithm. Firstly, obtain the time-frequency image using wavelet transform based on threshold denoising. Secondly, acquire time-frequency image characteristics by means of Hu invariant moment and correlation fusion gray-level co-occurrence matrix of characteristic value, thus, to extract the fault information of the gearing of wind turbine generator. Lastly, diagnose the fault type using the improved actual-value negative selection algorithm. The application of this method in the gear fault diagnosis on the test bed of wind turbine step-up gearbox proves that it is effective in the improvement of diagnosis accuracy.

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

PurposeIn 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/approachThe 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 detection.FindingsThe present study aims to develop different fast access methodologies for the prognosis and detection of failures, classifying and judging the types of failures based on AISs. The authors declare that the present study was neither published in any other vehicle of scientific information nor is under consideration for publication in another scientific journal, and that this paper strictly followed the ethical procedures of research and publication as requested.Originality/valueThis study is original by the fact that conventional structural integrity monitoring methods need improvements, which intelligent computing techniques can satisfy. Intelligent techniques are tools inspired by natural and/or biological processes and belong to the field of computational intelligence. They present good results in problems of pattern recognition and diagnosis and thus can be adapted to solve problems of monitoring and identifying structural failures in mechanical and aeronautical engineering. Thus, the proposal of this study demonstrates and strengthens the emergence of an innovative technological tool, the biological immune system and the AIS, and these are presented as computation methods in the field of SHM in rotating systems – a topic not yet addressed in the literature.

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Objectives: To generate complete and non-redundant detector set with optimal worst-case time complexity. Methods: In this study, a novel exact matching and string-based Negative Selection Algorithm utilizing r-chunk detectors is proposed. Improved algorithms are tested on some data sets; the experiments’ results are compared with recently published ones. Moreover, algorithms’ complexities are also proved mathematically. Findings: For string-based Artificial Immune Systems, r-chunk detector is the most common detector type and their generation complexity is one of the important factors considered in the literature. We proposed optimal algorithms based on automata to present all detectors. Novelty/applications: The algorithm could generate the representation of complete and nonredundant detector set with optimal worst-case time complexity. To the best of our knowledge, the algorithm is the first one to possess such worst-case training time complexity. Keywords: Artificial Immune Systems, Negative Selection Algorithms, Positive Selection Algorithms, Detector Sets, Self, Non-self.

Performance assessment of a bio-inspired anomaly detection algorithm for unsupervised SHM: application to a Manueline masonry church

Vibration-based techniques are commonly used in structural health monitoring (SHM) to assess the condition of structural systems and identify the presence of damage. Negative selection algorithms (NSAs) are bio-inspired methods which allow to automatise the damage detection process by classifying the monitored system's features as normal or abnormal. In this paper, an NSA with a non-random strategy for detector generation is tested on the monitoring data of a remarkable masonry church in Portugal. The work aims to make users aware of NSA potential, contributing to a diligent application of the method in terms of best algorithm instance definition. Different setting approaches for the algorithm parameters are discussed and compared, exploiting artificial outliers of the features distribution to assess the NSA performance. Such a strategy allows the optimisation of the algorithm in most of the civil engineering applications where no information about the features belonging to unhealthy scenarios is available.

Negative Selection Algorithm Based on Antigen Density Clustering

The negative selection algorithm (NSA) is one of the basic algorithms of the artificial immune system. In the traditional negative selection algorithm, candidate detectors are randomly generated without considering the uneven distributions of self-antigens and nonself-antigens, thereby resulting in many redundant detectors, and it is difficult for these detectors to fully cover the area of nonself-antigens. To overcome the problem of low detector generation efficiency, a negative selection algorithm that is based on antigen density clustering (ADC-NSA) is proposed in this paper. The algorithm divides the process of detector generation into three steps: the first step is to calculate the density of the antigens by using the method of antigen density clustering to select nonself-clusters. The second step is to prioritize the abnormal points (nonself-antigens that are not clustered) as the centers of candidate detectors and to generate the detectors via calculation. The third step is to generate the detectors via the traditional algorithm. Detector generation via these three steps can reduce the randomness of the detector generation in the traditional algorithm, thereby improving the efficiency of detector generation. The experimental results demonstrate that on the BCW and KDD-Cup datasets, the negative selection algorithm that is based on antigen density clustering can effectively increase the detection rate while reducing the false-positive rate compared with the traditional negative selection algorithm (RNSA) and two improved algorithms at the same expected coverage.

Infrared Target Extraction Based on Immune Extension Neural Network

This paper addresses the difficulties of target extraction from low contrast low resolution infrared images. The extension neural network is integrated with the negative selection algorithm (NSA) in artificial immunity. Based on the specific recognition principle of long non-coding RNA (lncRNA) in biological immune system, the mature self-extension detectors are calculated to extract infrared target and background. Upon the convergence of clustering process, the output layer of network merges the categories corresponding to the target to be detected. The proposed method, it needs less detectors than the traditional NSA algorithm. Compared with several target detection algorithms, the proposed algorithm has higher detection accuracy and higher time efficiency.

