检索结果-内蒙古大学图书馆

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

Migration Threshold Tuning in the Deterministic dendritic cell algorithm 8th

Migration Threshold Tuning in the Deterministic Dendritic Ce...

引用

8th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Greensmith, Julie Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9783030345006;9783030344993

In this paper we explore the sensitivity of the migration threshold parameter in the Deterministic dendritic cell algorithm (dDCA), one of the four main types of Artificial Immune System. This is with a view to the future construction of a DCA augmented with Deep Learning. Learning mechanisms are absent in the original DCA although tuneable parameters are identified which have the potential to be learned over time. Proposed in this paper is the necessary first step towards placing the dDCA within the context of Deep Learning by understanding the maximum migration threshold parameter. Tuning the maximum migration threshold determines the results of the signal processing within the algorithm, and here we explore a range of values. We use the previously explored Ping Scan Dataset to evaluate the influence of this key parameter. Results indicate a close relationship between the maximum migration threshold and the signal values of given datasets. We propose in future to ascertain an optimisation function which would learn the maximum migration threshold during run time. This work represents a necessary step towards producing a DCA which automatically interfaces with any given anomaly detection dataset.

关键词： Artificial immune systems dendritic cell algorithm Parameter tuning

来源：评论

学校读者我要写书评

暂无评论

A New Cryptojacking Malware Classifier Model Based on dendritic cell algorithm 3

A New Cryptojacking Malware Classifier Model Based on Dendri...

引用

3rd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Ahmad, Azuan Zainudin, Wan Shafiuddin Kama, Mohd Nazri Bin Ismail, Saiful Adli Saudi, Madihah Mohd Univ Sains Islam Malaysia Islamic Sci Inst Negeri Sembilan Malaysia CyberSecur Malaysia Seri Kembangan Selangor Malaysia Univ Teknol Malaysia Fak Teknol & Informat Razak Johor Baharu Johor Malaysia Univ Sains Islam Malaysia Islamic Sci Inst Nilai Negeri Sembilan Malaysia

ISBN: (纸本)9781450376259

A new threat known as "cryptojacking" has entered the picture where cryptojacking malware is the future trend for cyber criminals, who infect victim's device, install cryptojacking malware, and use the stolen resources for crytocurrency mining. Worse comes to worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. IoT devices are vulnerable to attacks because of their simple configuration, unpatched vulnerability and weak passwords. IoT devices also prone to be poorly monitored because of their nature. There is lack of studies that provide in depth analysis on cryptojacking malware classification using machine learning approach where the current research mostly focused on manual analysis of web-based cryptojacking attacks. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on machine learning technique that may fit in a low processing capability environment such as IoT and Cyber Physical Systems (CPS). This paper aim to disscuss a new approach based on dendritic cell algorithm in order to provide a lightweight cryptojacking classifier model. The output of this paper will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries.

关键词： Artificial Intelligence Cryptojacking Cyber Security Malware dendritic cell algorithm

来源：评论

学校读者我要写书评

暂无评论

A dendritic cell algorithm Based Approach for Malicious TCP Port Scanning Detection 15

A Dendritic Cell Algorithm Based Approach for Malicious TCP ...

引用

15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Almasalmeh, Nuha Saidi, Firas Trabelsi, Zouheir United Arab Emirates Univ Coll Informat Technol Al Ain U Arab Emirates Univ Manouba Natl Sch Comp Sci Manouba Tunisia

ISBN: (纸本)9781538677476

The proliferation of cyber-attacks brings up an urgent need to develop sophisticated detection tools. Some of these tools are based on algorithms inspired from the Human Immune System (HIS). The dendritic cell algorithm (DCA) is one of such HIS inspired methods, which is based on the Danger model. In the current study, two kinds of DCA algorithms (deterministic and classical DCA) are analyzed in order to detect DoS attacks. Moreover, this paper discusses the enhancement of the DCA algorithm to cover the detection of malicious TCP port scanning. By using different use-cases, the evaluation and results are accumulated to show the efficiency of the DCA algorithm used in the Port scanning detection.

关键词： Artificial immune systems dendritic cell algorithm denial of service intrusion detection port scanning

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 New Cryptojacking Malware Classifier Model Based on dendritic cell algorithm 2019

A New Cryptojacking Malware Classifier Model Based on Dendri...

引用

Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Azuan Ahmad Wan Shafiuddin Mohd Nazri Kama Madihah Mohd Saudi Islamic Science Institute Universiti Sains Islam Malaysia Negeri Sembilan Malaysia Zainudin CyberSecurity Malaysia Seri Kembangan Selangor Malaysia Saiful Adli Bin Ismail Fakulti Teknologi dan Informatik Razak Universiti Teknologi Malaysia

ISBN: (纸本)9781450376259

关键词： Artificial Intelligence dendritic cell algorithm Malware Cryptojacking Cyber Security

来源：评论

学校读者我要写书评

暂无评论

A scalable and distributed dendritic cell algorithm for big data classification

引用

SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100432-000页

作者： Dagdia, Zaineb Chelly Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales Inst Super Gest Tunis LARODEC Tunis Tunisia

In the era of big data, scaling evolution up to large-scale data sets is a very interesting and challenging task. The application of standard biological systems in such data sets is not straightforward. Therefore, a new class of scalable biological systems that embraces the huge storage and processing capacity of distributed platforms is required. In this work, we focus on the dendritic cell algorithm (DCA), a bio-inspired classifier, and its limitation when coping with very large data sets. To overcome this limitation, we propose a novel distributed DCA version for data classification based on the MapReduce framework to distribute the functioning of this algorithm through a cluster of computing elements. Our experimental results show that our proposed distributed solution is suitable to enhance the performance of the DCA enabling the algorithm to be applied over big data classification problems.

关键词： dendritic cell algorithm Big data Distributed processing

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

A Revised dendritic cell algorithm Using K-Means Clustering 20

A Revised Dendritic Cell Algorithm Using K-Means Clustering

引用

20th IEEE International Conference on High Performance Computing and Communications (HPCC) / 16th IEEE International Conference on Smart City (SmartCity) / 4th IEEE International Conference on Data Science and Systems (DSS)

作者： Elisa, Noe Yang, Longzhi Qu, Yanpeng Chao, Fei Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Dalian Maritime Univ Informat Sci & Technol Coll Dalian Peoples R China Xiamen Univ Dept Cognit Sci Xiamen Peoples R China

ISBN: (纸本)9781538666142

The most daunting and challenging task in intrusion detection is to distinguishing between normal and malicious traffics effectively. In order to complete such a task, the biological danger theory has appeared to be one of the most appealing immunological models which has been converted to a computer science algorithm, named as dendritic cell algorithm (DCA). To perform a binary classification, the DCA goes through four phases, preprocessing, detection, context assessment and classification. In particular, the context assessment phase is performed by comparing the signal concentration values between mature (i.e., abnormality) and semi-mature (i.e., normality) contexts. The conventional DCA requires a crisp separation between semi-mature and mature cumulative context values. This can be hard if the difference between the two contexts is marginal, which negatively affects the classification accuracy. In addition, it is technically difficult to quantify the actual meaning of semi-mature and mature in the DCA. This paper proposes an approach that integrates the K-Means clustering algorithm to the DCA to map the DCA cumulative semi-mature and mature context values into semi-mature (normal) and mature (anomaly) clusters in order to improve the classification accuracy. The KDD99 data set was utilized in this work for system validation and evaluation, and the experimental results revealed an improvement in the classification accuracy by the proposed approach.

关键词： dendritic cell algorithm Danger theory K-Means clustering Artificial immune systems

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：