The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, that reduces inventory and the lead-time for delivering custom, batch-of-one products based on achieving convergence of Additive, Subtractive, and Hybrid manufacturing machines, Automation and Robotic Systems, Sensors, Computing, and Communication Networks, Artificial Intelligence, and Big Data. A DM system consists of embedded electronics, sensors, actuators, control software, and inter-connectivity to enable the machines and the components within them to exchange data with other machines, components therein, the plant operators, the inventory managers, and customers. This paper presents the cybersecurity risks in the emerging DM context, assesses the impact on manufacturing, and identifies approaches to secure DM.
A thorough and systematic understanding of different elements of cyberattacks is essential for developing the necessary tools to prevent, detect, diagnose, and mitigate cyberattacks in manufacturing systems. In response, researchers have proposed several attack taxonomies as methods for recognizing and categorizing various cyberattack attributes. However, those taxonomies cover selected attack attributes depending on the research focus, sometimes accompanied by inconsistent naming and definitions. These seemingly different taxonomies often overlap and can complement each other to create a comp…
Cybersecurity testing of automotive systems has become a practical necessity, with the wide adoption of advanced driving assistance functions and vehicular communications. These functionalities require the integration of information and communication technologies that not only allow for a plethora of on-the-fly configuration abilities, but also provide a huge surface for attacks. Theses circumstances have also been recognized by standardization and regulation bodies, making the need for not only proper cybersecurity engineering but also proving the effectiveness of security measures by verific…
The UK Critical National Infrastructure is critically dependent on digital technologies that provide communications, monitoring, control, and decision-support functionalities. Digital technologies are progressively enhancing efficiency, reliability, and availability of infrastructure, and enabling new benefits not previously available. These benefits can introduce vulnerabilities through the connectivity enabled by the digital systems, thus, making it easier for would-be attackers, who frequently use socio-technical approaches, exploiting humans-in-the-loop to break in and sabotage an organiza…
Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologies such as Additive Manufacturing (AM)/3D printing, as well as the Industrial Internet of Things (IIoT) and the broader trend toward Industry 4.0. However, this adoption is not without risks: with a growing complexity and connectivity, so too grows the cyber-physical attack surface. Here, malicious actors might seek to steal sensitive information or sabotage products or production lines, causing financial and reputat…
Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework pr…
Additive Manufacturing (AM, or 3D printing) is a novel manufacturing technology that is being adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper we address sabotage via tampering with the 3D printing process. We present an object verification system using side-channel emanations: sound generated by onboard stepper motors. The contributions of this paper are following. We present two algorithms: one which generates a master audio fingerprint for the unmodified printing process, and one whic…
Cyber-Physical Additive Manufacturing (AM) constructs a physical 3D object layer-by-layer according to its digital representation and has been vastly applied to fast prototyping and the manufacturing of functional end-products across fields. The computerization of traditional production processes propels these technological advancements; however, this also introduces new vulnerabilities, necessitating the study of cyberattacks on these systems. The AM Sabotage Attack is one kind of kinetic cyberattack that originates from the cyber domain and can eventually lead to physical damage, injury, or …
The automotive industry is experiencing a serious transformation due to a digitalisation process and the transition to the new paradigm of Mobility-as-a-Service. The next-generation vehicles are going to be very complex cyber-physical systems, whose design must be reinvented to fulfil the increasing demand of smart services, both for safety and entertainment purposes, causing the manufacturers' model to converge towards that of IT companies. Connected cars and autonomous driving are the preeminent factors that drive along this route, and they cause the necessity of a new design to address the …
Increasing usage of Digital Manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g. g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufac…
Agentic Artificial Intelligence for Ethical Cybersecurity in Uganda: A Reinforcement Learning Framework for Threat Detection in Resource-Constrained Environments
The recent passing of the Cybersecurity Information Sharing Act of 2015 introduces a new framework for information sharing between private and US government entities with the expressed intent to identify cybersecurity threats. This is the latest in a series of similar bills that have been introduced to Congress over the last several years. While each of the previous standalone bills were defeated following widespread public resistance, the latest edition was included as an amendment to the United States' 2016 spending bill. This means that any dissenting congressmen unwilling to pass the spend…
GuardSec: A Multi-Modal Web Platform for Real-Time Digital Fraud Detection, Entity Verification, and Connection Security Analysis in the African Context
AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent
School of Computer Science, University of Galway（戈尔韦大学计算机科学学院）；Faculty of Information Technology and Computer Science, University of Central Punjab（旁遮普中央大学信息技术与计算机科学学院）
As machine learning and cybersecurity continue to explode in the context of the digital ecosystem, the complexity of cybersecurity data combined with complicated and evasive machine learning algorithms leads to vast difficulties in designing an end to end system for intelligent, automatic anomaly classification. On the other hand, traditional systems use elementary statistics techniques and are often inaccurate, leading to weak centralized data analysis platforms. In this paper, we propose a novel system that addresses these two problems, titled CAMLPAD, for Cybersecurity Autonomous Machine Le…
A Survey of Social Cybersecurity: Techniques for Attack Detection, Evaluations, Challenges, and Future Prospects
Saginaw Valley State University（萨吉诺谷州立大学）；Howard University（霍华德大学）；University of Cincinnati（辛辛那提大学）；University of North Texas（北得克萨斯大学）；Qatar University（卡塔尔大学）；Carnegie Mellon University（卡内基梅隆大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
