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4th IEEE International Conference on E-Health and Bioengineering (EHB)

作者： Chelly, Zeineb Elouedi, Zied Inst Super Gest Tunis LARODEC Tunis Tunisia

ISBN: (纸本)9781479923724

The performance of the Fuzzy-Rough dendritic cell immune algorithm relies on its data pre-processing phase which is based on the use of Rough Set Theory (RST). However, the developed approach presents an information loss as data should be discretized beforehand. Therefore, in this paper, we propose a new data pre-processing phase for the Fuzzy-Rough hybrid approach based on Fuzzy Rough Set Theory (FRST) to allow dealing with real-valued data with no data quantization beforehand. Results show that applying FRST, instead of RST, is more convenient for the Fuzzy-Rough dendritic cell algorithm data pre-processing phase yielding much better performance in terms of accuracy.

关键词： Computational Biology dendritic cell algorithm Fuzzy Rough Sets Pre-processing

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Outbreak detection model based on danger theory

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APPLIED SOFT COMPUTING 2014年第0期24卷 612-622页

作者： Mohsin, Mohamad Farhan Mohamad Abu Bakar, Azuraliza Hamdan, Abdul Razak Univ Kebangsaan Malaysia Fac Sci & Informat Technol Ctr Artificial Intelligence Technol Data Mining & Optimizat Res Grp Bangi Selangor Malaysia

In outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory;a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm;namely the dendritic cell algorithm. To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Outbreak detection Artificial immune system Danger theory dendritic cell algorithm

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Host-based intrusion detection systems adapted from agent-based artificial immune systems

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NEUROCOMPUTING 2012年 88卷 78-86页

作者： Ou, Chung-Ming Kainga Univ Dept Informat Management Luchu 338 Taiwan

Agent-based artificial immune system (ABAIS) is adopted to intrusion detection system (IDS). An agent-based IDS (ABIDS) inspired by the danger theory of human immune system is proposed. Multiple agents are embedded to ABIDS, where agents coordinate one another to calculate mature context antigen value (MCAV) and update activation threshold for security responses. The intelligence behind ABIDS is based on the danger theory and the functionalities of dendritic cells in human immune systems, while dendritic cells agents (DC agent) are emulated for innate immune subsystem and artificial T-cell agents (TC agent) are for adaptive immune subsystem. Antigens are profiles of system calls while corresponding behaviors are regarded as signals. This ABIDS is based on the dual detections of DC agents for signals and TC agents for antigens. ABAIS is an intelligent system with learning and memory capabilities. According to MCAVs, immune response to malicious behaviors is activated by either computer host or Security Operating Center. Accordingly, computer hosts met with malicious intrusions can be effectively detected by input signals and temporary output signals such as PAMP, danger and safe signals. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Intrusion detection Artificial immune system Agent dendritic cell algorithm Danger theory

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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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Improving the reliability of real-time embedded systems using innate immune techniques

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EVOLUTIONARY INTELLIGENCE 2008年第2期1卷 113-132页

作者： Lay, Nicholas Bate, Iain Univ York Dept Comp Sci York N Yorkshire England

Previous work has shown that immune-inspired techniques have good potential for solving problems associated with the development of real- time embedded systems (RTES), where for various reasons traditional real-time development techniques are not suitable. This paper examines in more detail the general applicability of the dendritic cell algorithm (DCA) to the problem of task scheduling in RTES. To make this possible, an understanding of the problem characteristics is formalised, such that the results produced by the DCA can be examined in relation to the overall problem difficulty. The paper then contains a detailed understanding of how well the DCA which demonstrates that it generally performs well, however it clearly identifies properties of anomalies that are difficult to detect. These properties are as anticipated based on real-time scheduling theory.

关键词： Artificial immune systems dendritic cell algorithm Real-time systems Embedded systems Task scheduling

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The DCA: SOMe comparison A comparative study between two biologically inspired algorithms

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EVOLUTIONARY INTELLIGENCE 2008年第2期1卷 85-112页

作者： Greensmith, Julie Feyereisl, Jan Aickelin, Uwe Univ Nottingham Sch Comp Sci Wollaton Rd Nottingham NG8 1BB England

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a 'context aware' detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

关键词： dendritic cell algorithm Self-organizing map SYN scan detection Comparison

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SWIM: An Effective Method to Perceive Cyberspace Situation from Honeynet

