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Computational Intelligence Magazine, IEEE

Issue 2 • Date May 2008

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Displaying Results 1 - 24 of 24
  • Front cover - IEEE Computational Intelligence Magazine - May 2008

    Page(s): c1
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  • Table of contents - Vol 3 No 2

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  • CI-based Cyber Security applications [Editor's Remarks]

    Page(s): 2
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    Freely Available from IEEE
  • Our continuing focus on education [President's Message]

    Page(s): 3
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  • IEEE Frank Rosenblatt Technical Field Award [Society Briefs]

    Page(s): 4 - 5
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  • IEEE Fellows-Class of 2008 [Society Briefs]

    Page(s): 5 - 9
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  • Newly elected AdCom Members-Class of 2010 [Society Briefs]

    Page(s): 9 - 11
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  • Interview with Jim Bezdek, University of West Florida, USA [Career Profile]

    Page(s): 12 - 14
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (820 KB) |  | HTML iconHTML  

    Presents an interview and profile of Jim Bezdek, the recipient of the 2007 IEEE Technical Field Award. View full abstract»

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  • 2008 IEEE World Congress on Computational Intelligence - Call for Participations

    Page(s): 17
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  • Hong Kong Chapter Report [Family Corner]

    Page(s): 18 - 20
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  • Special issue on computational intelligence in cyber security [Preface]

    Page(s): 21
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  • Immunocomputing for intelligent intrusion detection

    Page(s): 22 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1366 KB) |  | HTML iconHTML  

    Based on immunocomputing, this paper describes an approach to intrusion detection. The approach includes both low-level signal processing (feature extraction) and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization, both controlled by cytokines (messenger proteins). Such FIN can be formed from the network traffic signals using discrete tree transforms, singular value decomposition, and the proposed index of inseparability as a measure of quality of FIN. Recent results suggest that the approach outperforms (by training time and accuracy) state-of-the-art approaches of computational intelligence. View full abstract»

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  • Privacy-preserving collaborative data mining

    Page(s): 31 - 41
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2304 KB)  

    Data collection is a necessary step in data mining process. Due to privacy reasons, collecting data from different parties becomes difficult. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge. The objective of this paper is to provide solutions for privacy-preserving collaborative data mining problems. In particular, we illustrate how to conduct privacy-preserving naive Bayesian classification which is one of the data mining tasks. To measure the privacy level for privacy- preserving schemes, we propose a definition of privacy and show that our solutions preserve data privacy. View full abstract»

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  • On the use of recurrent neural networks to design symmetric ciphers

    Page(s): 42 - 53
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1033 KB) |  | HTML iconHTML  

    In this article, we describe an innovative form of cipher design based on the use of recurrent neural networks. The well-known characteristics of neural networks, such as parallel distributed structure, high computational power, ability to learn and represent knowledge as a black box, are successfully applied to cryptography. The proposed cipher has a relatively simple architecture and, by incorporating neural networks, it releases the constraint on the length of the secret key. The design of the symmetric cipher is described in detail and its security is analyzed. The cipher is robust in resisting different cryptanalysis attacks and provides efficient data integrity and authentication services. Simulation results are presented to validate the effectiveness of the proposed cipher design. View full abstract»

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  • Design of block ciphers by simple chaotic functions

    Page(s): 54 - 59
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    The relationships of chaotic functions and cryptography are investigated in this paper. 128-bit symmetric keys are derived from some chaotic function, called simple logistic function (SLF), for suitable parameters. An improved algorithm of discretization of SLF is introduced and applied to the design of block encryption ciphers and key schedules with proper parameters and initial values. These are chosen by the Lyapunov exponent method for testing chaos. Dynamic S- Boxes of such block ciphers are generated. Security analysis is also given to these S-Boxes. View full abstract»

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  • Everything that is not important: Negative databases [Research Frontier]

    Page(s): 60 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (214 KB) |  | HTML iconHTML  

    According to the self-nonself discrimination theory, from a data representation point of view, the collection of all immune cells comprises a distributed model of what self is not; if the model is good enough, then it is also a model of self - the collection of all immune cells define what self is by individually specifying what it is not. The work reviewed in this paper is predominantly inspired by this image and asks whether the same principle can be applied to a database - here viewed as a list of strings - and how the properties of representing a database negatively can be leveraged for its security. View full abstract»

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  • Integrating trust into the CyberCraft Initiative via the Trust Vectors model [Application Notes]

    Page(s): 65 - 68
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (444 KB) |  | HTML iconHTML  

    This research supports the hypothesis that the Trust Vector model can be modified to fit the CyberCraft Initiative, and that there are limits to the utility of historical data. This research proposed some modifications and expansions to the Trust Model Vector, and identified areas for future research. View full abstract»

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  • Enhancing Computer Security with Smart Technology [Book Review]

    Page(s): 70 - 71
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  • CEC 2007 Conference Report [Conference Reports]

    Page(s): 72 - 73
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  • 2007 IEEE International Conference on Granular Computing [Conference Reports]

    Page(s): 74
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  • Call for Papers - A special issue of Computational Intelligence Magazine on Computational Intelligence

    Page(s): 75
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  • Application-specific publications [Member's Inquiry]

    Page(s): 76 - 77
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  • Conference Calendar [2008-2009]

    Page(s): 78
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope