Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing)

probabilistic Modelling In Bioinformatics And Medical Informatics Has Been Written For Researchers And Students In Statistics, Machine Learning, And The Biological Sciences. The First Part Of This Book Provides A Self-contained Introduction To The Methodology Of Bayesian Networks. The Following Parts Demonstrate How These Methods Are Applied In Bioinformatics And Medical Informatics. All Three Fields - The Methodology Of Probabilistic Modeling, Bioinformatics, And Medical Informatics - Are Evolving Very Quickly. The Text Should Therefore Be Seen As An Introduction, Offering Both Elementary Tutorials As Well As More Advanced Applications And Case Studies. Data Mining And Knowledge Discovery (dmkd) Is A Rapidly Expanding Field In Computer Science. It Has Become Very Important Because Of An Increased Demand For Methodologies And Tools That Can Help The Analysis And Understanding Of Huge Amounts Of Data Generated On A Daily Basis By Institutions Like Hospitals, Research Laboratories, Banks, Insurance Companies, And Retail Stores And By Internet Users. This Explosion Is A Result Of The Growing Use Of Electronic Media. But What Is Data Mining (dm)? A Web Search Using The Google Search Engine Retrieves Many (really Many) Definitions Of Data Mining. We Include Here A Few Interesting Ones. One Of The Simpler Definitions Is: “as The Term Suggests, Data Mining Is The Analysis Of Data To Establish Relationships And Identify Patterns” [1]. It Focuses On Identifying Relations In Data. Our Next Example Is More Elaborate: An Information Extraction Activity Whose Goal Is To Discover Hidden Facts Contained In Databases. Using A Combination Of Machine Learning, Statistical Analysis, Modeling Techniques And Database Technology, Data Mining Finds Patterns And Subtle Relationships In Data And Infers Rules That Allow The Prediction Of Future Results. Typical Applications Include Market Segmentation, Customer Profiling, Fraud Detection, Evaluation Of Retail Promotions, And Credit Risk Analysis [2]. Trends In Data Mining And Knowledge Discovery -- Advanced Methods For The Analysis Of Semiconductor Manufacturing Process Data -- Clustering And Visualization Of Retail Market Baskets -- Segmentation Of Continuous Data Streams Based On A Change Detection Methodology -- Instance Selection Using Evolutionary Algorithms: An Experimental Study -- Using Cooperative Coevolution For Data Mining Of Bayesian Networks -- Knowledge Discovery And Data Mining In Medicine -- Satellite Image Classification Using Cascaded Architecture Of Neural Fuzzy Network -- Discovery Of Positive And Negative Rules From Medical Databases Based On Rough Sets. Edited By Nikhil R. Pal, Lakhmi Jain. The Internet began as a private network connecting government, military, and academic researchers. As such, there was little need for secure protocols, encrypted packets, and hardened servers. When the creation of the World Wide Web unexpectedly ushered in the age of the commercial Internet, the network's size and subsequent rapid expansion made it impossible retroactively to apply secure mechanisms. The Internet's architects never coined terms such as spam, phishing, zombies, and spyware, but they are terms and phenomena we now encounter constantly. Programming detectors for such threats has proven difficult. Put simply, there is too much information---too many protocols, too many layers, too many applications, and too many uses of these applications---for anyone to make sufficient sense of it all. Ironically, given this wealth of information, there is also too little information about what is important for detecting attacks. Methods of machine learning and data mining can help build better detectors from massive amounts of complex data. Such methods can also help discover the information required to build more secure systems. For some problems in computer security, one can directly apply machine learning and data mining techniques. Other problems, both current and future, require new approaches, methods, and algorithms. This book presents research conducted in academia and industry on methods and applications of machine learning and data mining for problems in computer security and will be of interest to researchers and practitioners, as well students. ‘Dr. Maloof not only did a masterful job of focusing the book on a critical area that was in dire need of research, but he also strategically picked papers that complemented each other in a productive manner. ... This book is a must read for anyone interested in how research can improve computer security.’ Dr Eric Cole, Computer Security Expert This work summarizes the theoretical and algorithmic basis of optimized pr- abilistic advising. It developed from a series of targeted research projects s- ported both by the European Commission and Czech grant bodies. The source text has served as a common basis of communication for the research team. When accumulating and re?ning the material we found that the text could also serve as • a grand example of the strength of dynamic Bayesian decision making, • a practical demonstration that computational aspects do matter, • a reference to ready particular solutions in learning and optimization of decision-making strategies, • a source of open and challenging problems for postgraduate students, young as well as experienced researchers, • a departure point for a further systematic development of advanced op- mized advisory systems, for instance, in multiple participant setting. These observations have inspired us to prepare this book. Prague, Czech Republic Miroslav K ́ arn ́ y October 2004 Josef B ̈ ohm Tatiana V. Guy Ladislav Jirsa Ivan Nagy Petr Nedoma Ludv ́?k Tesa? r Contents 1 Introduction............................................... 1 1. 1 Motivation............................................. 1 1. 2 State of the art......................................... 3 1. 2. 1 Operator supports................................. 3 1. 2. 2 Mainstream multivariate techniques................. 4 1. 2. 3 Probabilistic dynamic optimized decision-making...... 6 1. 3 Developed advising and its role in computer support......... 6 1. 4 Presentation style, readership andlayout................... 7 1. 5 Acknowledgements...................................... 10 2 Underlying theory......................................... 11 2. 1 General conventions..................................... 11 2. 2 Basic notions and notations............................... The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit, therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters Advanced Methods for Knowledge Discovery from Complex Data brings together research articles by active practitioners and leading researchers reporting recent advances in the field of knowledge discovery, where the information is mined from complex data, such as unstructured text from the world-wide web, databases naturally represented as graphs and trees, geoscientific data from satellites and visual images, multimedia data and bioinformatics data. An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining, including descriptions of some currently popular tools like genetic algorithms, neural networks and case-based reasoning. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks. With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, as well as recent working applications this book provides a representative selection of the available methods and their evaluation in real domains. It will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field. A website supports the book: http://www.cse.uta.edu/amkdcd.

