主机免疫系统模型及其检测器生成算法研究
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摘要
人工免疫系统作为计算智能研究的新领域,具备强大的信息处理和问题求解能力,在计算机安全领域得到了广泛应用,基于免疫的动态防护技术克服了传统安全技术的一些弱点,具有广阔的应用前景。
本文借鉴人工免疫系统理论,结合主机系统的安全需求,提出了一个主机免疫系统模型,并给出了模型的形式化描述,设计了模型的结构框架,讨论了模型的免疫仿生机制和工作流程,分析了模型的重要特性;自适应性、动态防护性、可扩展性和鲁棒性。依此模型,构建了主机免疫系统体系结构,实现了基于Windows2000的主机免疫原型系统,该原型系统具备文件免疫、进程免疫和网络接口免疫等功能,构成主机全方位的免疫防护体系。然后,研究了主机免疫系统模型中检测器的生成方法,剖析了穷举等现有检测器生成算法,提出了一种自体变异的检测器生成算法,阐述了算法的实现过程——由保留一定数量空位的变异自体生成检测器,从理论上分析了该算法的性能和复杂性,结果表明,该算法克服了现有算法检测器生成效率低的不足。最后,对该算法进行了仿真实验,验证了理论分析的结果。
总之,本文提出的模型为全面解决主机安全问题提供了新的思路,提出的算法性能优于穷举等现有检测器生成算法,具有良好的实用价值。
As a novel branch of computational intelligence, Artificial Immune System (AIS) has strong capabilities of information processing and problem-solving paradigm. AIS have been applied broadly in the field of computer security. The dynamic defending technology based on immunity has conquered many weaknesses of traditional network security technology, and there is a wider prospect of its application.
Firstly, a host immune system model (HISM) is presented by drawing inspiration from theories of AIS and combining the host security requirements, and HISM is formally described. The framework of HISM is designed, the immune mechanism and the work flow of HISM are discussed, and several important characteristics of HISM are analyzed, such as adaptability, dynamic defending, expansibility and robustness. According to HISM, the architecture of the host immune system is constructed, the prototype immune system of the host is implemented based on Windows 2000, which composes all-round host immune defending system, and has many functions such as file immunity, process immunity, network interface immunity and so on. And then, the detector generating technique of HISM is researched, some existing detector generating algorithms are analyzed, a self-mutation detector generating algorithm (SMDGA) is presented, the implementation course of SMDGA is described, the detector is generated by the mutated self string with a number of blank symbols in SMDGA, the performance and complexity of SMDGA are analyzed theoretically, the conclusion indicate that SMDGA overcomes the low generating detector efficiency of the existing algorithms. Finally, the results of theoretical analysis are verified by the simulation of SMDGA.
In summary, a new way is introduced to solve security problems of the host system through this thesis, the proposed algorithm has a better performance than existing algorithms such as exhaustive detector generating algorithm, and it is practically valuable.
本文借鉴人工免疫系统理论,结合主机系统的安全需求,提出了一个主机免疫系统模型,并给出了模型的形式化描述,设计了模型的结构框架,讨论了模型的免疫仿生机制和工作流程,分析了模型的重要特性;自适应性、动态防护性、可扩展性和鲁棒性。依此模型,构建了主机免疫系统体系结构,实现了基于Windows2000的主机免疫原型系统,该原型系统具备文件免疫、进程免疫和网络接口免疫等功能,构成主机全方位的免疫防护体系。然后,研究了主机免疫系统模型中检测器的生成方法,剖析了穷举等现有检测器生成算法,提出了一种自体变异的检测器生成算法,阐述了算法的实现过程——由保留一定数量空位的变异自体生成检测器,从理论上分析了该算法的性能和复杂性,结果表明,该算法克服了现有算法检测器生成效率低的不足。最后,对该算法进行了仿真实验,验证了理论分析的结果。
总之,本文提出的模型为全面解决主机安全问题提供了新的思路,提出的算法性能优于穷举等现有检测器生成算法,具有良好的实用价值。
As a novel branch of computational intelligence, Artificial Immune System (AIS) has strong capabilities of information processing and problem-solving paradigm. AIS have been applied broadly in the field of computer security. The dynamic defending technology based on immunity has conquered many weaknesses of traditional network security technology, and there is a wider prospect of its application.
Firstly, a host immune system model (HISM) is presented by drawing inspiration from theories of AIS and combining the host security requirements, and HISM is formally described. The framework of HISM is designed, the immune mechanism and the work flow of HISM are discussed, and several important characteristics of HISM are analyzed, such as adaptability, dynamic defending, expansibility and robustness. According to HISM, the architecture of the host immune system is constructed, the prototype immune system of the host is implemented based on Windows 2000, which composes all-round host immune defending system, and has many functions such as file immunity, process immunity, network interface immunity and so on. And then, the detector generating technique of HISM is researched, some existing detector generating algorithms are analyzed, a self-mutation detector generating algorithm (SMDGA) is presented, the implementation course of SMDGA is described, the detector is generated by the mutated self string with a number of blank symbols in SMDGA, the performance and complexity of SMDGA are analyzed theoretically, the conclusion indicate that SMDGA overcomes the low generating detector efficiency of the existing algorithms. Finally, the results of theoretical analysis are verified by the simulation of SMDGA.
