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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Greensmith, Julie Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9781450334723

The Internet of Things is a network of 'smart' objects, transforming everyday objects into entities which can measure, sense and understand their environment. The devices are uniquely identifiable, rely on near field connectivity, often in embedded devices. The Internet of Things is designed to be deployed without human intervention or interaction. One application is the 'smart house', with components including household appliances, networked with the user able to control devices remotely. However, the security inherent in these systems is added as somewhat of an afterthought. One hypothetical scenario is where a malicious party could exploit this technology with potentially disastrous consequences, turning on a cooker remotely leading to digital arson. Reliance on standard methods is insufficient to provide the user with adequate levels of security, an area where AIS may be extremely useful. There are currently limitations with AIS applied in security, focussing on detection without providing automatic responses. This problem provides an opportunity to advance AIS in providing both an ideal scenario for testing their real-world application and to develop novel responsive AIS. A responsive version of the deterministic dendritic cell algorithm will be proposed to demonstrate how responsive AIS will need to be developed to meet these future challenges through proposing the incorporation of a model of T-cell responses.

关键词： Artificial Immune Systems Internet of Things Automated Responses Security dendritic cell algorithm

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An Efficient Botnet Detection System for P2P Botnet

An Efficient Botnet Detection System for P2P Botnet

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International Conference on Wireless Communications, Signal Processing and Networking

作者： M. Thangapandiyan P. M. Rubesh Anand Department of Electronics and Communication Engineering Hindustan University Chennai India

ISBN: (纸本)9781467393393

Peer-to-Peer (P2P) botnets are exploited by the botmasters for their resiliency against the take down efforts. As the modern botnets are stealthier, the traditional botnet detection approaches are not suitable for the botnet detection. In this paper, an efficient botnet detection system is proposed for detecting the P2P botnet. The proposed botnet detection system estimates the flow export using NetFlow protocol. The packet flow is analyzed using three main components namely, Exporter, Collector, and Analyzer. The exporter captures the packet and monitors the contents of the packet. The collector captures the flow traffic and the analyzer component initiates an automated analysis of traffic with the captured packet information. The packet flow information is collected by virtual interface and physical probe. The virtual interface is used for collecting the malicious traffic information between the Virtual Machines (VMs) and the physical probe gathers malicious traffic information between the network bridges connecting VMs. The information collected from these techniques are analyzed for detecting the botnets in inter VM and intra VM. Compared to the existing dendritic cell algorithm (DCA), the proposed VM based botnet detection system has minimal time consumption, increased detection speed, and higher attack prevention ratio.

关键词： P2P botnet Virtual Machine dendritic cell algorithm NetFlow Protocol Intrusion Detection

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The Designation of Bio-Inspired Intrusion Detection System Model in Cloud Computing Based on Machine Learning

The Designation of Bio-Inspired Intrusion Detection System M...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Yufei Ge Hong Liang Lin Chen Qian Zhang College of Computer and Communication Engineering China University of Petroleum

ISBN: (纸本)9781510803084

The paper proposed a new model by applying bio-inspired theory into intrusion detection system in cloud computing. dendritic cell algorithm(DCA) based on Danger Theory get involved in the model designation in allusion to the existing problem, high false negative rate, low detection efficiency, low real-time process, and lack of adaptive of the unknown aggressive behavior for nowadays detection mechanisms. Combing the pertinent knowledge of machine learning theory, then to design one improved bio-inspired intrusion detection system model in cloud computing based on machine learning. Finally we introduce our system module as well as the detailed work processes.

关键词： Intrusion Detection System Bio-Inspired dendritic cell algorithm Cloud Computing Model Designation

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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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Further Exploration of the Hybrid Fuzzy-Rough dendritic cell Immune Classifier

Further Exploration of the Hybrid Fuzzy-Rough Dendritic Cell...

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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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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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Intrusion Detection Systems Adapted from Agent-based Artificial Immune Systems

Intrusion Detection Systems Adapted from Agent-based Artific...

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IEEE International Conference on Fuzzy Systems (FUZZ 2011)

作者： Ou, Chung-Ming Wang, Yao-Tien Ou, C. R. Kainan Univ Dept Informat Management Luchu 33857 Taiwan Hungkuang Univ Dept Comp Sci & Informat Engn Taichung Taiwan Hsiuping Inst Technol Dept Elect Engn Taichung Taiwan

ISBN: (纸本)9781424473175

Agent-based artificial immune system (ABAIS) is applied to intrusion detection systems (IDS). A multiagent-based IDS (ABIDS) inspired by the danger theory of human immune system is proposed. The intelligence behind ABIDS is based on the functionality of dendritic cells in human immune systems and the danger theory, while dentritic 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 agent for signals and TC agent for antigen, where each agent coordinates with other to calculate danger value (DV). ABAIS is an intelligent system with learning and memory capabilities. According to DVs, immune response for malicious behaviors is activated by either computer host or Security Operating Center (SOC). 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. Accordingly, computer hosts met with malicious intrusions can be effectively detected via input signals and temporary output signals such as PAMP, danger and safe signals.

关键词： intrusion detection multiagent artificial immune systems dendritic cell 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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A Sense of 'Danger' for Windows Processes

A Sense of 'Danger' for Windows Processes

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8th International Conference on Artificial Immune Systems

作者： Manzoor, Salman Shafiq, M. Zubair Tabish, S. Momina Farooq, Muddassar NUCES FAST Next Generat Intelligent Networks Res Ctr NexGIN Islamabad 44000 Pakistan

ISBN: (纸本)9783642032455

The sophistication of modern computer malware demands run-time malware detection strategies which are not only efficient but also robust to obfuscation and evasion attempts. In this paper;we investigate the suitability of recently proposed dendritic cell algorithms (DCA), both classical DCA (cDCA) and deterministic DCA (dDCA), for malware detection at run-time. We have collected API call traces of real malware and benign processes running on Windows operating system. We evaluate the accuracy of cDCA and dDCA for classifying between malware and benign processes using API call sequences. Moreover, we also study the effects of antigen multiplier and time-windows on the detection accuracy of both algorithms.

关键词： API Call Sequence Artificial Immune System dendritic cell algorithm Malware Detection

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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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