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

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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Theoretical formulation and analysis of the deterministic dendritic cell algorithm

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BIOSYSTEMS 2013年第2期111卷 127-135页

作者： Gu, Feng Greensmith, Julie Aickelin, Uwe Univ Leeds Sch Comp Leeds LS2 9JT W Yorkshire England Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

As one of the emerging algorithms in the field of artificial immune systems (AIS), the dendritic cell algorithm (DCA) has been successfully applied to a number of challenging real-world problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have mainly focused on its empirical aspects. Therefore, it is necessary to provide a formal definition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we define the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions. Runtime analyses of the standard algorithm and the one with additional segmentation are performed. Our analysis suggests that the standard dDCA has a runtime complexity of O(n(2)) for the worst-case scenario, where n is the number of input data instances. The introduction of segmentation changes the algorithm's worst case runtime complexity to O(max(nN, nz)), for DC population size N with size of each segment z. Finally, two runtime variables of the algorithm are formulated based on the input data, to understand its runtime behaviour as guidelines for further development. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： Artificial immune systems dendritic cell algorithm Runtime analysis Formulation and formalisation

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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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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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Method of Locating Anomaly Source in Software System Based on dendritic cell algorithm

Method of Locating Anomaly Source in Software System Based o...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Liu Sai Ke Jie Wuhan Univ Comp Sch Wuhan 430072 Peoples R China

ISBN: (纸本)9783038351153

The method of anomaly detection in traditional software system cannot locate anomaly or find the lack of abnormal source accurately and timely. With regard to this deficiency, this paper presents an improved algorithm based on biological immune dendritic cell algorithm. This method aims to modify PAMP signal to achieve the purpose of locating anomaly source. It proves not only applicable to the real-time detection, but also to locate the anomaly source and processing, which further improves the accuracy of anomaly detection.

关键词： dendritic cell algorithm software system anomaly detection anomaly source

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Denial of Service Attack Detection using dendritic cell algorithm 8

Denial of Service Attack Detection using Dendritic Cell Algo...

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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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Using dendritic cell algorithm to Detect the Resource Consumption Attack over MANET

Using Dendritic Cell Algorithm to Detect the Resource Consum...

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2nd International Conference on Software Engineering and Computing Systems (ICSECS 2011)

作者： Abdelhaq, Maha Hassan, Rosilah Alsaqour, Raed Univ Kebangsaan Malaysia Sch Comp Sci Fac Informat Sci & Technol Bangi 43600 Selangor Darul Malaysia

ISBN: (纸本)9783642222023

Artificial Immune Systems (AISs) and Mobile Ad Hoc Networks (MANETs) are two up to date attractive technologies. AIS is utilized to introduce efficient intrusion detection algorithms to secure both host based and network based systems, whilst MANET is defined as a collection of mobile, decentralized, and self organized nodes. Securing MANET is a problem which adds more challenges on the research. This is because MANET properties make it harder to be secured than the other types of static networks. We claim that AIS properties as robust, self-healing, and self-organizing system can meet the challenges of securing MANET environment. This paper objective is to utilize the benefits of one of the Danger Theory based AIS intrusion detection algorithms, namely the dendritic cell algorithm (DCA) to detect a type of Denial of Service (DoS) attack called Resource Consumption Attack (RCA) and also called sleep deprivation attack over MANET. The paper introduces a Mobile dendritic cell algorithm (MDCA) architecture in which DCA plugged to be applied by each MANET node.

关键词： Mobile Ad Hoc Networks Denial of Service attack Resource Consumption Attack Danger Theory Artificial Immune System 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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Migration Threshold Tuning in the Deterministic dendritic cell algorithm 8th

Migration Threshold Tuning in the Deterministic Dendritic Ce...

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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

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