In summary, a new way is introduced to solve security problems of the host system through this thesis, the proposed algorithm has a better performance than existing algorithms such as exhaustive detector generating algorithm, and it is practically valuable.
引文
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[3]Timmis J,Neal M and Hunt J.Artificial immune system for data analysis[J].Biosystems,2000,55(1-3);143-150.
[4]丁永生,任立红.人工免疫系统;理论与应用.模式识别与人工智能[J].2000,13(1);52-59.
[5]洪征,吴礼发等.AIS在计算机安全领域的应用与展望[J].解放军理工大学(自然科学版),2005,6(6);531-536.
[6]王磊,潘进,焦李成.免疫规划[J].计算机学报,2000,23(8);806-812.
[7]肖人彬,王磊.人工免疫系统;原理、模型、分析及展望[J].计算机学报,2002,25(12);1281-1293.
[8]焦李成,杜海峰.人工免疫系统进展与展望[J].电子学报,2003,31(10);1540-1548.
[9]Jerne N K.Towards a Network Theory of the Immune System[J].Annual Immunology,1974,125(c);373-389.
[10]Hoffmann G M.A Neural Network Model Based on the Analogy with the Immune System[J].Theory Biology,1986,122(3);33-67.
[11]靳蕃,范俊波等.神经网络与神经计算机[M].成都;西南交通大学出版社,1991.
[12]S.Forrest,A.S.Perelson,L.Allen,and C.R.Cheru R.Kuri.Self-Nonself Discrimination in a Computer[A].In;Proceedings of IEEE Symposium on Research in Security and Privacy[C].Los Alamitos;CA,1994;202-212.
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[14]A Hofmeyr,S.Forrest,Architecture for an Artificial Immune System[J].Evolutionary Computation,2000,8(4);443-473.
[15]T.Okamoto and Y.Ishida.A Distributed Approach against Computer Viruses Inspired by the Immune System[J].IEEE Transactions on Communication.2000,83(5);908-915.
[16]R.S.Jensen.Immune system for virus detection and elimination[D].Denmark;Technical University of Denmark,2002.
[17]P.K.harmer,G.B.Lanmont.An Agent Based Architecture for a Computer Virus Immune System[A].In;GECCO 2000[C].Las Vegas,Nevada,USA,1999.
[18]宋程,基于计算机免疫的病毒检测研究[D].成都;四川大学硕士学位论文,2005.
[19]Kephart J O,Sorkin G B,Swimmer M.Immune System for Cyberspace[A].In;Proceeding 1997 IEEE International Conference on systems Man and Cybernetics[C].Orlando,USA,1997;879-884.
[20]Hofmeyr S,Forrest S,Somayaji A.Intrusion detection using sequences of system calls[J].Journal of Computer Security,1998,6(3);151-180.
[21]Jungwon Kim,Bentley P J.Towards an artificial immune system for network intrusion detection;an investigation of clonal selection with a negative selection operator[A].In;Congress on Evolutionary Computation(CEC-2001)[C].Seoul,Korea,2001;1244-1252.
[22]Paul D.Williams,Kevin P.Anchor,John L.Bebo,Gregg H.Gunsch,Gray D.Lamout.CDIS;Towards a Computer Immune System for Detecting Network Intrusions[A].In;Proceedings 4th International Symposium[C].Las Davis;CA,2001;117-133.
[23]罗文坚.面向入侵检测的人工免疫模型和算法研究[D].合肥;中国科学技术大学博士学位论文,2003.
[24]P.D haeseleer,S.Forrest,P.Helman,An Immunological Approach to Change Detection;Algorithms,Analysis and Implications[A].In;Proceeding the 1996 IEEE Symposium on Security and Privacy[C].Las Alamitos;CA,1996;110-119.
[25]M.Ayara,J.Timmis,L.N.de Lemos,R.de Castro,R.Duncan.Negative Selection;How to Generate Detectors[A].In;Proceedings of 1st International Conference on Artificial Immune Systems(ICARIS-2002)[C].UK;Springer-Verlag,2002;89-98.
[26]张衡,吴礼发等.一种r可变否定选择算法及其仿真分析[J].计算机学报,2005,28(10);1614-1619.
[27]罗文坚,曹先彬等.检测器自适应生成算法[J].自动化学报,2005,31(6);907-916.
[28]程永新,许家珆.一种新型检测器生成算法模型[A].见;第十五届中国神经网络大会(CNNC2007)暨首届中国人工免疫系统专题会议(CAISC06)[C].哈尔滨;哈尔滨工程大学学报杂志社,2006;252-256.
[29]刘国英,陈蔼祥等.一种新的免疫入侵检测器生成算法[J].计算机技术与发展,2006,16(5);128-130.
[30]莫宏伟.人工免疫系统原理与应用[M].哈尔滨;哈尔滨工业大学出版社,2002.
[31]朱锡华.生命的卫士——免疫系统[M].北京;科学技术文献出版社,1999.
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