In the past, there were perceived delineations between the cyber world and the physical world. We are becoming increasingly aware of the overlap between these two worlds, and the overlap itself is increasing. The overlap between these two worlds is known as cyber-physical systems. There have been several incidents involving cyber-physical systems and the number of these incidents is increasing dramatically. In the past there has been no effort to identify methods for describing these incidents in the unique context of cyber-physical systems. This research provides a taxonomy for classifying these incidents that focuses on cross domain, impact oriented analysis. A repository for information about these incidents has also been created as part of this research.

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yes / Cyber-attacks have been an increasing threat on people and organisations, which led to massive unpleasant impact. Therefore, there were many solutions to handle cyber-attacks, including Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS). These solutions will provide a huge number of alarms that produce more are false positives. Therefore, the IDS tool result should be operated by a human intelligent be filtered effectively the huge amount of alerts to identify true positive attacks and perform accordingly to the incident response rule. This requires the IT employees to have enough knowledge and competency on operating IDS, IPS and incident handling. This paper aims to examine the awareness of cyber security threat among all IT employees, focusing on three domains: Knowledge, Monitoring and Prevention.

The growing adoption of smart manufacturing systems and its related technologies (e.g., embedded sensing, internet-of-things, cyber-physical systems, big data analytics, and cloud computing) is promising a paradigm shift in the manufacturing industry. Such systems enable extracting and exchanging actionable knowledge across the different entities of the manufacturing cyber-physical system and beyond. From a quality control perspective, this allows for more opportunities to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. However, a multitude of challenges are arising, with the growing adoption of smart manufacturing, including industrial data characterized by increasing volume, velocity, variety, and veracity, as well as the security of the manufacturing system in the presence of growing connectivity. Taking advantage of these emerging opportunities and tackling the upcoming challenges require creating novel quality control and data analytics methods, which not only push the boundaries of the current state-of-the-art research, but discover new ways to analyze the data and utilize it. One of the key pillars of smart manufacturing systems is real-time automated process monitoring, diagnosis, and control methods for process/product anomalies. For machining applications, traditionally, deterioration in quality measures may occur due to a variety of assignable causes of variation such as poor cutting tool replacement decisions and inappropriate choice cutting parameters. Additionally, due to increased connectivity in modern manufacturing systems, process/product anomalies intentionally induced through malicious cyber-attacks -- aiming at degrading the process performance and/or the part quality -- is becoming a growing concern in the manufacturing industry. Current methods for detecting and diagnosing traditional causes of anomalies are primarily lab-based and require experts to perform initial set-ups and continual fine-tuning, reducing the applicability in industrial shop-floor applications. As for efforts accounting for process/product anomalies due cyber-attacks, these efforts are in early stages. Therefore, more foundational research is needed to develop a clear understanding of this new type of cyber-attacks and their effects on machining processes, to ensure smart manufacturing security both on the cyber and the physical levels. With primary focus on machining processes, the overarching goal of this dissertation work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on adopting this goal in three distinct areas of interest: (1) Statistical Process Monitoring of Time-Between-Events Data (e.g., failure-time data); (2) Defending against Product-Oriented Cyber-Physical Attacks on Intelligent Machining Systems; and (3) Modeling Machining Process Data: Time Series vs. Spatial Point Cloud Data Structures. / PHD / Recent advancements in embedded sensing, internet-of-things, big data analytics, cloud computing, and communication technologies and methodologies are shifting the modern manufacturing industry toward a novel operational paradigm. Several terms have been coined to refer to this new paradigm such as cybermanufacturing, industry 4.0, industrial internet of things, industrial internet, or more generically smart manufacturing (term to be used henceforth). The overarching goal of smart manufacturing is to transform modern manufacturing systems to knowledge-enabled Cyber-Physical Systems (CPS), in which humans, machines, equipment, and products communicate and cooperate together in real-time, to make decentralized decisions resulting in profound improvements in the entire manufacturing ecosystem. From a quality control perspective, this allows for more opportunities to utilize manufacturing process data to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. With primary focus on machining processes, the overarching goal of this work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on three distinct areas of interest: (1) Monitoring of time-between-events data of mechanical components replacements (e.g., failure-time data); (2) Defending against cyber-physical attacks on intelligent machining systems aiming at degrading machined parts quality; and (3) Modeling machining process data using two distinct data structures, namely, time series and spatial point cloud data.

