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

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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

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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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Innate immune memory and its application to artificial immune systems

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JOURNAL OF SUPERCOMPUTING 2022年第9期78卷 11680-11701页

作者： Wang, Dongmei Liang, Yiwen Dong, Hongbin Tan, Chengyu Xiao, Zhenhua Liu, Sai Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China South Cent Univ Nationalities Coll Comp Sci Wuhan 430072 Peoples R China

The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as dendritic cell algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called aeuro similar to Trained Immunity (TM) or Innate Immune Memory (IIM)(TM) that thinks innate immunity can also build immunological memory to enhance the immune system (TM) s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms (TM) adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of dendritic cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.

关键词： Innate immune memory Artificial immune systems Danger theory dendritic cell algorithm Classification

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Accelerating Cyber-Breach Investigations Through Novel Use of Artificial Immune System algorithms 42nd

Accelerating Cyber-Breach Investigations Through Novel Use o...

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42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Donnachie, Benjamin Verrall, Jason Hopgood, Adrian Wong, Patrick Kennedy, Ian Open Univ Walton Hall Milton Keynes England Univ Portsmouth Winston Churchill Ave Portsmouth England

ISBN: (纸本)9783031214400;9783031214417

The use of artificial immune systems for investigation of cyber-security breaches is presented. Manual reviews of disk images are impractical because of the size of the dataset. Machine-learning algorithms for detection of misuse require labelled training data, which are generally unavailable. They are also necessarily retrospective, so they are unlikely to detect new forms of intrusion. For those reasons, this article proposes the use of artificial immune systems for unsupervised anomaly detection. Specifically, a deterministic dendritic cell algorithm (dDCA) has been implemented that has successfully detected automated SQL injection attacks from sample disk images. For comparison, it outperformed an unsupervised k-means clustering algorithm. However, many significant anomalies were not detected, so further work is required to refine the algorithm using more extensive datasets, and to encode complementary expert knowledge.

关键词： Anomaly detection Artificial Immune Systems Cybersecurity dendritic cell algorithm Unsupervised learning

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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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A dendritic cell algorithm for Real-Time Anomaly Detection

A Dendritic Cell Algorithm for Real-Time Anomaly Detection

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2012 IEEE International Conference on Computer Science and Automation Engineering（CSAE 2012）

作者： Song Yuan 1,2 ,Qi-juan Chen 1 1.College of Power and Mechanical Engineering Wuhan University Wuhan,China 2.College of Computer Science and Technology Wuhan University of Science and Technology Wuhan,China

The analysis process of the classical dendritic cell algorithm(DCA)is performed offline,which has constrained its application in the area of anomaly *** order to continuously detect abnormal behaviors as soon as they occur, a real-time analysis algorithm is proposed,when an antigen has been presented by sufficient dendritic cells,it will be immediately output and assessed,thus the purpose of real-time or near-to real-time analysis can be *** assessments will reduce the influence of the errors,and the antigen and signal pool of temporal correlation will eliminate the mutual interference of the antigens and signals,which are far *** results of the experiments show that the real- time analysis algorithm proposed has the considerable detection accuracy.

关键词： artificial immune danger theory dendritic cell algorithm anomaly detection real-time analysis

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