The book first explores the cybersecurity's landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.

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.

This book addresses automated software fingerprinting in binary code, especially for cybersecurity applications. The reader will gain a thorough understanding of binary code analysis and several software fingerprinting techniques for cybersecurity applications, such as malware detection, vulnerability analysis, and digital forensics. More specifically, it starts with an overview of binary code analysis and its challenges, and then discusses the existing state-of-the-art approaches and their cybersecurity applications. Furthermore, it discusses and details a set of practical techniques for compiler provenance extraction, library function identification, function fingerprinting, code reuse detection, free open-source software identification, vulnerability search, and authorship attribution. It also illustrates several case studies to demonstrate the efficiency, scalability and accuracy of the above-mentioned proposed techniques and tools. This book also introduces several innovative quantitative and qualitative techniques that synergistically leverage machine learning, program analysis, and software engineering methods to solve binary code fingerprinting problems, which are highly relevant to cybersecurity and digital forensics applications. The above-mentioned techniques are cautiously designed to gain satisfactory levels of efficiency and accuracy. Researchers working in academia, industry and governmental agencies focusing on Cybersecurity will want to purchase this book. Software engineers and advanced-level students studying computer science, computer engineering and software engineering will also want to purchase this book.

At the dawn of the 21st century and the information age, communication and c- puting power are becoming ever increasingly available, virtually pervading almost every aspect of modern socio-economical interactions. Consequently, the potential for realizing a signi?cantly greater number of technology-mediated activities has emerged. Indeed, many of our modern activity ?elds are heavily dependant upon various underlying systems and software-intensive platforms. Such technologies are commonly used in everyday activities such as commuting, traf?c control and m- agement, mobile computing, navigation, mobile communication. Thus, the correct function of the forenamed computing systems becomes a major concern. This is all the more important since, in spite of the numerous updates, patches and ?rmware revisions being constantly issued, newly discovered logical bugs in a wide range of modern software platforms (e. g. , operating systems) and software-intensive systems (e. g. , embedded systems) are just as frequently being reported. In addition, many of today's products and services are presently being deployed in a highly competitive environment wherein a product or service is succeeding in most of the cases thanks to its quality to price ratio for a given set of features. Accordingly, a number of critical aspects have to be considered, such as the ab- ity to pack as many features as needed in a given product or service while c- currently maintaining high quality, reasonable price, and short time -to- market.

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. 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. Many companies nowadays leverage cloud services for conducting major business operations (e.g., Web service, inventory management, customer service, etc.). However, the fear of losing control and governance still persists due to the inherent lack of transparency and trust in clouds. The complex design and implementation of cloud infrastructures may cause numerous vulnerabilities and misconfigurations, while the unique properties of clouds (elastic, self-service, multi-tenancy) can bring novel security challenges. In this book, 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 Google GCP). 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.

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.

The book first explores the cybersecurity's landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.

This book addresses automated software fingerprinting in binary code, especially for cybersecurity applications. The reader will gain a thorough understanding of binary code analysis and several software fingerprinting techniques for cybersecurity applications, such as malware detection, vulnerability analysis, and digital forensics. More specifically, it starts with an overview of binary code analysis and its challenges, and then discusses the existing state-of-the-art approaches and their cybersecurity applications. Furthermore, it discusses and details a set of practical techniques for compiler provenance extraction, library function identification, function fingerprinting, code reuse detection, free open-source software identification, vulnerability search, and authorship attribution. It also illustrates several case studies to demonstrate the efficiency, scalability and accuracy of the above-mentioned proposed techniques and tools. This book also introduces several innovative quantitative and qualitative techniques that synergistically leverage machine learning, program analysis, and software engineering methods to solve binary code fingerprinting problems, which are highly relevant to cybersecurity and digital forensics applications. The above-mentioned techniques are cautiously designed to gain satisfactory levels of efficiency and accuracy. Researchers working in academia, industry and governmental agencies focusing on Cybersecurity will want to purchase this book. Software engineers and advanced-level students studying computer science, computer engineering and software engineering will also want to purchase this book.

This book constitutes the revised selected papers of the 11th International Symposium on Foundations and Practice of Security, FPS 2018, held in Montreal, QC, Canada, in March 2018. The 16 full papers, 1 short paper, 1 position paper and 2 invited papers presented in this book, were carefully reviewed and selected from 51 submissions. They cover a range of topics including mobile security; cloud security and big data; IoT security; software security, malware analysis, and vulnerability detection; cryptography; cyber physical security and hardware security; and access control.

At the dawn of the 21st century and the information age, communication and c- puting power are becoming ever increasingly available, virtually pervading almost every aspect of modern socio-economical interactions. Consequently, the potential for realizing a signi?cantly greater number of technology-mediated activities has emerged. Indeed, many of our modern activity ?elds are heavily dependant upon various underlying systems and software-intensive platforms. Such technologies are commonly used in everyday activities such as commuting, traf?c control and m- agement, mobile computing, navigation, mobile communication. Thus, the correct function of the forenamed computing systems becomes a major concern. This is all the more important since, in spite of the numerous updates, patches and ?rmware revisions being constantly issued, newly discovered logical bugs in a wide range of modern software platforms (e. g. , operating systems) and software-intensive systems (e. g. , embedded systems) are just as frequently being reported. In addition, many of today's products and services are presently being deployed in a highly competitive environment wherein a product or service is succeeding in most of the cases thanks to its quality to price ratio for a given set of features. Accordingly, a number of critical aspects have to be considered, such as the ab- ity to pack as many features as needed in a given product or service while c- currently maintaining high quality, reasonable price, and short time -to- market.

This book is a comprehensive presentation of embedded Java security. It is compared with the security model of the Java 2 Standard Edition in order to view the impact of limited resources on security. No other book specifically addresses the topic of embedded Java security. Furthermore, the book provides hints and suggestions as ways for hardening security, and offers researchers and practitioners alike a broader and deeper understanding of the issues involved in embedded Java security, and as a larger view - mobile devices security. The author is a well-known authority and expert in mobile computing and embedded devices.

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. 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. Many companies nowadays leverage cloud services for conducting major business operations (e.g., Web service, inventory management, customer service, etc.). However, the fear of losing control and governance still persists due to the inherent lack of transparency and trust in clouds. The complex design and implementation of cloud infrastructures may cause numerous vulnerabilities and misconfigurations, while the unique properties of clouds (elastic, self-service, multi-tenancy) can bring novel security challenges. In this book, 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 Google GCP). 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.

This book constitutes the carefully refereed post-proceedings of the 6th Symposium on Foundations and Practice of Security, FPS 2013, held in La Rochelle, France, in October 2013. The 25 revised full papers presented together with a keynote address were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on security protocols, formal methods, physical security, attack classification and assessment, access control, cipher attacks, ad-hoc and sensor networks, resilience and intrusion detection.