Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need for capturing dynamics for the prediction on a toy data set created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-ofthe-art methods on two real-world data sets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.

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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.

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The next generation of smart manufacturing systems will incorporate various recent enabling technologies. These technologies will aid in ushering the era of the fourth industrial revolution. They will make the supply chain and the product lifecycle of the manufacturing system efficient, decentralized, and well-connected. However, these technologies have various security issues and, when integrated in the supply chain and the product lifecycle of manufacturing systems, can pose various challenges for maintaining the security requirements such as confidentiality, integrity, and availability. In this paper, we will present the various trends and advances in the security of the supply chain and product lifecycle of the manufacturing system while highlighting the roles played by the major enabling components of Industry 4.0.