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Advances in Information Security

Kayem A: Enabling Privacy Preserving
Data Analytics

Enabling Privacy Preserving Data Analytics

Modeling, Analysis, Design, Human Factors, and Their Convergence

Foundations of Cyber Deception

This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addresses techniques and methodologies related to digital image forensics. It discusses advanced techniques for image manipulation detection, including the use of deep learning architectures to generate and manipulate synthetic satellite images. This book also explores methods for face recognition under adverse conditions and the importance of forensics in criminal investigations. Additionally, the book highlights anti-forensic measures applied to photos and videos, focusing on their effectiveness and trade-offs.The second area of this book focuses on the broader landscape of security, including the detection of synthetic human voices, secure deep neural networks (DNNs) and federated learning in the context of machine learning security. It investigates novel methods for detecting synthetic human voices using neural vocoder artifacts, and it explores the vulnerabilities and security challenges of federated learning in the face of adversarial attacks. Furthermore, this book delves into the realms of linguistic steganography and steganalysis, discussing the evolving techniques that utilize deep learning and natural language processing to enhance payload and detection accuracy.Overall, this book provides a comprehensive overview of the ever-evolving field of digital forensics and security, making it an invaluable resource for researchers and students interested in image forensics, machine learning security and information protection. It equips readers with the latest knowledge and tools to address the complex challenges posed by the digital landscape. Professionals working in this related field will also find this book to be a valuable resource.

Adversarial Multimedia Forensics

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning, federated learning, intrusion detection systems, and distributed computing paradigms. This book also offers a collection of the most up-to-date research, providing a complete overview of security and privacy-preserving in IoT environments. It introduces new approaches based on machine learning that tackles security challenges and provides the field with new research material that’s not covered in the primary literature.&nbsp; The Internet of Things (IoT) refers to a network of tiny devices linked to the Internet or other communication networks. IoT is gaining popularity, because it opens up new possibilities for developing many modern applications. This would include smart cities, smart agriculture, innovativehealthcare services and more. The worldwide IoT market surpassed $100 billion in sales for the first time in 2017, and forecasts show that this number might reach $1.6 trillion by 2025. However, as IoT devices grow more widespread, threats, privacy and security concerns are growing. The massive volume of data exchanged highlights significant challenges to preserving individual privacy and securing shared data. Therefore, securing the IoT environment becomes difficult for research and industry stakeholders. Researchers, graduate students and educators in the fields of computer science, cybersecurity, distributed systems and artificial intelligence will want to purchase this book. It will also be a valuable companion for users and developers interested in decision-making and security risk management in IoT environments.

Decision Making and Security Risk Management for IoT Environments

This book provides a comprehensive review of the most up to date research related to cloud security auditing and discusses auditing the cloud infrastructure from the structural point of view, while focusing on virtualization-related security properties and consistency between multiple control layers. It presents an off-line automated framework for auditing consistent isolation between virtual networks in OpenStack-managed cloud spanning over overlay and layer 2 by considering both cloud layers’ views. A runtime security auditing framework for the cloud with special focus on the user-level including common access control and authentication mechanisms e.g., RBAC, ABAC and SSO is covered as well.&nbsp; This book also discusses a learning-based proactive security auditing system, which extracts probabilistic dependencies between runtime events and applies such dependencies to proactively audit and prevent security violations resulting from critical events. Finally, this book elaborates the design and implementation of a middleware as a pluggable interface to OpenStack for intercepting and verifying the legitimacy of user requests at runtime. The authors discuss how state-of-the-art security auditing solutions may help increase cloud tenants’ trust in the service providers by providing assurance on the compliance with the applicable laws, regulations, policies, and standards. This book introduces the latest research results on both traditional retroactive auditing and novel (runtime and proactive) auditing techniques to serve different stakeholders in the cloud. This book covers security threats from different cloud abstraction levels and discusses a wide-range of security properties related to cloud-specific standards (e.g., Cloud Control Matrix (CCM) and ISO 27017). It also elaborates on the integration of security auditing solutions into real world cloud management platforms (e.g., OpenStack, Amazon AWS and GoogleGCP). This book targets industrial scientists, who are working on cloud or security-related topics, as well as security practitioners, administrators, cloud providers and operators. Researchers and advanced-level students studying and working in computer science, practically in cloud security will also be interested in this book.

