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Android Malware Detection using Machine Learning
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 netw...
Computer Security – ESORICS 2019
The two volume set, LNCS 11735 and 11736, constitutes the proceedings of the 24th European Symposium on Research in Computer Security, ESORIC 2019, held in Luxembourg, in September 2019. The total of 67 full papers included in these proceedings was carefully reviewed and selected from 344 submissions. The papers were organized in topical sections named as follows:Part I: machine learning; information leakage; signatures and re-encryption; side channels; formal modelling and verification; attacks; secure protocols; useful tools; blockchain and smart contracts.Part II: software security; cryptographic protocols; security models; searchable encryption; privacy; key exchange protocols; and web security.
Detection of Intrusions and Malware, and Vulnerability Assessment
This book constitutes the proceedings of the 18th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2021, held virtually in July 2021. The 18 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 65 submissions. DIMVA serves as a premier forum for advancing the state of the art in intrusion detection, malware detection, and vulnerability assessment. Each year, DIMVA brings together international experts from academia, industry, and government to present and discuss novel research in these areas. Chapter “SPECULARIZER: Detecting Speculative Execution Attacks via Performance Tracing” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Innovations In Digital Forensics
Digital forensics deals with the investigation of cybercrimes. With the growing deployment of cloud computing, mobile computing, and digital banking on the internet, the nature of digital forensics has evolved in recent years, and will continue to do so in the near future.This book presents state-of-the-art techniques to address imminent challenges in digital forensics. In particular, it focuses on cloud forensics, Internet-of-Things (IoT) forensics, and network forensics, elaborating on innovative techniques, including algorithms, implementation details and performance analysis, to demonstrate their practicality and efficacy. The innovations presented in this volume are designed to help various stakeholders with the state-of-the-art digital forensics techniques to understand the real world problems. Lastly, the book will answer the following questions: How do the innovations in digital forensics evolve with the emerging technologies? What are the newest challenges in the field of digital forensics?
Artificial Intelligence for Cybersecurity
This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity. This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more. Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.
New Trends in Intelligent Software Methodologies, Tools and Techniques
The integration of AI with software is an essential enabler for science and the new economy, creating new markets and opportunities for a more reliable, flexible and robust society. Current software methodologies, tools and techniques often fall short of expectations, however, and much software remains insufficiently robust and reliable for a constantly changing and evolving market. This book presents 54 papers delivered at the 20th edition of the International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques (SoMeT_21), held in Cancun, Mexico, from 21–23 September 2021. The aim of the conference was to capture the essence of a new state-of-the-art in soft...
Applied Learning Algorithms for Intelligent IoT
This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interface...
Malware Detection
This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.
Data Science For Cyber-security
Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.
Android Malware
Mobile devices, such as smart phones, have achieved computing and networking capabilities comparable to traditional personal computers. Their successful consumerization has also become a source of pain for adopting users and organizations. In particular, the widespread presence of information-stealing applications and other types of mobile malware raises substantial security and privacy concerns. Android Malware presents a systematic view on state-of-the-art mobile malware that targets the popular Android mobile platform. Covering key topics like the Android malware history, malware behavior and classification, as well as, possible defense techniques.
