Industry 4.0 encourages the integration of intelligent technology with manufacturing systems. Among them, additive manufacturing (AM) is critical to solving some of the fourth industrial revolution's most pressing needs. With AM gaining popularity, the need for the validation of 3D designs grows. In this paper, we introduce a novel concept of a distributed marketplace that will support the attestation of 3D printing designs. We build a mathematical trust model that ensures truthfulness among rational, selfish, and independent agents, which is based on a reward/penalty system. The payment for participating in the evaluation is calculated by factoring in agents' reputations and peer feedback. Moreover, we describe the architecture and the implementation of the trust model on the blockchain using smart contracts for the creation of a distributed marketplace. Our model relies both on theoretical and practical best practices to create a unique platform that elicits effort and truthfulness from the participants. Finally, we present a performance evaluation and cost analysis of the proposed architecture to evaluate scalability and financial viability.

Outsourced Additive Manufacturing (AM) exposes sensitive design data to external malicious actors. Even with end-to-end encryption between the design owner and 3D-printer, side-channel attacks can be used to bypass cyber-security measures and obtain the underlying design. In this paper, we develop a method based on the power side-channel that enables accurate design reconstruction in the face of full encryption measures without any prior knowledge of the design. Our evaluation on a Fused Deposition Modeling (FDM) 3D Printer has shown 99 % accuracy in reconstruction, a significant improvement on the state of the art. This approach demonstrates the futility of pure cyber-security measures applied to Additive Manufacturing.

Additive manufacturing (AM), a.k.a. 3D printing is increasingly used to manufacture functional parts of safety-critical systems. The AM's dependence on computerization raises the concern that the AM process can be tampered with, and a part's mechanical properties sabotaged. To address this threat, we propose a novel approach for detecting sabotage attacks based on trusted monitoring of the current delivered to each printer motor. The proposed approach offers numerous advantages: 1) it is non-invasive in a time-critical process, 2) it can be retrofitted in legacy systems, and 3) it can be air-gapped from the computerized components of the AM process, making simultaneous compromise more difficult. We evaluated the approach on five categories of toolpath command-level manipulations that impact the geometry of the 3D printed object. Our evaluation showed that all but one tested category of attacks can be reliably detected, even if a single toolpath command is modified.

Additive manufacturing, also referred to as 3D printing, has become viable for manufacturing functional parts. For example, the U.S. Federal Aviation Administration recently approved General Electric jet engine fuel nozzles that are produced by additive manufacturing. BecUniversity of South Alabama, Mobile, Alabama with cyber technology, a number of security concerns have been raised. This chapter specifically considers attacks that deliberately sabotage the mechanical properties of functional parts produced by additive manufacturing; the feasibility of these attacks has already been discussed in the literature. Investments in security measures directly depend on cost-benefit analyses conducted by the participants involved in additive manufacturing processes. This chapter discusses the entities that can be considered to be financially liable in the event of a successful sabotage attack. The analysis employs a model that distinguishes between the levels at which the additive manufacturing process has been sabotaged. Specifically, it differentiates between the additive manufacturing service provider and the various commodity suppliers. For each possible combination of injured party and level of attack, the involved parties that may face liability exposure are identified. This is accomplished by analyzing the necessary components that establish liability. The analysis reveals that liability potential exists at all levels of the additive manufacturing process in the event of a sabotage attack. For this reason, it is imperative that the involved actors conduct or re-evaluate their cost-benefit analyses and invest in security measures.

Additive manufacturing (AM) is a rapidly growing, multibillion dollar industry. AM is increasingly being used to manufacture functional parts, including components of safety critical systems in aerospace, automotive, and other industries. This makes AM an attractive attack target. AM Security is a fairly new field of research that addresses this novel threat.This paper serves dual purposes: For researchers just entering AM security, we provide an in-depth introduction to this highly multi-disciplinary research field. And, for active researchers in the field, this paper provides a comprehensive, structured survey of the state of the art as well as our proposal for attack taxonomies.

Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer’s stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes, and different audio recorder positions.

Additive manufacturing involves a new class of cyber-physical systems that manufacture 3D objects incrementally by depositing and fusing together thin layers of source material. In 2015, the global additive manufacturing industry had 5.165 billion in revenue, with 32.5

Additive Manufacturing (AM), a.k.a. 3D Printing, is increasingly used to manufacture functional parts of safety-critical systems. AM's dependence on computerization raises the concern that the AM process can be tampered with, and a part's mechanical properties sabotaged. This can lead to the destruction of a system employing the sabotaged part, causing loss of life, financial damage, and reputation loss. To address this threat, we propose a novel approach for detecting sabotage attacks. Our approach is based on continuous monitoring of the current delivered to all actuators during the manufacturing process and detection of deviations from a provable benign process. The proposed approach has numerous advantages: (i) it is non-invasive in a time-critical process, (ii) it can be retrofitted in legacy systems, and (iii) it is airgapped from the computerized components of the AM process, preventing simultaneous compromise. Evaluation on a desktop 3D Printer detects all attacks involving a modification of X or Y motor movement, with false positives at 0%.

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 which computes the similarity between other print recordings and the master audio fingerprint. We then evaluate the deviation due to tampering, focusing on the detection of minimal tampering primitives. By detecting the deviation at the time of its occurrence, we can stop the printing process for compromised objects, thus save time and prevent material waste. We discuss impacts on the method by aspects like background noise, or different audio recorder positions. We further outline our vision with use cases incorporating our approach.

Additive manufacturing (AM), or 3D printing, is an emerging manufacturing technology that is expected to have far-reaching socioeconomic, environmental, and geopolitical implications. As use of this technology increases, it will become more common to produce functional parts, including components for safety-critical systems. AM's dependence on computerization raises the concern that the manufactured part's quality can be compromised by sabotage. This paper demonstrates the validity of this concern, as we present the very first full chain of attack involving AM, beginning with a cyber attack aimed at compromising a benign AM component, continuing with malicious modification of a manufactured object's blueprint, leading to the sabotage of the manufactured functional part, and resulting in the physical destruction of a cyber-physical system that employs this part. The contributions of this paper are as follows. We propose a systematic approach to identify opportunities for an attack involving AM that enables an adversary to achieve his/her goals. Then we propose a methodology to assess the level of difficulty of an attack, thus enabling differentiation between possible attack chains. Finally, to demonstrate the experimental proof for the entire attack chain, we sabotage the 3D printed propeller of a quadcopter UAV, causing the quadcopter to literally fall from the sky.

We consider the following dynamic load balancing game: Given an initial assignment of jobs to identical parallel machines, the system is modified; specifically, some machines are added or removed. Each job's cost is the load on the machine it is assigned to; thus, when machines are added, jobs have an incentive to migrate to the new unloaded machines. When machines are removed, the jobs assigned to them must be reassigned. Consequently, other jobs might also benefit from migrations. In our job-extension penalty model, for a given extension parameter δ≥0, if the machine on which a job is assigned to in the modified schedule is different from its initial machine, then the job's processing time is extended by δ. We provide answers to the basic questions arising in this model. Namely, the existence and calculation of a Nash Equilibrium and a Strong Nash Equilibrium, and their inefficiency compared to an optimal schedule. Our results show that the existence of job-migration penalties might lead to poor stable schedules; however, if the modification is a result of a sequence of improvement steps or, better, if the sequence of improvement steps can be supervised in some way (by forcing the jobs to play in a specific order) then any stable modified schedule approximates well an optimal one. Our work adds two realistic considerations to the study of job scheduling games: the analysis of the common situation in which systems are upgraded or suffer from failures, and the practical fact according to which job migrations are associated with a cost.