Machine Learning Techniques For Pattern Recognition And Information Security
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Machine Learning Techniques For Pattern Recognition And Information Security
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Author : Mohit Dua
language : en
Publisher:
Release Date : 2020
Machine Learning Techniques For Pattern Recognition And Information Security written by Mohit Dua and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Database security categories.
"This book examines the impact of machine learning techniques on pattern recognition and information security"--
Handbook Of Research On Machine Learning Techniques For Pattern Recognition And Information Security
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Author : Dua, Mohit
language : en
Publisher: IGI Global
Release Date : 2021-05-14
Handbook Of Research On Machine Learning Techniques For Pattern Recognition And Information Security written by Dua, Mohit and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-14 with Computers categories.
The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives. The Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.
Introduction To Machine Learning With Applications In Information Security
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Author : Mark Stamp
language : en
Publisher: CRC Press
Release Date : 2017-09-22
Introduction To Machine Learning With Applications In Information Security written by Mark Stamp and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-22 with Business & Economics categories.
Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.
Artificial Intelligence And Data Mining Approaches In Security Frameworks
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Author : Neeraj Bhargava
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-11
Artificial Intelligence And Data Mining Approaches In Security Frameworks written by Neeraj Bhargava and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-11 with Technology & Engineering categories.
ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS Written and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library. This groundbreaking new volume: Clarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks Covers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day Contains numerous examples, offering critical solutions to engineers and scientists Presents these new applications of AI and data mining that are of prime importance to human civilization as a whole
Computer Vision And Recognition Systems Using Machine And Deep Learning Approaches
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Author : Chiranji Lal Chowdhary
language : en
Publisher: Computing and Networks
Release Date : 2021-11
Computer Vision And Recognition Systems Using Machine And Deep Learning Approaches written by Chiranji Lal Chowdhary and has been published by Computing and Networks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11 with Computers categories.
Written by a team of International experts, this edited book covers state-of-the-art research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real-world applications. The book will be useful for industry and academic researchers, scientists and engineers.
Machine Learning And Security
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Author : Clarence Chio
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-01-26
Machine Learning And Security written by Clarence Chio and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-26 with Computers categories.
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself! With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Deep Learning Applications For Cyber Security
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Author : Mamoun Alazab
language : en
Publisher: Springer
Release Date : 2019-08-14
Deep Learning Applications For Cyber Security written by Mamoun Alazab and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-14 with Computers categories.
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Android Malware Detection Using Machine Learning
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Author : ElMouatez Billah Karbab
language : en
Publisher: Springer Nature
Release Date : 2021-07-10
Android Malware Detection Using Machine Learning written by ElMouatez Billah Karbab and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-10 with Computers categories.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Advances In Pattern Recognition Research
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Author : Thomas Lu
language : en
Publisher:
Release Date : 2018
Advances In Pattern Recognition Research written by Thomas Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.
Artificial Intelligence (AI) has become a popular research topic recently. Pattern recognition (PR) is an important part of an AI system. If the AI is considered as the digital "brain", then the PR is the visual and auditory "cortex" that converts the optical signals from the eyes and the acoustic signals from the ears to meaningful symbolic texts that the brain can digest. Over the past 40+ years, the processing speed of a digital computer has increased from kbits/s to tera floating point operations per second (TFLOPS), a 109 times acceleration. PR research has made significant advancements along the advancement of digital hardware, especially the graphical processing unit (GPU) technology that helps the rapid processing of complex images. In this book, the authors have collected the latest work from leading researchers in the PR fields. The topics are broad, which include optical implementation of various filters, digital implementation of state-of-the-art neural network (NN) training methods, and the latest deep leaning (DL) models. We also included applications of PR in various fields.In Chapter One, an optical implementation of an advanced multi-stage automatic target recognition (ATR) processor is introduced. The grayscale optical correlator (GOC) has been implemented in a compact and rugged 2x2x2 inch3 cube. It is the world's smallest optical correlator. Combined with a neural network (NN) classifier, the system becomes an efficient embedded vision system that learns to detect multiple targets embedded in large images with unknown backgrounds.The deep neural network (DNN) learning model has become a phenomenal research topic. In Chapter Two, state-of-the-art DNN architectures are introduced. Applications of DNN in object segmentation, recognition and augmented reality are presented.In Chapter Three, recent trends on invariant pattern recognition via joint transform correlation (JTC) are presented. Enhanced correlation filters such as logarithmic fringe-adjusted filter (LFAF), phase-encoded fringe-adjusted JTC (PJTC), shifted PJTC (SPJTC), Gaussian filtering based SPJTC (G-SPJTC) and Gaussian filter based logarithmic fringe-adjusted JTC (G-LFJTC) are discussed and tested for face recognition and texture identification.In Chapter Four, a class of optical synthetic filters, the optimal trade-off maximum average correlation height (OT-MACH) filter is investigated. The spatial domain OT-MACH (SPOT-MACH) filters are compared to the frequency domain filters for PR in infrared (IR) images with poor contrast or large illumination gradients.Cyber security has become an important research topic. Most cyber-attacks follow a certain pattern. Chapter Five discusses the applications of DL models as a PR technique to exploit this underlying characteristic of the cyber-attack data in information security.Chapter Six discusses the recognition of handwritten numerals in the Modified National Institute of Standard (MNIST) database using probabilistic neural network (PNN) models.Chapter Seven discusses several training methodologies of the artificial neural network (ANN) models. In Chapter Eight, the ANN training models are used in extracting spatial features for printed characters recognition.
Pattern Recognition Machine Intelligence And Biometrics
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Author : Patrick S. P. Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-13
Pattern Recognition Machine Intelligence And Biometrics written by Patrick S. P. Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-13 with Computers categories.
"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.