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检索条件"主题词=dendritic cell algorithm"

共 61 条记录，以下是21-30 订阅

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A Novel Bio-Inspired Predictive Model for Spam Filtering Based on dendritic cell algorithm

A Novel Bio-Inspired Predictive Model for Spam Filtering Bas...

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IEEE Symposium on Computational Intelligence in Cyber Security

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

ISBN: (纸本)9781479945214

Electronic mail has become the most popular, frequently-used and powerful medium for quicker personal and business communications. However, one of the common security issues and annoying problems faced by email users and organizations is receiving a large number of unsolicited email messages, known as spam emails, every day. A traditional countermeasure in most email systems nowadays is simple filtering mechanisms that can block or quarantine unwanted emails based on some keywords defined by the user. These filters require continual effort to keep them relevant and current with some extensions proposed to improve their performance. However, due to the gigantic volumes of received emails and the continual change in spamming techniques to bypass the implemented solutions, novel automated ideas and countermeasures need to be investigated. This paper explores a novel algorithm inspired by the immune system called dendritic cell algorithm (DCA). This algorithm is evaluated on a number of benchmark datasets to detect spam emails. The results demonstrate that this approach can be a promising solution for email classification and spam filtering.

关键词： business communication classification information filtering security of data unsolicited e-mail DCA bio-inspired predictive model business communications dendritic cell algorithm electronic mail email classification personal communications security issues spam filtering unsolicited email messages Context Educational institutions Electronic mail Immune system Machine learning algorithms Monitoring Vectors electronic mail Unsolicited electronic mail business communication dendritic cells Personal Communication Machine learning algorithms Information filtering Data Security Dynamic Channel Allocation (DCA) Educational institutions Immune System Safety Problem

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The dendritic cell algorithm Optimized by Chemokine Simulator in Spyware Detection

The Dendritic Cell Algorithm Optimized by Chemokine Simulato...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yuan, Jie Tan, Chengyu Chen, Yuxi Xiao, Yue Gu, Yichun Wuhan Univ Comp Sch Wuhan 430072 Peoples R China Wuhan Univ Int Sch Software Wuhan Peoples R China

ISBN: (纸本)9781457715846

Spywares' changeable and complex hidden acts and behavior correlation increase the difficulty in detecting. Most of current detection methods put emphasis on the hidden characteristics whereas ignore the other. dendritic cell algorithm can combine a set of input signals deriving from antigens and classify them. In human immune system, the chemokine can stimulate dendritic cells' chemotaxis, guide them to the infected tissue. This passage takes behaviors and correlation among different behaviors fully into consideration, and employs the dendritic cell algorithm to detect spywares. Moreover, it can make the detection more efficient and accurate that applying chemokine simulator which imitates the function that chemokine does on immune system to drive the DCA and referencing concentration of chemokine into output signals to quicken the trend rate of CSM.

关键词： Danger Theory dendritic cell algorithm spyware hidden behavior chemokine

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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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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 Revised dendritic cell algorithm Using K-Means Clustering 20

A Revised Dendritic Cell Algorithm Using K-Means Clustering

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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

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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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Fault Diagnosis Research of Rotating Machinery Based on dendritic cell algorithm

Fault Diagnosis Research of Rotating Machinery Based on Dend...

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IEEE International Conference on Information and Automation 2015

作者： Cui, Delong Zhang, Qinghua Xiong, Jianbin Li, Qinxue Liu, Mei Guangdong Univ Petrochem Technol Coll Comp & Elect Informat Maoming 525000 Peoples R China Guangdong Prov Key Lab Fault Diag Petrochem Equip Maoming 525000 Peoples R China

ISBN: (纸本)9781467391047

Rotating machinery play an important role in modern industry. Ensuring security and reliability of manufacture equipments has been receiving more and more recognition. This paper proposes an innovative techniques for rotating machinery fault diagnosis. The fault diagnosis system is comprised of clearly defined separate submodels including antigen submodel, memory submodel, DC submodel, analyse submodel and diagnositic submodel etc. Based on the system model, a novel rotating machinery fault diagnosis scheme based on dendritic cell algorithm (DCA) and dimensionless parameter is proposed in this paper. To demonstrate our method, we apply our method to the real test bed of concurrent fault diagnosis for rotating machinery. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for rotating machinery and has high potential applications in real project.

关键词： rotating machinery fault diagnosis dendritic cell algorithm dimensionless parameter artificial immune

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ANDROID MALWARE DETECTION USING THE dendritic cell algorithm 13

ANDROID MALWARE DETECTION USING THE DENDRITIC CELL ALGORITHM

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International Conference on Machine Learning and Cybernetics

作者： Ng, Deniel V. Hwang, Jen-Ing G. Fu Jen Catholic Univ Dept Comp Sci & Informat Engn New Taipei Taiwan

ISBN: (纸本)9781479942152

Most smartphones run on Android OS, which facilitates the installation of third-party applications. Unfortunately, malware also exists for the Android. Malware can perform various harmful activities. In this paper, we have collected the behaviors of 100 Android applications. These collected applications consist of 50 benign applications and 50 pieces of malware. The invoked system calls were logged to serve as the behaviors of these applications. Then, the data were input to the dendritic cell algorithm (DCA). The DCA was inspired by a danger model of the human immune system and is able to detect anomalies. We used the features of the DCA to perform anomaly detection and classified the collected applications as either benign or malicious. Our experiment results showed that the DCA could achieve a higher accuracy than either the decision tree, the naive Bayes, or the support vector machine.

关键词： Malware dendritic cell algorithm Anomaly detection Android

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

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

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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

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A Rough Information Extraction Technique for the dendritic cell algorithm within Imprecise Circumstances

A Rough Information Extraction Technique for the Dendritic C...

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8th Hellenic Conference on Artificial Intelligence (SETN)

作者： Chelly, Zeineb Elouedi, Zied Univ Tunis LARODEC High Inst Management Tunis Tunis Tunisia

ISBN: (纸本)9783319070636;9783319070643

The dendritic cell algorithm (DCA) is an immune inspired classification algorithm based on the behavior of dendritic cells (DCs). The performance of DCA depends on the extracted features and their categorization to their specific signal types. These two tasks are performed during the DCA data pre-processing phase and are both based on the use of the Principal Component Analysis (PCA) information extraction technique. However, using PCA presents a limitation as it destroys the underlying semantics of the features after reduction. On the other hand, DCA uses a crisp separation between the two DCs contexts;semi-mature and mature. Thus, the aim of this paper is to develop a novel DCA version based on a two-leveled hybrid model handling the imprecision occurring within the DCA. In the top-level, our proposed algorithm applies a more adequate information extraction technique based on Rough Set Theory (RST) to build a solid data pre-processing phase. At the bottom level, our proposed algorithm applies Fuzzy Set Theory to smooth the crisp separation between the two DCs contexts. The experimental results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy.

关键词： dendritic cell algorithm Information Extraction Imprecise Reasoning Classification

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