Bøger i SpringerBriefs in Computer Science serien

The 5G technology has been commercialized worldwide and is expected to provide superior performance with enhanced mobile broadband, ultra-low latency transmission, and massive IoT connections. Meanwhile, the edge computing paradigm gets popular to provide distributed computing and storage resources in proximity to the users. As edge services and applications prosper, 5G and edge computing will be tightly coupled and continuously promote each other forward.Embracing this trend, however, mobile users, infrastructure providers, and service providers are all faced with the energy dilemma. On the user side, battery-powered mobile devices are much constrained by battery life, whereas mobile platforms and apps nowadays are usually power-hungry. At the infrastructure and service provider side, the energy cost of edge facilities accounts for a large proportion of operating expenses and has become a huge burden.This book provides a collection of most recent attempts to tackle the energy issues in mobile edge computing from new and promising perspectives. For example, the book investigates the pervasive low-battery anxiety among modern mobile users and quantifies the anxiety degree and likely behavior concerning the battery status. Based on the quantified model, a low-power video streaming solution is developed accordingly to save mobile devices' energy and alleviate users' low-battery anxiety. In addition to energy management for mobile users, the book also looks into potential opportunities to energy cost saving and carbon emission reduction at edge facilities, particularly the 5G base stations and geo-distributed edge datacenters.

This SpringerBrief presents a brief introduction to probabilistic risk assessment (PRA), followed by a discussion of abnormal event detection techniques in industrial control systems (ICS). It also provides an introduction to the use of game theory for the development of cyber-attack response models and a discussion on the experimental testbeds used for ICS cyber security research. The probabilistic risk assessment framework used by the nuclear industry provides a valid framework to understand the impacts of cyber-attacks in the physical world. An introduction to the PRA techniques such as fault trees, and event trees is provided along with a discussion on different levels of PRA and the application of PRA techniques in the context of cybersecurity. A discussion on machine learning based fault detection and diagnosis (FDD) methods and cyber-attack detection methods for industrial control systems are introduced in this book as well.A dynamic Bayesian networks based method that can be used to detect an abnormal event and classify it as either a component fault induced safety event or a cyber-attack is discussed. An introduction to the stochastic game formulation of the attacker-defender interaction in the context of cyber-attacks on industrial control systems to compute optimal response strategies is presented. Besides supporting cyber-attack response, the analysis based on the game model also supports the behavioral study of the defender and the attacker during a cyber-attack, and the results can then be used to analyze the risk to the system caused by a cyber-attack. A brief review of the current state of experimental testbeds used in ICS cybersecurity research and a comparison of the structures of various testbeds and the attack scenarios supported by those testbeds is included. A description of a testbed for nuclear power applications, followed by a discussion on the design of experiments that can be carried out on the testbed and the associated results is covered as well.This SpringerBrief  is a useful resource tool for researchers working in the areas of cyber security for industrial control systems, energy systems and cyber physical systems. Advanced-level students that study these topics will also find this SpringerBrief useful as a study guide.

This SpringerBrief discusses underlying principles of malware reverse engineering and introduces the major techniques and tools needed to effectively analyze malware that targets business organizations. It also covers the examination of real-world malware samples, which illustrates the knowledge and skills necessary to take control of cyberattacks.This SpringerBrief explores key tools and techniques to learn the main elements of malware analysis from the inside out. It also presents malware reverse engineering using several methodical phases, in order to gain a window into the mind set of hackers. Furthermore, this brief examines malicious program's behavior and views its code-level patterns. Real world malware specimens are used to demonstrate the emerging behavioral patterns of battlefield malware as well.This SpringerBrief is unique, because it demonstrates the capabilities of emerging malware by conducting reverse-code engineering on real malware samples and conducting behavioral analysis in isolated lab system. Specifically, the author focuses on analyzing malicious Windows executables. This type of malware poses a large threat to modern enterprises. Attackers often deploy malicious documents and browser-based exploits to attack Windows enterprise environment. Readers learn how to take malware inside-out using static properties analysis, behavioral analysis and code-level analysis techniques.The primary audience for this SpringerBrief is undergraduate students studying cybersecurity and researchers working in this field. Cyber security professionals that desire to learn more about malware analysis tools and techniques will also want to purchase this SpringerBrief.