With recent advancements in computer and network technologies, cyber-physical systems have become more susceptible to cyber-attacks; and production systems are no exception. Unlike traditional Information Technology (IT) systems, cyber-physical systems are not limited to attacks aimed at Intellectual Property (IP) theft, but also include attacks that maliciously affect the physical world. In manufacturing, such cyber-physical attacks can destroy equipment, force dimensional product changes, alter a product's mechanical characteristics, or endanger human lives. The manufacturing industry often relies on modern Quality Control (QC) tools to protect against quality losses, such as those that can occur from an attack. However, cyber-physical attacks can still be designed to avoid detection by traditional QC methods, which suggests a strong need for new and more robust QC tools. Such new tools should be able to prevent, or at least minimize, the effects of cyber-physical attacks on production systems. Unfortunately, little to no research has been done on using QC tools for cyber-physical security of production systems. Hence, the overarching goal of this work is to allow QC systems to be designed and used effectively as a second line of defense, when traditional cyber-security techniques fail and the production system is already breached. To this end, this work focuses on: 1) understanding the role of QC systems in cyber-physical attacks within manufacturing through developing a taxonomy encompassing the different layers involved; 2) identifying existing weaknesses in QC tools and exploring the effects of exploiting them by cyber-physical attacks; and 3) proposing more effective QC tools that can overcome existing weaknesses by introducing randomness to the tools, for better security against cyber-physical attacks in manufacturing. / Ph. D. / The recent technological developments in computers and networking have made systems, such as production systems, more vulnerable to attacks having both cyber and physical components; i.e., to cyber-physical attacks. In manufacturing, such attacks are not only capable of stealing valuable information, but can also destroy equipment, force physical product changes, alter product’s mechanical characteristics, or endanger human lives. Typically, the manufacturing industry have relied on various Quality Control (QC) tools, such as product inspection, to detect the effects caused by these attacks. However, these attacks could be still designed in a way to avoid detection by traditional QC methods, which suggests a need for new and more effective QC tools. Such new tools should be able to prevent, or at least minimize, the effects of these attacks in manufacturing. Unfortunately, almost no research has been done on using QC tools for securing production systems against these malicious attacks. Hence, the overarching goal of this work is to allow QC systems to be designed in a more effective manner to act as a second line of defense, when traditional cyber-security measures and attackers have already accessed the production system. To this end, this work focuses on: 1) understanding the role of QC systems during the attack; 2) identifying existing weaknesses in QC tools and determining the effects of exploiting them by the attack; and 3) proposing more effective QC tools, for better protection against these types of cyber-physical attacks in manufacturing.

NEW IHL FRAMEWORK FOR CYBER WARFARE - ABSTRACT Regarding the increasing number of revealed cyber-attacks aimed at public facilities including the governmental ones by who seems to be other state actors, this thesis aims to reveal the major importance of cyber warfare, point out the fatal vacuum regarding the IHL framework currently in force and suggests its completion by a new IHL convention, which would regulate cyberwarfare in International Armed Conflicts. In order to provide a well-structured and pertinent arguments to support its main points, the thesis uses methods of qualitative analysis of the current IHL sources including international treaties, customary law and work of the main institutions of international justice along with work of judicial scholars and cyber experts. The work contains five main chapters. The first chapter presents the underlining principles of Laws of Wars, including its theory, history and development; and focuses on one of its three main regimes - the International Humanitarian Law. The second part is dedicated to the topic of cyber warfare, defines its scope as computer network attacks, explains their classification system, analyses their effects and provides examples of such attacks. The third chapter focuses on the issue of the current legal vacuum in relation to cyber...

<p>Highly intelligent and technically savvy people are employed to hack data systems throughout the world for prominence or monetary gain. Organizations must combat these criminals with people of equal or greater ability. There have been reports of heightened threats from cyber criminals focusing upon the energy sector, with recent attacks upon natural gas pipelines and payment centers. The Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR) working collaboratively with the Purdue Process Safety and Assurance Center (P2SAC) reached out to the Computer and Information Technology Department to assist with analysis of the current cybersecurity posture of the companies involved with the CISTAR initiative. This cybersecurity research project identifies the overall defensive cyber posture of CISTAR companies and provides recommendations on how to bolster internal cyberspace defenses through the identification of gaps and shortfalls, which aided the compilation of suggestions for improvement. Key findings include the correlation of reduced cybersecurity readiness to companies founded less than 10 years ago, cybersecurity professionals employed by all CISTAR companies and all CISTAR companies implementing basic NIST cybersecurity procedures.</p>

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