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dendritic cell algorithm with Bayesian Optimization Hyperband for Signal Fusion

Computers, Materials & Continua 2023年第8期76卷 2317-2336页

作者： Dan Zhang Yu Zhang Yiwen Liang School of Computer Science Wuhan UniversityWuhan430072China GNSS Research Center Wuhan UniversityWuhan430079China

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune *** many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of *** loss function of DCA is ambiguous due to its *** reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter ***,these studies are either time-consuming or need to be revised in the case of non-convex *** overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss *** study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion *** BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential *** experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

关键词： dendritic cell algorithm signal fusion parameter optimization bayesian optimization hyperband

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dendritic cell algorithm with Grouping Genetic algorithm for Input Signal Generation

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2025-2045页

作者： Dan Zhang Yiwen Liang Hongbin Dong School of Computer Science Wuhan UniversityWuhan430072China School of Cyber Science and Engineering Wuhan UniversityWuhan430072China

The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune *** one of the prevalent classifiers,the dendritic cell algorithm(DCA)has been widely used to solve binary problems in the real *** classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain ***,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant *** filtered features and applying expertise may not produce an optimal classification *** overcome these limitations,this study models feature selection and signal categorization into feature grouping *** study hybridizes Grouping Genetic algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search *** GGA-DCA aims to search for the optimal feature grouping scheme without expertise *** this study,the data coding and operators of GGA are redefined for grouping *** experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

关键词： dendritic cell algorithm combinatorial optimization grouping problems grouping genetic algorithm

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A genetic algorithm-based dendritic cell algorithm for input signal generation

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APPLIED INTELLIGENCE 2023年第22期53卷 27571-27588页

作者： Zhang, Dan Zhang, Yu Liang, Yiwen Wuhan Univ Sch Comp Sci 299 Bayi Road Wuhan 430072 Hubei Peoples R China Wuhan Univ GNSS Res Ctr 299 Bayi Road Wuhan 430072 Hubei Peoples R China

The dendritic cell algorithm (DCA) is a classification algorithm based on the biological antigen presentation process. Its classification efficiently depends on a data preprocessing procedure, where feature selection and signal categorization are the main work for generating input signals. Several methods have been employed (e.g., correlation coefficient and rough set theory). Those studies preferred to measure the importance of features by evaluating their relevance to the class. Generally, they determined a mapping relationship between important features and signal categories of DCA based on expert knowledge. Typically, those studies ignore the effect of unimportant features, and the mapping relationship determined by expertise may not produce an optimal classification result. Thus, a hybrid model, GA-DCA, is proposed for feature selection and signal categorization based on the genetic algorithm (GA). This study transforms feature selection and signal categorization into a grouping task (i.e., divides features into different signal groups). This study introduces a permutation-based expression with "Group" symbols to represent a potential feature grouping scheme. Correspondingly, adaptive operators are proposed to expand each possible scheme on the path from the initial feature grouping to the best feature grouping. GA-DCA searches the optimal feature subset and automatically assigns them to the most suitable signal groups without expertise. This study verifies the proposed approach by employing the UCI Machine Learning Repository and Keel-dataset Repository, and significant performance improvement is achieved.

关键词： dendritic cell algorithm Genetic algorithm Input signal generation Feature grouping

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TwoDCA: A 2-Dimensional dendritic cell algorithm with Dynamic cell Migration

TwoDCA: A 2-Dimensional Dendritic Cell Algorithm with Dynami...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Greensmith, Julie Yang, Longzhi Univ Nottingham Sch Comp Sci Nottingham NG7 2QL England Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