Detekcija napada zasnovana na veštačkom imunom sistemu

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.

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

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.

Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detection

This paper proposes a novel multidomain features-based genetic algorithm (GA) optimized artificial immune system (AIS) framework for fault detection in real systems. Different from native real-valued negative selection algorithm (RNSA) that operates in original data space, this algorithm utilizes feature space transformation and diversity factor-based GA for optimized detector distribution in nonself feature space. The proposed framework comprises three stages namely; feature extraction, unsupervised feature selection, and GA optimized AIS. In the first stage, signal processing methods are applied to extract multidomain features (time-domain statistical, frequency domain statistical, and special features) of the system. In the second stage, two unsupervised methods namely, ${k}$ -NN clustering and pretraining using deep learning neural network are proposed for dominant fault-characterizing feature selection. Finally, in the third stage, the fault-characterizing feature vectors are used for system status categorization (i.e., normal, fault) using selected (fault-characterizing) features-based AIS method. The efficacy of the proposed framework is verified through experiments on motor bearing fault detection using vibration signal. The major accomplishment of the proposed combination of space transformation, feature selection and AIS (anomaly classification) techniques is the alleviation of computational burden on RNSA implementation. Moreover, GA optimized AIS fault diagnosis based on well-established features gives improved detection performance.

DETECÇÃO DE FALHAS ESTRUTURAIS DE UM PRÉDIO DE DOIS ANDARES UTILIZANDO SISTEMA IMUNOLÓGICO ARTIFICIA

This work presents the innovative proposal for the development of SHMs with focus on physical cyber systems applied in a two-story building based on Intelligent Computing (CI) techniques, the negative selection algorithm from the Artificial Immune System, to perform the analysis and monitoring of structural integrity in a building.

Anomaly process detection using negative selection algorithm and classification techniques

Artificial immune system is derived from the biological immune system. This system is an important method for generating detectors that include self-adaption, self- regulation and self-learning which have self/non-self-detection features. This method is used in anomaly process detection where the anomaly is non-self in the system. We present a new combining technique for anomaly process detection. This combined technique is a unification of both negative selection and classification algorithm. The main aim of the proposed techniques is to increase the accuracy in this system while decreasing its training time. In this research, CICIDS 2017 and NSL-KDD dataset with different sets of features and the same number of detectors are used. This paper presents a framework for detecting anomaly processes on a host base computer system which is established on the artificial immune system. We evaluate our technique using machine learning algorithms such as: logistic regression, random forest, decision tree and K-neighbors. Moreover, we use WEKA tool classification to perform a correlation based feature selection on the dataset.

Use The Positive Selection Algorithm and The Negative Selection Algorithm in The English Character Recognition

Use The Positive Selection Algorithm and The Negative Selection Algorithm in The English Character Recognition

Yapay Bağışıklık Algoritmaları ile Web Trafik Verilerinde Anomali Tespiti

In recent years, different types of threats and attacks continue to increase in the internet world. It is important to detect anomalies in the time series quickly and accurately for web traffic data measured by the number of online visitors. Different methodologies and data classification techniques are used to detect abnormal traffic in network data. This problem is generally evaluated by classifying the signal windows by feature extraction. In this study, a method based on the Negative Selection Algorithm (NSA) of Artificial Immune Systems for the detection of abnormal web traffic on the network is proposed and a user-friendly application software is developed. For web traffic, the real data in the Yahoo Webscope S5 dataset is used and the data is split into windows using the window shift method. In the experimental studies, the detection of abnormal traffic data in the web traffic data is realized by monitoring the changes in the number of activated detectors in the NSA structure. On the application software developed in the study, it is observed that abnormal conditions in the web traffic data are detected with low error rates with NSA.

Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

A Preliminary Survey on Artificial Immune Systems (AIS): A Review on Their Techniques, Strengths and Drawbacks

An Artificial Immune System (AIS) is defined as computational intelligence evolved from immunology (biological immune systems) that tries to replicate the way human defensive system works. In this paper, the researchers categorize, compare, and summarize all the major techniques and algorithms of AIS. Particularly, the discussion highlights on the theoretical aspects, techniques, and algorithms of three well-established techniques of AIS, namely the negative selection algorithm, immune network algorithms, and clonal selection algorithms by focusing on their similarities and differences. In addition, this paper elaborates the differences among the techniques and algorithms in terms of the types of data, learning, concepts, elements, generation and operations, strengths and drawbacks, application areas and the evolution of AIS techniques. The techniques selected for this research was guided by beneficial previous literatures carried out by Dasgupta, de Castro, Timmis, and Luo. The findings of this study can help provide greater insights into the understanding of the three AIS techniques, which can further improve the current practice of practitioners and enrich the body of knowledge, benefiting researchers, educators, students, and others.