Smartphones form an important source of trust in modern computing. But, while their mobility is convenient, smartphones can be stolen or seized, allowing an adversary to impersonate the user in their digital life: accessing the user's services and decrypting their sensitive files. With this in mind, we build SocIoTy, which leverages a user's existing IoT devices to add a context-sensitive layer of security for non-expert users. Instead of assuming the existence of dedicated hardware, SocIoTy re-uses the devices of a user's smart home to provide cryptographic services, which we term at-home cryptography. We show that at-home cryptography can be built from simple cryptographic primitives, and that our SocIoTy solution is able to provide useful functionalities, like two-factor authentication (2FA) and secure file storage, while protecting against powerful adversaries in this setting. We implement and evaluate SocIoTy in real-world use cases and provide microbenchmarks for individual cryptographic operations on realistic models of IoT devices. We also provide full benchmarks of an end-to-end deployment on a simulated smart home, using a smartphone and 9 IoT devices to generate and display 2FA one-time passwords in less than 200 milliseconds. SocIoTy is able to provide strong, practical cryptography while binding its execution to the smart home itself, all without requiring additional hardware.

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, 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.