The dendritic cell algorithm (DCA) is a multi-agent artificial immune system designed for anomaly detection. The algorithm is composed of artificial dendritic cell agents that process timestamped stream data. The lifespan of a cell agent is determined by its migration threshold, which influences the algorithm's dynamics significantly. The migration threshold is fixed during cell initialisation which limits the performance on various problems. This work proposes a dynamic migration threshold adjustment mechanism by mapping the population to a 2D grid and using Von Neumann Neighbourhoods to adapt this parameter at run time. This forms a novel algorithm variant termed 'twoDCA', implemented using the Repast Simphony agent based java API. This new algorithm is applied on synthetic stream data using a sin function generator with two different ways of migration threshold parameter generation. The experimental results show that the introduction of the Von Neumann Neighbourhoods has led to a statistically significant impact on certain behaviours of the algorithm. In particular, the great dynamics of twoDCA is realised by carrying forward the updated migration thresholds between cell reincarnations. The twoDCA is readily applicable to 2D data streams, which will diversify the range of applications substantially to which the algorithm can be applied and yields opportunities to add learning components to the core functionality of the algorithm.

关键词： Artificial Immune Systems dendritic cell algorithm Migration Threshold Von Neumann Neighbourhood

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Towards Bio-Inspired Anomaly Detection Using the Cursory dendritic cell algorithm

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algorithmS 2022年第1期15卷 1页

作者： Pinto, Carlos Pinto, Rui Goncalves, Gil Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal SYSTEC Res Ctr Syst & Technol Rua Dr Roberto Frias S-NOff i219 P-4200465 Porto Portugal

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems' dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the dendritic cell algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory dendritic cell algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work's contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms,

关键词： Artificial Immune Systems dendritic cell algorithm Cyber-Physical Production Systems anomaly detection fault detection Industry 4 0 smart manufacturing systems

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Multiresolution dendritic cell algorithm for network detection

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PEERJ COMPUTER SCIENCE 2021年 7卷 e749页

作者： Limon-Cantu, David Alarcon-Aquino, Vicente Univ Americas Puebla Dept Comp Elect & Mech Cholula Mexico

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as dendritic cell algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

关键词： Network anomaly detection Intruder detection systems Artificial immune systems Machine learning Wavelet transforms Wavelets dendritic cell algorithm

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Immune Inspired dendritic cell algorithm for Stock Price Manipulation Detection

Immune Inspired Dendritic Cell Algorithm for Stock Price Man...

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Intelligent Systems Conference (IntelliSys)

作者： Rizvi, Baqar Belatreche, Ammar Bouridane, Ahmed Univ Northumbria Newcastle Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9783030295165;9783030295158

Stock price manipulation is a term given to illicit or unlawful activities that tries to artificially impact a security's price. This alters the prime objective of transaction of stocks legally. This research presents a detection model for Stock price manipulation schemes like Pump & Dump and Spoof Trading. The proposed research is validated on tick data, containing time series with high volatility and high frequency trading that makes the detection process more difficult. A few number of past researches for price manipulation detection have been conducted based on unsupervised learning. Additionally, the existing researches also targeted specific manipulation schemes and a general detection method suitable enough to capture other anomalies is missing. This research proposes an unsupervised learning technique where dendritic cell algorithm is combined with non-parametric density estimation based clustering method for detecting price manipulation. The outcome of the proposed approach are evaluated using the area under Receiver Operating Characteristics (ROC) curve and a maximum value of 0.98 is achieved. The results in this work compared with existing benchmark approaches in unsupervised learning reports a significant improvement of 18%.

关键词： Market manipulation dendritic cell algorithm Immune system Anomaly detection KDE clustering

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Signal Categorisation for dendritic cell algorithm Using GA with Partial Shuffle Mutation 19th

Signal Categorisation for Dendritic Cell Algorithm Using GA ...

