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8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Igbe, Obinna Ajayi, Oluwaseyi Saadawi, Tarek CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781538611043

Denial-of-service (DoS) and distributed denial-of-service (DDoS) is one of the most popular and easy-to-implement attacks targeting online systems and networks. This paper presents a system for detecting DoS attacks in a network using the dendritic cell algorithm (DCA). The proposed system classifies incoming network traffic into either of two classes: "normal" or "DoS attack." This paper investigates some common DoS/DDoS attacks that target communication networks, and how these attacks can be detected using the dendritic cell algorithm (DCA) which is an Artificial Immune System (AIS)-based algorithm. The proposed detection system is evaluated using the popular NSL-KDD dataset. Our results show that our system is very effective in detecting DoS/DDoS attacks with very high accuracy.

关键词： DoS Artificial Immune System Danger Theory dendritic cell algorithm IDS

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The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using dendritic cell algorithm 5

The Significant Effect of Feature Selection Methods in Spam ...

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5th International Conference on Information and Communication Technology (ICoIC7)

作者： Zainal, Kamahazira Jali, Mohd Zalisham Univ Sains Islam Malaysia Fac Sci & Technol Nilai N Sembilan Malaysia

ISBN: (纸本)9781509049127

The vast amount of online documentation and the thriving of Internet especially mobile technology have caused a crucial demand to handle and organize unstructured data appropriately. An information retrieval or even knowledge discovery can be enhanced when a proper and structured data are available. This paper studies empirically the effect of pre-selected term weighting schemes, namely as Term Frequency (TF), Information Gain Ratio (IG Ratio) and Chi-Square (CHI2) in the assessment of a threat's impact loss. This feature selection method then further fed in conjunction with the dendritic cell algorithm (DCA) as the classifier to measure the risk concentration of a spam message. The final outcome of this research is very much expected to be able in assisting people to make a decision once they knew the possible impact caused by a particular spam. The findings showed that TF is the best feature selection methods and well suited to be demonstrated together with the DCA, resulted with high accuracy risk classification rate.

关键词： term weighting schemes feature selection methods dendritic cell algorithm spam severity assessment spam risk concentration

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Detecting Denial of Service Attacks using a Combination of dendritic cell algorithm and the Negative Selection algorithm 2

Detecting Denial of Service Attacks using a Combination of D...

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2nd IEEE International Conference on Smart Cloud (SmartCloud)

作者： Igbe, Obinna Ajayi, Oluwaseyi Saadawi, Tarek CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781538636848

As one of the most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on computing systems and networks. This paper presents a system for detecting denial of service (DoS) attacks in a network using a combination of the dendritic cell algorithm (DCA) and the negative selection algorithm (NSA). The proposed system classifies incoming network traffic into either of two classes: "normal" or "DoS attack." Experimentally, our approach follows a majority voting technique by creating multiple instances of the DCA and the NSA algorithm and assigning weights to their respective output. The effectiveness of our proposed detection system is evaluated using an in-house generated signal dataset. Our results show that our system is very effective in detecting DoS/DDoS attacks with very high accuracy. Analysis of the proposed DoS detection system is also presented.

关键词： DoS Artificial Immune System Negative Selection algorithm dendritic cell algorithm IDS

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A survey of the dendritic cell algorithm

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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

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

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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

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An upper and lower CUSUM for signal normalization in the dendritic cell algorithm

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EVOLUTIONARY INTELLIGENCE 2016年第1-2期9卷 37-51页

作者： Mohsin, Mohamad Farhan Mohamad Hamdan, Abdul Razak Abu Bakar, Azuraliza Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Data Min & Optimizat Res Grp Bangi 43600 Selangor Malaysia Univ Utara Malaysia Coll Arts & Sci Sch Comp Sintok Kedah Malaysia

Signal normalization is a part of signal formalization which is a vital data pre-processing constraint required for the functioning of the dendritic cell algorithm. In existing applications, most normalization algorithms are developed purposely for a specific application with drawing on human domain expertise and very few algorithms are designed for general problems. This makes it difficult for the inexperienced user to exploit existing approaches to another problem, particularly when the initial information about the problem is limited. Therefore, this study proposes a new signal normalization method for the dendritic cell algorithm that uses the statistical upper and lower cumulative sum so that the algorithm can be applied to general classification problems. In addition, a new method to calculate the anomaly threshold based on the average mature-contact antigen value is presented to suit the proposed algorithm. The proposed model is evaluated by applying it to eight universal classification datasets and assessing its performance according to four measurement metrics: detection rate, specificity, false alarm rate, and accuracy. Its performance is compared with that of the existing dendritic cell algorithm and four non-bio-inspired classifiers, namely, rough set, decision tree, naive Bayes, and multilayer perceptron. The results show that the proposed model outperforms the existing model and the other classifiers as well as demonstrates a significant improvement in terms of specificity, false alarm rate, and accuracy for all datasets. This indicates that the proposed normalization approach can be applied to general classification problems and can improve detection performance.

关键词： Artificial immune system dendritic cell algorithm Danger theory Signal normalization

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State awareness towards Resiliency in Cyber-Physical System A Modified Danger Theory based Deterministic dendritic cell algorithm Approach

State awareness towards Resiliency in Cyber-Physical System ...

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IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS)

作者： Lokesh, M. R. Kumaraswamy, Y. S. Sathyabama Univ Dept Comp Sci & Engn Madras Tamil Nadu India Nagarjuna Coll Engn & Technol Dept Comp Sci & Engn Devanahalli Bengaluru India

ISBN: (纸本)9781467374378

State awareness plays major role in developing resiliency in cyber-physical system where the environment is diverse in nature. Cyber-physical system is modeled with Multi Agent Paradigm and biological inspired Danger Theory based-dendritic cell algorithm Methodology is applied towards developing state awareness. The proposed methodology uses, Threshold-based agent population concentration to define each state toward indicates different state awareness on resiliency in cyber-physical system. This achieves new proposal of state awareness algorithm. The algorithm is validated with simulation results as well numerical analyses.

关键词： Resiliency State awareness Cyber-physical system Multi Agent System Artifical Immune System Danger Theory dendritic cell algorithm

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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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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

作者： Delong Cui Qinghua Zhang Jianbin Xiong Qinxue Li Mei Liu College of Computer and Electronic Information Guangdong University of Petrochemical Technology

ISBN: (纸本)9781467391054

关键词： Rotating machinery Fault diagnosis dendritic cell algorithm Dimensionless parameter Artificial immune

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Result-Controllable dendritic cell algorithm

Result-Controllable Dendritic Cell Algorithm

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10th International Conference on Intelligent Computing (ICIC)

作者： Yuan, Song Zhang, He Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9783319093338;9783319093321

To realize that the false positive rate and false negative rate can be adjusted and improve the detection accuracy of the classical dendritic cell algorithm which contains various uncertain elements, the concept of Tendency Factor and a Result-Controllable dendritic cell algorithm are proposed by analyzing the signal processing function, weight matrixes and the other random parameters involved. The new algorithm has the higher detection accuracy and better robustness, in which the Tendency Factor can be obtained according to different contexts in order to control the detection results. Simulation experiments are performed using different parameters and multiple data sets and the Tendency Factor and the Result-Controllable dendritic cell algorithm are proved to be reasonable and effective.

关键词： dendritic cell algorithm anomaly detection tendency factor artificial immune

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