An increasing number of countries develop capabilities for cyber-espionage and sabotage. The sheer number of reported network compromises suggests that some of these countries view cyber-means as integral and well-established elements of their strategical toolbox. At the same time the relevance of such attacks for society and politics is also increasing. Digital means were used to influence the US presidential election in 2016, repeatedly led to power outages in Ukraine, and caused economic losses of hundreds of millions of dollars with a malfunctioning ransomware. In all these cases the question who was behind the attacks is not only relevant from a legal perspective, but also has a political and social dimension. Attribution is the process of tracking and identifying the actors behind these cyber-attacks. Often it is considered an art, not a science. This book systematically analyses how hackers operate, which mistakes they make, and which traces they leave behind. Using examples from real cases the author explains the analytic methods used to ascertain the origin of Advanced Persistent Threats.

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.

This open access book constitutes the refereed proceedings of the 15th International Annual Conference on Cyber Security, CNCERT 2018, held in Beijing, China, in August 2018. The 14 full papers presented were carefully reviewed and selected from 53 submissions. The papers cover the following topics: emergency response, mobile internet security, IoT security, cloud security, threat intelligence analysis, vulnerability, artificial intelligence security, IPv6 risk research, cybersecurity policy and regulation research, big data analysis and industrial security.

Cyber Mercenaries explores the secretive relationships between states and hackers. As cyberspace has emerged as the new frontier for geopolitics, states have become entrepreneurial in their sponsorship, deployment, and exploitation of hackers as proxies to project power. Such modern-day mercenaries and privateers can impose significant harm undermining global security, stability, and human rights. These state-hacker relationships therefore raise important questions about the control, authority, and use of offensive cyber capabilities. While different countries pursue different models for their proxy relationships, they face the common challenge of balancing the benefits of these relationships with their costs and the potential risks of escalation. This book examines case studies in the United States, Iran, Syria, Russia, and China for the purpose of establishing a framework to better understand and manage the impact and risks of cyber proxies on global politics.

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.

This publication highlights the fast-moving technological advancement and infiltration of Artificial Intelligence into society. Concepts of evolution of society through interconnectivity are explored, together with how the fusion of human and technological interaction leading to Augmented Humanity is fast becoming more than just an endemic phase, but a cultural phase shift to digital societies. It aims to balance both the positive progressive outlooks such developments bring with potential issues that may stem from innovation of this kind, such as the invasive procedures of bio hacking or ethical connotations concerning the usage of digital twins. This publication will also give the reader a good level of understanding on fundamental cyber defence principles, interactions with Critical National Infrastructure (CNI) and the Command, Control, Communications and Intelligence (C3I) decision-making framework. A detailed view of the cyber-attack landscape will be garnered; touching on the tactics, techniques and procedures used, red and blue teaming initiatives, cyber resilience and the protection of larger scale systems. The integration of AI, smart societies, the human-centric approach and Augmented Humanity is discernible in the exponential growth, collection and use of [big] data; concepts woven throughout the diversity of topics covered in this publication; which also discusses the privacy and transparency of data ownership, and the potential dangers of exploitation through social media. As humans are become ever more interconnected, with the prolificacy of smart wearable devices and wearable body area networks, the availability of and abundance of user data and metadata derived from individuals has grown exponentially. The notion of data ownership, privacy and situational awareness are now at the forefront in this new age.