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Elisa, Noe Yang, Longzhi Chao, Fei Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Xiamen Univ Sch Informat Sci & Engn Cognit Sci Dept Xiamen Peoples R China

ISBN: (纸本)9783030299330;9783030299323

dendritic cell algorithm (DCA) is a bio-inspired system which was specifically developed for anomaly detection problems. In its preprocessing phase, the conventional DC requires domain or expert knowledge to manually categorise the input features for a given dataset into three signal categories termed as safe signal, pathogenic associated molecular pattern and danger signal. The manual preprocessing phase often over-fits the data to the algorithm, which is undesirable. The principal component analysis (PCA) and fuzzy-rough set theory (FRST) based-DCA techniques have been proposed to overcome the aforementioned limitation by automatically categorising the input features to their convenient signal categories. However, the PCA destroys the underlying meaning behind the initial features presented in the input dataset and generates poor classification performance, whilst FRST-DCA is only practical for very simple datasets. Therefore, this study investigates the employment of Genetic algorithm based on Partial Shuffle Mutation to automatically categorise the input features into the three signal categories. The experimental results of the proposed approach on eleven benchmark datasets have revealed its superiority over other versions of DCA in terms of accuracy, sensitivity and specificity.

关键词： dendritic cell algorithm Signal categorisation Genetic algorithm Partial shuffle mutation Features-to-Signal mapping

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A Study of the Necessity of Signal Categorisation in dendritic cell algorithm 19th

A Study of the Necessity of Signal Categorisation in Dendrit...

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19th Annual UK Workshop on Computational Intelligence (UKCI)

作者： Elisa, Noe Chao, Fei Yang, Longzhi Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Xiamen Univ Sch Informat Sci & Engn Cognit Sci Dept Xiamen Peoples R China

ISBN: (纸本)9783030299330;9783030299323

dendritic cell algorithm (DCA) is a binary classifier in the category of artificial immune systems. During its pre-processing phase, DCA requires features to be mapped into three signal categories including safe signal, pathogenic associated molecular pattern, and danger signal, which is usually referred to as signal categorisation. Conventionally, feature-to-signal mapping is performed either manually or automatically by using dimension reduction or feature selection techniques such as principal component analysis and fuzzy rough set theory. The former has been criticised for its potential over-fitting, whilst the latter may suffer from either the loss of underlying feature meaning or impractical for large and complex datasets. This work therefore investigate the necessity of the signal categorisation process by proposing a DCA without the use of signal categorisation but with generalised context detection functions, where the more complex parameters of these functions are learned using the genetic algorithm. This is followed by a comparative study on twelve well-known datasets;the experimental results show overall better performances in terms of accuracy, sensitivity and specificity compared to the conventional DCAs. This confirms that the signal categorisation phase is not necessary, if the weights of the generalised context detection functions can be optimised.

关键词： dendritic cell algorithm Signal categorisation Genetic algorithm Feature-to-signal mapping Classification

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A Comparative Study of Genetic algorithm and Particle Swarm Optimisation for dendritic cell algorithm

A Comparative Study of Genetic Algorithm and Particle Swarm ...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Elisa, Noe Yang, Longzhi Chao, Fei Naik, Nitin Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Xiamen Univ Dept AI Xiamen Peoples R China Minist Def Def Sch Commun Informat Syst London England

ISBN: (纸本)9781728169293

dendritic cell algorithm (DCA) is a class of artificial immune systems that was originally developed for anomaly detection in networked systems and later as a general binary classifier. Conventionally, in its life cycle, the DCA goes through four phases including feature categorisation into artificial signals, context detection of data items, context assignment, and finally labeling of data items as either abnormal or normal class. During the context detection phase, the DCA requires users to manually pre-define the parameters used by its weighted function to process the signals and data items. Notice that the manual derivation of the parameters of the DCA cannot guarantee the optimal set of weights being used, research attention has thus been attracted to the optimisation of the parameters. This paper reports a systematic comparative study between Genetic algorithm (GA) and Particle Swarm Optimisation (PSO) on parameter optimisation for DCA. In order to evaluate the performance of GA-DCA and PSO-DCA, twelve publicly available datasets from UCI machine learning repository were employed. The performance results based on the computational time, classification accuracy, sensitivity, F-measure, and precision show that, the GA-DCA overall outperforms PSO-DCA for most of the datasets.

关键词： dendritic cell algorithm particles swarm optimisation genetic algorithm danger theory artificial immune systems

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