nowadays, A Significant Number Of Applications Require The Organization Of Data Elements Which Contain At Least One Spatial Attribute. Space Support In Databases Poses New Challenges In Every Part Of A Database Management System And The Capability Of Spatial Support In The Physical Layer Is Considered Very Important. This Has Led To The Design Of Spatial Access Methods To Enable The Effective And Efficient Management Of Spatial Objects.

r-trees Have A Simplicity Of Structure And, Together With Their Resemblance To The B-tree, Allow Developers To Incorporate Them Easily Into Existing Database Management Systems For The Support Of Spatial Query Processing.

this Book Provides An Extensive Survey Of The R-tree Evolution, Studying The Applicability Of The Structure And Its Variations To Efficient Query Processing, Accurate Proposed Cost Models, And Implementation Issues Like Concurrency Control And Parallelism. Based On The Observation That ``space Is Everywhere, The Authors Anticipate That We Are In The Beginning Of The Era Of The ``ubiquitous R-tree Analogous To The Way B-trees Were Considered 25 Years Ago. Written For Database Researchers, Designers And Programmers As Well As Graduate Students, This Comprehensive Monograph Will Be A Welcome Addition To The Field.

the Book Successfully Integrates Research Results Of The Last 20 Years, In A Clear And Highly Readable Manner. It Is The First Book Dedicated To R-trees And Related Access Methods, And I Believe It Will Be Valuable As A Reference To Everyone Interested In The Area.

