Synthetic biology is developing into a promising science and engineering field. One of the enabling technologies for this field is the DNA synthesizer. It allows researchers to custom-build sequences of oligonucleotides (short DNA strands) using the nucleobases: Adenine (A), Guanine (G), Cytosine (C), and Thymine (T). Incorporating these sequences into organisms can result in improved disease resistance and lifespan for plants, animals, and humans. Hence, many laboratories spend large amounts of capital researching and developing unique sequences of oligonucleotides. However, these DNA synthesizers are fully automated systems with cyber-domain processes and physical domain components. Hence, they may be prone to security breaches like any other computing system. In our work, we present a novel acoustic side-channel attack methodology which can be used on DNA synthesizers to breach their confidentiality and steal valuable oligonucleotide sequences. Our proposed attack methodology achieves an average accuracy of 88.07% in predicting each base and is able to reconstruct short sequences with 100% accuracy by making less than 21 guesses out of 4 15 possibilities. We evaluate our attack against the effects of the microphone's distance from the DNA synthesizer and show that our attack methodology can achieve over 80% accuracy when the microphone is placed as far as 0.7 meters from the DNA synthesizer despite the presence of common room noise. In addition, we reconstruct DNA sequences to show how effectively an attacker with biomedical-domain knowledge would be able to derive the intended functionality of the sequence using the proposed attack methodology. To the best of our knowledge, this is the first methodology that highlights the possibility of such an attack on systems used to synthesize DNA molecules.
This paper presents a novel multi-modal sabotage attack detection system for Additive Manufacturing (AM) machines. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques. Besides, we analyze the value of each side-channel for performing attack detection in terms of mutual information shared with the machine control parameters. We evaluate our system on real-world test cases and achieve an attack detection accuracy of 98.15%. AM, or 3D Printing, is seeing practical use for the rapid prototyping and production of industrial parts. The digitization of such systems not only makes AM a crucial technology in Industry 4.0 but also presents a broad attack surface that is vulnerable to kinetic cyberattacks. In the field of AM security, sabotage attacks are cyberattacks that introduce inconspicuous defects to a manufactured component at any specific process of the AM digital process chain, resulting in the compromise of the component's structural integrity and load-bearing capabilities. Defense mechanisms that detect such attacks using side-channel analysis have been studied. However, most current works focus on modeling the state of AM systems using a single sidechannel, thus limiting their effectiveness at attack detection. In this paper, we demonstrate the value of a multi-modal sabotage attack detection system in comparison to uni-modal techniques. INDEX TERMS Additive-manufacturing, cyber-physical systems security, side-channel analysis, 3D-printer security, sabotage attack.
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