Cloud Security Auditing

This book discusses and summarizes current research issues, identifies challenges, and outlines future directions for proactive and dynamic network defense. This book also presents the latest fundamental research results toward understanding proactive and dynamic network defense by top researchers in related areas. It includes research results that offer formal frameworks to define proactive and dynamic network defense, and develop novel models to analyze and evaluate proactive designs and strategies in computer systems, network systems, cyber-physical systems and wireless networks. A wide variety of scientific techniques have been highlighted to study these problems in the fundamental domain.


 As the convergence of our physical and digital worlds grows fast pace, protecting information systems from being tampered or unauthorized access is becoming one of the most importance issues. The traditional mechanisms of network defense are built upon a static, passive, and reactive nature, which has insufficient to defend against today's attackers that attempt to persistently analyze, probe, circumvent or fool such mechanisms. It has not yet been fully investigated to address the early stage of ¿cyber kill chain¿ when adversaries carry out sophisticated reconnaissance to plan attacks against a defense system.


 Recently, proactive and dynamic network defense has been proposed as an important alternative towards comprehensive network defense. Two representative types of such defense are moving target defense (MTD) and deception-based techniques. These emerging approaches show great promise to proactively disrupt the cyber-attack kill chain and are increasingly gaining interest within both academia and industry. However, these approaches are still in their preliminary design stage. Despite the promising potential, there areresearch issues yet to be solved regarding the effectiveness, efficiency, costs and usability of such approaches. In addition, it is also necessary to identify future research directions and challenges, which is an essential step towards fully embracing proactive and dynamic network defense.


This book will serve as a great introduction for advanced-level computer science and engineering students who would like to start R&amp;D efforts in the field of proactive and dynamic network defense. Researchers and professionals who work in this related field will also find this book useful as a reference.

Proactive and Dynamic Network Defense

This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes.
The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions ¿ this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works.
 
The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgroundsin artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.

Cyber Threat Intelligence

This book covers and makes four major contributions: 1) analyzing and surveying the pros and cons of current approaches for identifying rumor sources on complex networks; 2) proposing a novel approach to identify rumor sources in time-varying networks; 3) developing a fast approach to identify multiple rumor sources; 4) proposing a community-based method to overcome the scalability issue in this research area. These contributions enable rumor source identification to be applied effectively in real-world networks, and eventually diminish rumor damages, which the authors rigorously illustrate in this book. 
 
In the modern world, the ubiquity of networks has made us vulnerable to various risks. For instance, viruses propagate throughout the Internet and infect millions of computers. Misinformation spreads incredibly fast in online social networks, such as Facebook and Twitter. Infectious diseases, such as SARS, H1N1 or Ebola, have spread geographically and killed hundreds of thousands people. In essence, all of these situations can be modeled as a rumor spreading through a network, where the goal is to find the source of the rumor so as to control and prevent network risks. So far, extensive work has been done to develop new approaches to effectively identify rumor sources. However, current approaches still suffer from critical weaknesses. The most serious one is the complex spatiotemporal diffusion process of rumors in time-varying networks, which is the bottleneck of current approaches. The second problem lies in the expensively computational complexity of identifying multiple rumor sources. The third important issue is the huge scale of the underlying networks, which makes it difficult to develop efficient strategies to quickly and accurately identify rumor sources. These weaknesses prevent rumor source identification from being applied in a broader range of real-world applications. This book aims to analyze and address these issuesto make rumor source identification more effective and applicable in the real world. 
 
The authors propose a novel reverse dissemination strategy to narrow down the scale of suspicious sources, which dramatically promotes the efficiency of their method. The authors then develop a Maximum-likelihood estimator, which can pin point the true source from the suspects with high accuracy. For the scalability issue in rumor source identification, the authors explore sensor techniques and develop a community structure based method. Then the authors take the advantage of the linear correlation between rumor spreading time and infection distance, and develop a fast method to locate the rumor diffusion source. Theoretical analysis proves the efficiency of the proposed method, and the experiment results verify the significant advantages of the proposed method in large-scale networks. 
 
This book targets graduate and post-graduate students studying computer science and networking. Researchers and professionals working in network security, propagation models and other related topics, will also be interested in this book.