This SpringerBrief introduces methodologies and tools for quantitative understanding and assessment of supply chain risk to critical infrastructure systems. It unites system reliability analysis, optimization theory, detection theory and mechanism design theory to study vendor involvement in overall system security. It also provides decision support for risk mitigation.This SpringerBrief introduces I-SCRAM, a software tool to assess the risk. It enables critical infrastructure operators to make risk-informed decisions relating to the supply chain, while deploying their IT/OT and IoT systems.The authors present examples and case studies on supply chain risk assessment/mitigation of modern connected infrastructure systems such as autonomous vehicles, industrial control systems, autonomous truck platooning and more. It also discusses how vendors of different system components are involved in the overall security posture of the system and how the risk can be mitigated through vendor selection and diversification. The specific topics in this book include: Risk modeling and analysis of IoT supply chains Methodologies for risk mitigation, policy management, accountability, and cyber insurance Tutorial on a software tool for supply chain risk management of IoT  These topics are supported by up-to-date summaries of the authors' recent research findings. The authors introduce a taxonomy of supply chain security and discusses the future challenges and directions in securing the supply chains of IoT systems. It also focuses on the need for joint policy and technical solutions to counter the emerging risks, where technology should inform policy and policy should regulate technology development.This SpringerBrief has self-contained chapters, facilitating the readers to peruse individual topics of interest. It provides a broad understanding of the emerging field of cyber supply chain security in the context of IoT systems to academics, industry professionals and government officials.

This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3  discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data.  Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elementsTo support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained problem. In addition, real-world data is complex and heterogeneous, and often violate the low-level assumptions required by existing compressive sensing techniques. These violations significantly reduce the applicability and effectiveness of existing compressive sensing methods. Third, synchronization of network data reduces the data ranks and increases spatial locality. However, periodic time series exhibit not only misalignment but also different frequencies, which makes it difficult to synchronize data in the time and frequency domains.The primary audience for this book is data engineers, analysts and researchers, who need to deal with big data with missing anomalous and synchronization problems. Advanced level students focused on compressive sensing techniques will also benefit from this book as a reference.

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effectiveAdvanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

This book provides basic knowledge about main memory management in relational databases as it is needed to support large-scale applications processed completely in memory. In business operations, real-time predictability and high speed is a must. Hence every opportunity must be exploited to improve performance, including reducing dependency on the hard disk, adding more memory to make more data resident in the memory, and even deploying an in-memory system where all data can be kept in memory.The book provides one chapter for each of the main related topics, i.e. the memory system, memory management, virtual memory, and databases and their memory systems, and it is complemented by a short survey of six commercial systems: TimesTen, MySQL, VoltDB, Hekaton, HyPer/ScyPer, and SAP HANA.

This Springer Brief presents a selection of tools and techniques which either enable or improve the use of DevOps for airborne software engineering. They are evaluated against the unique challenges of the aviation industry such as safety and airworthiness, and exercised using a demonstrator in order to gather first experience.The book is structured as follows: after a short introduction to the main topics of the work in chapter 1, chapter 2 provides more information on the tools, techniques, software and standards required to implement the subsequently presented ideas. In particular, the development practice BDD, the relation between DevOps, CI & CD and both the Rust & the Nix programming language are introduced. In chapter 3 the authors explain and justify their ideas towards advancing the state of the art, mapping the aforementioned tools and techniques to the DevOps Cycle while considering aspects of Do-178C. Next, in chapter 4 the experiences gathered while implementing a demonstrator using the tools and techniques are described. Eventually, chapter 5 briefly summarizes the findings and presents a compilation of open points and missing pieces which are yet to be resolved.The book targets three different reader groups. The first one are development managers from the aerospace industry who need to see examples and experience reports for the application of DevOps for airborne software. The second group are investigators in the safety-critical embedded systems domain who look for benchmarks at various application domains. And the third group are lecturers who offer graduate level software engineering courses for safety-critical software engineering.

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes¿ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.

Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge, authored by Dr Teoh Teik Toe, in applying Convolutional Neural Networks (CNNs) to the medical imagery domain. This book will expose researchers to various applications and techniques applied with deep learning on medical images, as well as unique techniques to enhance the performance of these networks.Through the various chapters and topics covered, this book provides knowledge about the fundamentals of deep learning to a common reader while allowing a research scholar to identify some futuristic problem areas. The topics covered include brain tumor classification, pneumonia image classification, white blood cell classification, skin cancer classification and diabetic retinopathy detection. The first chapter will begin by introducing various topics used in training CNNs to help readers with common concepts covered across the book. Each chapter begins by providing information about the disease, its implications to the affected and how the use of CNNs can help to tackle issues faced in healthcare. Readers would be exposed to various performance enhancement techniques, which have been tried and tested successfully, such as specific data augmentations and image processing techniques utilized to improve the accuracy of the models.

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book.In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques¿ effectiveness.

This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications.The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.

In the treatment of chronic diseases, wireless Implantable Medical Devices (IMDs) are commonly used to communicate with an outside programmer (reader). Such communication raises serious security concerns, such as the ability for hackers to gain access to a patient's medical records. This brief provides an overview of such attacks and the new security challenges, defenses, design issues, modeling and performance evaluation in wireless IMDs.  While studying the vulnerabilities of IMDs and corresponding security defenses, the reader will also learn the methodologies and tools for designing security schemes, modeling, security analysis, and performance evaluation, thus keeping pace with quickly-evolving wireless security research.

This brief surveys existing techniques to address the problem of long delays and high power consumption for web browsing on smartphones, which can be due to the local computational limitation at the smartphone (e.g., running java scripts or flash objects) level. To address this issue, an architecture called Virtual-Machine based Proxy (VMP) is introduced, shifting the computing from smartphones to the VMP which may reside in the cloud. Mobile Web Browsing Using the Cloud illustrates the feasibility of deploying the proposed VMP system in 3G networks through a prototype using Xen virtual machines (in cloud) and Android Phones with ATT UMTS network. Techniques to address scalability issues, resource management techniques to optimize the performance of the VMs on the proxy side, compression techniques to further reduce the bandwidth consumption, and adaptation techniques to address poor network conditions on the smartphone are also included.

We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.  In this brief, "trust context" is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout the globe in cooperative and competitive activities. Information is created and consumed at a global scale. Systems, devices, and sensors create and process data, manage physical systems, and participate in interactions with other entities, people and systems alike.  To study trust in such applications, we need a multi-disciplinary approach.  This book reviews the components of the trust context through a broad review of recent literature in many different fields of study. Common threads relevant to the trust context across many application domains are also illustrated.Illustrations in the text © 2013 Aaron Hertzmann. www.dgp.toronto.edu/~hertzman

This book provides an analysis of transmission power and network performance in different wireless communication networks. It presents the latest research and techniques for power and interference control and performance modeling in wireless communication networks with different network topologies, air interfaces, and transmission techniques. While studying the power distributions and resource management, the reader will also learn basic methodology and skills for problem formulations, can ascertain the complexity for designing radio resource management strategies in modern wireless communication networks, thus keeping pace with state-of-the-art research progress in radio transmission technologies.

The authors survey a recent technique in computer vision called Interactive Co-segmentation, which is the task of simultaneously extracting common foreground objects from multiple related images. They survey several of the algorithms, present underlying common ideas, and give an overview of applications of object co-segmentation.

In the last few years, there has been extensive research activity in the emerging area of Intermittently Connected Mobile Ad Hoc Networks (ICMANs). By considering the nature of intermittent connectivity in most real word mobile environments without any restrictions placedon users' behavior, ICMANs are eventually formed without any assumption with regard to the existence of a end-to-end path between two nodes wishing to communicate. It is different from the conventional Mobile Ad Hoc Networks (MANETs), which have been implicitly viewed as aconnected graph with established complete paths between every pair of nodes. For the conventional MANETs, mobility of nodes is considered as a challenge and needs to be handled properly to enable seamless communication between nodes. However, to overcome intermittentconnectivity in the ICMANs context, mobility is recognized as a critical component for data communications between the nodes that may never be part of the same connected portion of the network. This comes at the cost of addition considerable delay in data forwarding, since data areoften stored and carried by the intermediate nodes waiting for the mobility to generate the next forwarding opportunity that can probably bring it close to the destination. Such incurred large delays primarily limit ICMANs to the applications, which must tolerate delays beyond traditionalforwarding delays. ICMANs belong to the family of delay tolerant networks (DTNs). However, the unique characteristics (e.g., self-organizing, random mobility and ad hoc based connection) derived from MANETs distinguish ICMANs from other typical DTNs such as interplanetarynetwork (IPN) with infrastructure-based architecture.By allowing mobile nodes to connect and disconnect based on their behaviors and wills, ICMANs enable a number of novel applications to become possible in the field of MANETs. For example, there is a growing demand for efficient architectures for deploying opportunistic contentdistribution systems over ICMANs. This is because a large number of smart handheld devices with powerful functions enable mobile users to utilize low cost wireless connectivities such as Bluetooth and IEEE 802.11 for sharing and exchanging the multimedia contents anytime anywhere. Note that such phenomenal growth of content-rich services has promoted a new kind of networking where the content is delivered from its source (referred to as publisher) towards interested users (referred to as subscribers) rather than towards the pre-specified destinations.Compared to the extensive research activities relating to the routing and forwarding issues in ICMANs and even DTNs, opportunistic content distribution is just in its early stage and has not been widely addressed.With all these in mind, this book provides an in-depth discussion on the latest research efforts for opportunistic content distribution over ICMANs.

