检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

dendritic cell algorithm with Grouping Genetic algorithm for Input Signal Generation

引用

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

来源：评论

学校读者我要写书评

暂无评论

dendritic cell algorithm Applied to Ping Scan Investigation Revisited: Detection Quality and Performance Analysis

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017年第4期1卷 236-247页

作者： Silva, Guilherme Costa Caminhas, Walmir Matos de Errico, Luciano Fed Univ Minas Gerais PPGEE UFMG Grad Program Elect Engn BR-31270901 Belo Horizonte MG Brazil Fed Univ Minas Gerais DELT UFMG Dept Elect Engn BR-31270901 Belo Horizonte MG Brazil

The dendritic cell algorithm is an immune inspired method based on the danger model, which relies on cell interactions to antigens and signals, considering the correlation between both events, to solve anomaly detection problems. Starting with new datasets, comprising ping scans and file transfers in computer networks, this paper proposes improvements in the algorithm test methodology and qualitative analysis. Results measurement, detection capability, and performance evaluation are explored and discussed, including a real-time analysis and a comparison with similar approaches. Concluding, the study discusses advantages and limitations of the studied approach, suggesting possible improvements and new applications.

关键词： Artificial immune systems dendritic cell algorithm intrusion detection ping scan investigation

来源：评论

学校读者我要写书评

暂无评论

dendritic cell algorithm Enhancement Using Fuzzy Inference System for Network Intrusion Detection

Dendritic Cell Algorithm Enhancement Using Fuzzy Inference S...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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

ISBN: (纸本)9781538617281

dendritic cell algorithm (DCA) is an immune-inspired classification algorithm which is developed for the purpose of anomaly detection in computer networks. The DCA uses a weighted function in its context detection phase to process three categories of input signals including safe, danger and pathogenic associated molecular pattern to three output context values termed as co-stimulatory, mature and semi-mature, which are then used to perform classification. The weighted function used by the DCA requires either manually pre-defined weights usually provided by the immunologists, or empirically derived weights from the training dataset. Neither of these is sufficiently flexible to work with different datasets to produce optimum classification result. To address such limitation, this work proposes an approach for computing the three output context values of the DCA by employing the recently proposed TSK+ fuzzy inference system, such that the weights are always optimal for the provided data set regarding a specific application. The proposed approach was validated and evaluated by applying it to the two popular datasets KDD99 and UNSW NB15. The results from the experiments demonstrate that, the proposed approach outperforms the conventional DCA in terms of classification accuracy.

关键词： Fuzzy inference systems TSK dendritic cell algorithm Danger theory Network intrusion detection

来源：评论

学校读者我要写书评

暂无评论

dendritic cell algorithm with Fuzzy Inference System for Input Signal Generation 18th

Dendritic Cell Algorithm with Fuzzy Inference System for Inp...

引用

18th UK Workshop on Computational Intelligence (UKCI)

作者： Elisa, Noe Li, Jie Zuo, Zheming Yang, Longzhi Northumbria Univ Newcastle Upon Tyne NE1 8ST Tyne & Wear England

ISBN: (纸本)9783319979823;9783319979816

dendritic cell algorithm (DCA) is a binary classification system developed by abstracting the biological danger theory and the functioning of human dendritic cells. The DCA takes three signals as inputs, including danger, safe and pathogenic associated molecular pattern (PAMP), which are generated in its pre-processing and initialization phase. In particular, after a feature selection process for a given training data set, each selected attribute is assigned to one of the three input signals. Then, these input signals are calculated as the aggregation of their associated features, usually implemented by a simple average function followed by a normalisation process. If a nonlinear relationship exists between a signal and its corresponding selected attributes, the resulting signal using the average function may negatively affect the classification results of the DCA. This work proposes an approach named TSK-DCA to address such limitation by aggregating the assigned features of a signal linearly or non-linearly depending on their inherit relationship using the TSK+ fuzzy inference system. The proposed approach was evaluated and validated using the popular KDD99 data set, and the experimental results indicate the superiority of the proposed approach compared to its conventional counterpart.

关键词： dendritic cell algorithm TSK plus fuzzy inference system Information aggregation Danger theory

来源：评论

学校读者我要写书评

暂无评论

dendritic cell algorithm with Optimised Parameters using Genetic algorithm

Dendritic Cell Algorithm with Optimised Parameters using Gen...

引用

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

作者： Elisa, Noe Yang, Longzhi Naik, Nitin Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Minist Def Def Sch Commun Informat Syst London England

ISBN: (纸本)9781509060177

Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, dendritic cell algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated.

关键词： dendritic cell algorithm genetic algorithm danger theory network intrusion detection KDD99 dataset

来源：评论

学校读者我要写书评

暂无评论

A survey of the dendritic cell algorithm

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2016年第3期48卷 505-535页

作者： Chelly, Zeineb Elouedi, Zied Univ Tunis Inst Super Gest Tunis LARODEC Dept Comp Sci Tunis Tunisia

The dendritic cell algorithm (DCA) is a classification algorithm based on the functioning of natural immune dendritic cells. Recently, the DCA has caught the attention of researchers due to its worthy characteristics as it exhibits several interesting and potentially beneficial features for binary classification problems. Although the studies related with the DCA are increasingly becoming popular giving rise to several DCA hybrid algorithms, according to our best knowledge, there is no study summarizing the basic features of these algorithms nor their application areas all in one paper. Therefore, this study aims at summarizing the powerful characteristics of the DCA as well as making a general review of it. In addition, the DCA hybrid algorithms are reviewed and open research areas are discussed for further research.

关键词： Evolutionary computing dendritic cell algorithm Survey Classification Hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年第0期64卷 98-107页

作者： El-Alfy, El-Sayed M. AlHasan, Ali A. King Fand Univ Petr & Minerals Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia Saudi Aramco Dhahran 31311 Saudi Arabia

With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Mobile spam filtering Feature analysis Hybrid machine learning Information fusion Immune system dendritic cell algorithm

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Multiagent-based computer virus detection systems: abstraction from dendritic cell algorithm with danger theory

引用

TELECOMMUNICATION SYSTEMS 2013年第2期52卷 681-691页

作者： Ou, Chung-Ming Kainan Univ Dept Informat Management Tao Yuan 338 Taiwan

Biologically-inspired artificial immune systems (AIS) have been applied to computer virus detection systems (CVDS). A multiagent-based CVDS (ABCVDS) inspired by the danger theory of human immune system is proposed. The intelligence behind ABCVDS is based on the functionalities of dendritic cells in human immune systems. Multiple agents are embedded to this virus detection system, where agents coordinate one another to calculate mature context antigen value (MCAV). Accordingly, computer hosts undergone with malicious intrusions can be effectively detected via input signals and temporary output signals.

关键词： Artificial immune system Danger theory Multiagent system Computer virus detection dendritic cell algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：