prof. Timos Sellis, National Technical University Of Athens

Nowadays, a significant number of applications require the organization of data elements which contain at least one spatial attribute. Space support in databases poses new challenges in every part of a database management system and the capability of spatial support in the physical layer is considered very important. This has led to the design of spatial access methods to enable the effective and efficient management of spatial objects. R-trees have a simplicity of structure and, together with their resemblance to the B-tree, allow developers to incorporate them easily into existing database management systems for the support of spatial query processing. This book provides an extensive survey of the R-tree evolution, studying the applicability of the structure and its variations to efficient query processing, accurate proposed cost models, and implementation issues like concurrency control and parallelism. Based on the observation that ``space is everywhere", the authors anticipate that we are in the beginning of the era of the ``ubiquitous R-tree" analogous to the way B-trees were considered 25 years ago. Written for database researchers, designers and programmers as well as graduate students, this comprehensive monograph will be a welcome addition to the field. The book successfully integrates research results of the last 20 years, in a clear and highly readable manner. It is the first book dedicated to R-trees and related access methods, and I believe it will be valuable as a reference to everyone interested in the area. Prof. Timos Sellis, National Technical University of Athens

cognitive Engineering: A Distributed Approach To Machine Intelligence Explores The Design Issues Of Intelligent Engineering Systems. Beginning With The Foundations Of Psychological Modeling Of The Human Mind, The Main Emphasis Is Given To Parallel And Distributed Realization Of Intelligent Models For Application In Reasoning, Learning, Planning And Multi-agent Co-ordination Problems. The Last Two Chapters Provide Case Studies On Human-mood Detection And Control, And Behavioral Co-operation Of Mobile Robots. This Is The First Comprehensive Text Of Its Kind, Bridging The Gap Between Cognitive Science And Cognitive Systems Engineering.

each Chapter Includes Plenty Of Numerical Examples And Exercises With Sufficient Hints, So That The Reader Can Solve The Exercises On Their Own. Computer Simulations Are Also Included In Most Chapters To Give A Clear Idea About The Application Of The Algorithms Undertaken In The Book. In Addition, Mathematical Analysis On Convergence And Stability Of The Neuro-fuzzy Models Will Enable The Reader To Pursue Their Research Career In Cognitive Engineering.

cognitive Engineering: A Distributed Approach To Machine Intelligence Is Unique In Its Theme And Contents, And Includes A Foreword By Professor Witold Pedrycz - Written With Graduates In Mind, This Book Would Also Be A Valuable Resource For Researchers In The Fields Of Cognitive Science, Computer Science And Cognitive Engineering.

Cognitive Engineering: A Distributed Approach to Machine Intelligence explores the design issues of intelligent engineering systems. Beginning with the foundations of psychological modeling of the human mind, the main emphasis is given to parallel and distributed realization of intelligent models for application in reasoning, learning, planning and multi-agent co-ordination problems. The last two chapters provide case studies on human-mood detection and control, and behavioral co-operation of mobile robots. This is the first comprehensive text of its kind, bridging the gap between Cognitive Science and Cognitive Systems Engineering. Each chapter includes plenty of numerical examples and exercises with sufficient hints, so that the reader can solve the exercises on their own. Computer simulations are also included in most chapters to give a clear idea about the application of the algorithms undertaken in the book. In addition, mathematical analysis on convergence and stability of the neuro-fuzzy models will enable the reader to pursue their research career in cognitive engineering. Cognitive Engineering: A Distributed Approach to Machine Intelligence is unique in its theme and contents, and includes a Foreword by Professor Witold Pedrycz - written with graduates in mind, this book would also be a valuable resource for researchers in the fields of Cognitive Science, Computer Science and Cognitive Engineering. What we profoundly witness these days is a growing number of human-centric systems and a genuine interest in a comprehensive understanding of their underlying paradigms and the development of solid and efficient design practices. We are indeed in the midst of the next information revolution, which very likely brings us into a completely new world of ubiquitous and invisible computing, Ambient Intelligent (AMI), and wearable hardware. This requires a totally new way of thinking in which cognitive aspects of design, cognitive system engineering and distributed approach play a pivotal role. This book fully addresses these timely needs by filling a gap between the two well-established disciplines of cognitive sciences and cognitive systems engineering. As we put succinctly in the preface, with the psychological perspective of human cognition in mind, “the book explores the computational models of reasoning, learning, planning and multi-agent coordination and control of the human moods”. This is an excellent, up to the point description of the book. The treatise is focused on the underlying fundamentals, spans across a vast territory embracing logic perspectives of human cognition, distributed models, parallel computing, expert systems, and intelligent robotics.