Cloud storage is an important service of cloud computing, which offers service for data owners to host their data in the cloud. This new paradigm of data hosting and data access services introduces two major security concerns. The first is the protection of data integrity. Data owners may not fully trust the cloud server and worry that data stored in the cloud could be corrupted or even removed. The second is data access control. Data owners may worry that some dishonest servers provide data access to users that are not permitted for profit gain and thus they can no longer rely on the servers for access control. To protect the data integrity in the cloud, an efficient and secure dynamic auditing protocol is introduced, which can support dynamic auditing and batch auditing. To ensure the data security in the cloud, two efficient and secure data access control schemes are introduced in this brief: ABAC for Single-authority Systems and DAC-MACS for Multi-authority Systems. While Ciphertext-Policy Attribute-based Encryption (CP-ABE) is a promising technique for access control of encrypted data, the existing schemes cannot be directly applied to data access control for cloud storage systems because of the attribute revocation problem. To solve the attribute revocation problem, new Revocable CP-ABE methods are proposed in both ABAC and DAC-MACS.

This brief presents a peer-to-peer (P2P) web-hosting infrastructure (named pWeb) that can transform networked, home-entertainment devices into lightweight collaborating Web servers for persistently storing and serving multimedia and web content. The issues addressed include ensuring content availability, Plexus routing and indexing, naming schemes, web ID, collaborative web search, network architecture and content indexing. In pWeb, user-generated voluminous multimedia content is proactively uploaded to a nearby network location (preferably within the same LAN or at least, within the same ISP) and a structured P2P mechanism ensures Internet accessibility by tracking the original content and its replicas. This new paradigm of information management strives to provide low or no-cost cloud storage and entices the end users to upload voluminous multimedia content to the cloud data centers. However, it leads to difficulties in privacy, network architecture and content availability. Concise and practical, this brief examines the benefits and pitfalls of the pWeb web-hosting infrastructure. It is designed for professionals and practitioners working on P2P and web management and is also a useful resource for advanced-level students studying networks or multimedia.

An Introduction to the Machine Learning Empowered Intelligent Data Center NetworkingFundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security.Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

Automated Configuration has long been the subject of intensive research, especially in Artificial Intelligence. It is a pervasive problem to be solved, and it is a good test of various knowledge representation and reasoning techniques. The problem shows up in applications such as various electrical circuit design, utility computing and even concurrent engineering. Automated Configuration Problem Solving defines the ubiquitous problem, illustrates the various solution techniques, and includes a survey using these techniques from the mid-70's until the mid-90's. During this time, various general approaches were developed, and then become more specialized. This book covers the development of the general problem solving techniques for automated configuration, which are based on both published academic work and patents.

Software similarity and classification is an emerging topic with wide applications. It is applicable to the areas of malware detection, software theft detection, plagiarism detection, and software clone detection. Extracting program features, processing those features into suitable representations, and constructing distance metrics to define similarity and dissimilarity are the key methods to identify software variants, clones, derivatives, and classes of software. Software Similarity and Classification reviews the literature of those core concepts, in addition to relevant literature in each application and demonstrates that considering these applied problems as a similarity and classification problem enables techniques to be shared between areas. Additionally, the authors present in-depth case studies using the software similarity and classification techniques developed throughout the book.