The Promise and Challenges of Artificial Immune System Based Web Usage Mining : Preliminary Results

The immune system can be viewed as a complex network structure that is able to respond to an almost unlimited multitude of foreign invaders such as viruses and bacteria. Hence, this parallel and distributed adaptive system promises tremendous potential in many intelligent computing applications, including Web mining. Some of these immunitybased techniques involve the development and analysis of algorithms that can identify patterns in observed data in order to make predictions about unseen data. In this paper, we introduce several new enhancements to deal with some of the weaknesses of previous artificial immune system models. Then, we present a framework for mining typical user profiles from server access logs based on ideas inspired from the natural immune system. An artificial immune system mimicking the body’s adaptive learning and defense mechanism in the face of invading biological agents is used as a monitoring and learning system for a Web site in the face of all incoming Web requests. The immune system approach to detecting different user access patterns can be expected to borrow its strength from its nature inspired origins. Like the natural immune system, its strongest advantage compared to current approaches is expected to be its ease of adaptation to the changing / dynamic environment that characterizes the World Wide Web. Also, our immune system inspired approach can be used for different applications ranging from profiling for personalization applications, to dynamic defense mechanism for security maintenance on sensitive ecommerce and general Web sites. A real web site is used as a testbed for the proposed approach.

[1]  Dipankar Dasgupta,et al.  Artificial neural networks and artificial immune systems: similarities and differences , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  H. Abbass,et al.  aiNet : An Artificial Immune Network for Data Analysis , 2022 .

[3]  Dipankar Dasgupta,et al.  Novelty detection in time series data using ideas from immunology , 1996 .

[4]  Maurice D. Mulvenna,et al.  Discovering Internet marketing intelligence through online analytical web usage mining , 1998, SGMD.

[5]  Anupam Joshi,et al.  Mining web access logs using a fuzzy relational clustering algorithm based on a robust estimator , 1999, WWW 1999.

[6]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[7]  Jiawei Han,et al.  WebML: Querying the World-Wide Web for Resources and Knowledge , 1998, Workshop on Web Information and Data Management.

[8]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[9]  D. Dasgupta,et al.  Immunity-based systems: a survey , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[10]  Jaideep Srivastava,et al.  Web Mining: Pattern Discovery from World Wide Web Transactions , 1996 .

[11]  Alan S. Perelson,et al.  Using Genetic Algorithms to Explore Pattern Recognition in the Immune System , 1993, Evolutionary Computation.

[12]  Oren Etzioni,et al.  Adaptive Web sites , 2000, CACM.

[13]  Alberto O. Mendelzon,et al.  WebOQL: restructuring documents, databases, and webs , 1999 .

[14]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[15]  M. Eaman.  Immune system. , 2000, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[16]  Anupam Joshi,et al.  Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering , 2000, Int. J. Artif. Intell. Tools.

[17]  Olfa Nasraoui,et al.  One Step Evolutionary Mining of Context Sensitive Associations and Web Navigation Patterns , 2002, SDM.

[18]  J Timmis,et al.  An artificial immune system for data analysis. , 2000, Bio Systems.

[19]  Dipankar Dasgupta,et al.  An Immunogenetic Approach to Spectra Recognition , 1999, GECCO.

[20]  Anupam,et al.  Mining Web Access Logs Using Relational Competitive Fuzzy Clustering , 1999 .

[21]  Oren Etzioni,et al.  Adaptive Web Sites: an AI Challenge , 1997, IJCAI.

[22]  Jon Timmis,et al.  Jisys: The Envelopment of an Artificial Immune System for Real World Applications , 1999 .

[23]  D. Dasgupta,et al.  A formal model of an artificial immune system. , 2000, Bio Systems.

[24]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[25]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.