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 6863-6872页

作者： Wang, Yu Guo, Yi Zhang, Liancheng Peng, Tianqiang Henan Univ Engn Coll Comp Sci Zhengzhou Henan Peoples R China Tsinghua Univ Inst Network Sci & Cyberspace Beijing Peoples R China State Key Lab Math Engn & Adv Comp Zhengzhou Henan Peoples R China

As a traditional and impactful proactive defense technology, honeynet is used by network defender to imitate normal production process, which traps and captures the behavior of attackers in order to analyze the methods and tools, and also to forecast the attacking intention and situation. However, the data obtained from honeynet usually have problems such as multiple types, high redundancy and low semantics level, which make it difficult to indicate network security situation directly. Through studying biological immune mechanism, a honeynet security warning model based on Danger Theory (an SWIM) is proposed. By utilizing the dendritic cell algorithm, this article discusses in detail the definition and mapping for input signals, capture of honeynet antigens, fundamental analysis for output signals, as well as the security early warning. The simulation results show that, in the face of typical network attacks, SWIM accurately reflects the attacking strength, which is capable of implementing the efficient network early warning.

关键词： Honeynet Danger Theory dendritic cell algorithm Network attacks Security early warning

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KEYLOGGERS SOFTWARE DETECTION TECHNIQUES 10

KEYLOGGERS SOFTWARE DETECTION TECHNIQUES

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10th International Conference on Intelligent Systems and Control (ISCO)

作者： Solairaj, A. Prabanand, S. C. Mathalairaj, J. Prathap, C. Vignesh, L. S. Nadar Saraswathi Coll Engn & Technol Theni Tamil Nadu India

ISBN: (纸本)9781467378079

Keyloggers is the action of recording the key stroke on a keyboard, typically in a covert manner. Software Keyloggers are detected based on the behavioral characteristics. They don't provide root privileges;detection is based on permission from kernel and prone to many attacks. Software Keyloggers is a software program that can be installed onto a computer, which monitors all the user activities on computer. Keyloggers steal the confidential information and they completely run in stealth mode. When Keyloggers is installed in a computer, it is not shown either in start-up icons or anywhere else on the computer that is being monitored. Software Keyloggers have posed a great threat to user privacy and security. Detection of Keyloggers is difficult because they run in hidden mode. Detection of Software Keyloggers is done using various technique namely Anti-Hook techniques, HoneyID: Spyware detection, bot detection, safe access to password protected accounts and dendritic cell algorithm. These algorithms are used to detect the existence of Keyloggers in computer, which strengthens user privacy and security.

关键词： Keylogger HoneyID Bot dendritic cell algorithm

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Modified Danger Theory based Optimized Artificial Immune Network on Resiliency in Cyber-Physical System 1

Modified Danger Theory based Optimized Artificial Immune Net...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

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 Bangalore Karnataka India

ISBN: (纸本)9781467379106

Resiliency plays major role in developing reliable and robustness in cyber-physical system where the environment is diverse in nature. The cyber-physical system is model with Multi Agent Paradigm and biological inspired Danger Theory based-modified Artificial Immune Network algorithm demonstrates on resiliency development. The propose algorithm defines resiliency as state awareness and healing process, with dendritic cells as Threshold-based agent population concentration and Artificial Recognization Ball as memory agent correspondingly. This algorithm is validated with simulation results as well numerical analyses.

关键词： Resiliency State awareness healing process Cyber-physical system Multi Agent System Artificial Immune System Danger Theory dendritic cell algorithm Artificial Immune Recognization2 algorithm

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Classifying in the Presence of Uncertainty: A DCA Perspective

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9th International Conference on Artificial Immune Systems (ICARIS 2010)

作者： Oates, Robert Kendall, Graham Garibaldi, Jonathan M. Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England

ISBN: (纸本)9783642145469

The dendritic cell algorithm is often presented as an immune-inspired one class classifier. Recently the dendritic cell algorithm has been criticised as its current decision making stage has many serious mathematical flaws which bring into question its applicability in other areas. However, previous work has demonstrated that the algorithm has properties which make it robust to a certain source of uncertainty, specifically measurement noise. This paper presents a discussion about the role of uncertainty within classification tasks and goes on to identify the strengths and weaknesses of the dendritic cell algorithm from this perspective. By examining other techniques for protecting against uncertainty, future directions for the dendritic cell algorithm are identified and discussed.

关键词： Machine Learning dendritic cell algorithm

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