evolutionary Multiobjective Optimization Is A Rare Collection Of The Latest State-of-the-art Theoretical Research, Design Challenges And Applications In The Field Of Multiobjective Optimization Paradigms Using Evolutionary Algorithms. It Includes Two Introductory Chapters Giving All The Fundamental Definitions, Several Complex Test Functions And A Practical Problem Involving The Multiobjective Optimization Of Space Structures Under Static And Seismic Loading Conditions Used To Illustrate The Various Multiobjective Optimization Concepts.

important Features Include:

• detailed Overview Of All The Multiobjective Optimization Paradigms Using Evolutionary Algorithms


• excellent Coverage Of Timely, Advanced Multiobjective Optimization Topics


• state-of-the-art Theoretical Research And Application Developments


• chapters Authored By Pioneers In The Field

academics And Industrial Scientists As Well As Engineers Engaged In Research, Development And Application Of Evolutionary Algorithm Based Multiobjective Optimization Will Find The Comprehensive Coverage Of This Book Invaluable.

Evolutionary Multiobjective Optimization is a rare collection of the latest state-of-the-art theoretical research, design challenges and applications in the field of multiobjective optimization paradigms using evolutionary algorithms. It includes two introductory chapters giving all the fundamental definitions, several complex test functions and a practical problem involving the multiobjective optimization of space structures under static and seismic loading conditions used to illustrate the various multiobjective optimization concepts. Important features include: Detailed overview of all the multiobjective optimization paradigms using evolutionary algorithms Excellent coverage of timely, advanced multiobjective optimization topics State-of-the-art theoretical research and application developments Chapters authored by pioneers in the field Academics and industrial scientists as well as engineers engaged in research, development and application of evolutionary algorithm based Multiobjective Optimization will find the comprehensive coverage of this book invaluable. Written by one of the world’s leading groups in the area of Bayesian identification, control and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, Optimized Bayesian Dynamic Advising comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization. The proposed non-standard problem formulation and its solution mark a significant contribution to the design of anthropocentric automation systems. Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making.

this Book Brings Together Research Articles By Active Practitioners And Leading Researchers Reporting Recent Advances In The Field Of Knowledge Discovery.

an Overview Of The Field, Looking At The Issues And Challenges Involved Is Followed By Coverage Of Recent Trends In Data Mining. This Provides The Context For The Subsequent Chapters On Methods And Applications. Part I Is Devoted To The Foundations Of Mining Different Types Of Complex Data Like Trees, Graphs, Links And Sequences. A Knowledge Discovery Approach Based On Problem Decomposition Is Also Described. Part Ii Presents Important Applications Of Advanced Mining Techniques To Data In Unconventional And Complex Domains, Such As Life Sciences, World-wide Web, Image Databases, Cyber Security And Sensor Networks.

with A Good Balance Of Introductory Material On The Knowledge Discovery Process, Advanced Issues And State-of-the-art Tools And Techniques, This Book Will Be Useful To Students At Masters And Phd Level In Computer Science, As Well As Practitioners In The Field.

Written by one of the world’s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising.

Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization.

Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers, and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making. A CD contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the most important and complex areas of the material presented.

Machine Learning and Data Mining for Computer Security provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security.

The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables.