Malicious Attack Propagation and Source Identification

<div>This book targets the key concern of protecting critical infrastructures such as smart grids. It explains various static and dynamic security analysis techniques that can automatically verify smart grid security and resiliency and identify potential attacks in a proactive manner. </div><div>This book includes three main sections. The first presents the idea of formally verifying the compliance of smart grid configurations with the security and resiliency guidelines. It provides a formal framework that verifies the compliance of the advanced metering infrastructure (AMI) configurations with the security and resiliency requirements, and generates remediation plans for potential security violations. The second section covers the formal verification of the security and resiliency of smart grid control systems by using a formal model to analyze attack evasions on state estimation, a core control module of the supervisory control system in smart grids. The model identifies attack vectors that can compromise state estimation. This section also covers risk mitigation techniques that synthesize proactive security plans that make such attacks infeasible. The last part of the book discusses the dynamic security analysis for smart grids. It shows that AMI behavior can be modeled using event logs collected at smart collectors, which in turn can be verified using the specification invariants generated from the configurations of the AMI devices. </div><div>Although the focus of this book is smart grid security and resiliency, the included formal analytics are generic enough to be extended to other cyber-physical systems, especially those related to industrial control systems (ICS). Therefore, industry professionals and academic researchers will find this book an exceptional resource to learn theoretical and practical aspects of applying formal methods for the protection of critical infrastructures.</div><div>
</div>

Static and Dynamic Approaches

Security and Resiliency Analytics for Smart Grids

This book offers a novel approach to data privacy by unifying  side-channel attacks within a general conceptual framework. This book  then applies the framework in three concrete domains.&nbsp;First,  the book examines privacy-preserving data publishing with publicly-known  algorithms, studying a generic strategy independent of data utility  measures and syntactic privacy properties before discussing an extended  approach to improve the efficiency. Next, the book explores  privacy-preserving traffic padding in Web applications, first via a  model to quantify privacy and cost and then by introducing randomness to  provide background knowledge-resistant privacy guarantee. Finally, the  book considers privacy-preserving smart metering by proposing a  light-weight approach to simultaneously preserving users' privacy and  ensuring billing accuracy.&nbsp;Designed for researchers and  professionals, this book is also suitable for advanced-level students  interestedin privacy, algorithms, or web applications.

From Data Publishing to Web Applications

Preserving Privacy Against Side-Channel Leaks

This book explores cybersecurity research and development efforts, including ideas that deal with the growing challenge of how computing engineering can merge with neuroscience. The contributing authors, who are renowned leaders in this field, thoroughly examine new technologies that will automate security procedures and perform autonomous functions with decision making capabilities. To maximize reader insight into the range of professions dealing with increased cybersecurity issues, this book presents work performed by government, industry, and academic research institutions working at the frontier of cybersecurity and network sciences. Cybersecurity Systems for Human Cognition Augmentation is designed as a reference for practitioners or government employees working in cybersecurity. Advanced-level students or researchers focused on computer engineering or neuroscience will also find this book a useful resource.

Cybersecurity Systems for Human Cognition Augmentation

Lecture Notes in Computer Science

16th TPC Technology Conference, TPCTC 2024, Guangzhou, China, August 30, 2024, Revised Selected Papers

Performance Evaluation and Benchmarking

Communications in Computer and Information Science

First International Conference, AIDSP 2023, Kanpur, India, October 20–21, 2023, Proceedings, Part I

Emerging Trends in Artificial Intelligence, Data Science and Signal Processing

This book is the first one that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. They develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.

Zeroing Dynamics, Gradient Dynamics, and Newton Iterations

Synthesis Lectures on Computer Science

Practical Uses and Instruments for Exploring Complex Networks

Graph Mining

Specialty Crops and Climate Change Modeling

Artificial Intelligence and Data Science in Agriculture

Milan, Italy, September 29–October 4, 2024, Proceedings, Part XIX

Computer Vision – ECCV 2024 Workshops

Studies in Systems, Decision and Control

Volume 2

AI and IoT: Driving Business Success and sustainability in the Digital Age

Digital Geographies

Purposeful Use of the Internet and Technologies

Global Digital Divides in the COVID-19 Era

ICT in Asset Management

This book provides insights on how to approach and utilise data science tools, technologies/methodologies related to artificial intelligence in industrial context including their essence and inter-connections. Description of technology/methodology approaches, their purpose and benefits when developing AI-solution is given with case studies.

Theories, Applications and Case Studies

AI Factory

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: <ul><li>A blend of robust statistics and machine learning principles</li><li>Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection</li><li>Python code with immediate application to data science problems</li></ul> Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Handling Outliers and Anomalies in Data Science

Fundamentals of Robust Machine Learning

6th International Conference, AIST 2024, Delhi, India, November 13–14, 2024, Proceedings, Part I

Artificial Intelligence and Speech Technology

Thematic Area, HCI 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, June 22–27, 2025, Proceedings, Part IV

Human-Computer Interaction

Enabling Privacy Preserving Data Analytics

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