This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables. This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security. "The information explosion has necessitated the development of intelligent tools for extracting useful knowledge from data. This book presents research on some of the most recent advances in data mining and knowledge discovery, and provides the theory as well as its applications on practical real world problems. In addition, the methodologies discussed encompass tools like Bayesian networks as well as major facets of computational intelligence paradigms such as neural networks, evolutionary computing, neuro-fuzzy computing and rough sets." "Advanced Techniques in Data Mining and Knowledge Discovery presents both practical detail and some of the most up-to-date theory in the field, which would be useful for postgraduate students, researchers, application engineers and professors who wish to develop applications using advanced data mining and knowledge discovery techniques."--Jacket Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

multiobjective Evolutionary Algorithms And Applications Provides Comprehensive Treatment On The Design Of Multiobjective Evolutionary Algorithms And Their Applications In Domains Covering Areas Such As Control And Scheduling. Emphasizing Both The Theoretical Developments And The Practical Implementation Of Multiobjective Evolutionary Algorithms, A Profound Mathematical Knowledge Is Not Required.

written For A Wide Readership, Engineers, Researchers, Senior Undergraduates And Graduate Students Interested In The Field Of Evolutionary Algorithms And Multiobjective Optimization With Some Basic Knowledge Of Evolutionary Computation Will Find This Book A Useful Addition To Their Book Case.

"Multiobjective Evolutionary Algorithms and Applications provides comprehensive treatment on the design of multiobjective evolutionary algorithms and their applications in domains covering areas such as control and scheduling. Emphasizing both the theoretical developments and the practical implementation of multiobjective evolutionary algorithms, a profound mathematical knowledge is not required." "Written for a wide readership, engineers, researchers, senior undergraduates and graduate students interested in the field of evolutionary computation and multiobjective optimization with some basic knowledge of evolutionary algorithms will find this book a useful addition to their book case."--Jacket Multiobjective Evolutionary Algorithms and Applications provides comprehensive treatment on the design of multiobjective evolutionary algorithms and their applications in domains covering areas such as control and scheduling. Emphasizing both the theoretical developments and the practical implementation of multiobjective evolutionary algorithms, a profound mathematical knowledge is not required. Written for a wide readership, engineers, researchers, senior undergraduates and graduate students interested in the field of evolutionary algorithms and multiobjective optimization with some basic knowledge of evolutionary computation will find this book a useful addition to their book case. "Cognitive Engineering: A Distributed Approach to Machine Intelligence explores the design issues of intelligent engineering systems. Beginning with the foundations of psychological modeling of the human mind, the main emphasis is given to parallel and distributed realization of intelligent models for application in reasoning, learning, planning and multi-agent co-ordination problems. The last two chapters provide case studies on human-mood detection and control, and behavioral co-operation of mobile robots. This is the first comprehensive text of its kind, bridging the gap between Cognitive Science and Cognitive Systems Engineering."--Jacket "Probabilistic Modeling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, informatics, and the biological sciences with Part 1 providing a self-contained introduction to Bayesian networks, neural computation and probabilistic inference, and Parts 2 & 3 demonstrating how these methods are applied in bioinformatics and medical informatics. All three fields are evolving rapidly and this book will be a welcome addition to the field."--Résumé de l'éditeur "Probabilistic Modeling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, informatics, and the biological sciences with Part 1 providing a self-contained introduction to Bayesian networks, neural computation and probabilistic inference, and Parts 2 & 3 demonstrating how these methods are applied in bioinformatics and medical informatics. All three fields are evolving rapidly and this book will be a welcome addition to the field."--Jacket "With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, as well as recent working applications this book provides a representative selection of the available methods and their evaluation in real domains. It will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field."--Jacket Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. This monograph is suitable as a secondary text for graduate level computational intelligence courses, and as a reference for researchers, lecturers, and practitioners in industry "Academics and industrial scientists as well as engineers engaged in research, development and application of evolutionary algorithm based Multiobjective Optimization will find the comprehensive coverage of this book invaluable."--Jacket A process in engineering sciences is represented by two attributes: i) its input(s)/output(s) and ii) the principles or techniques by which the given input(s) is transformed to the desired output(s). A state-of-the-art research monograph providing consistent treatment of supervisory control, by one of the world’s leading groups in the area of Bayesian identification, control, and decision making. Accompanying CD-ROM ... "contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the most important and complex areas of the material presented."--Page 4 of cover Even though some real-world problems can be reduced to a matter of a single objective very often it is hard to define all the aspects in terms of a single objective. Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts. This section will briefly revise Bayes' rule and the concept of conditional probabilities.