The popularity of blockchain technology led to the development of many web platforms with different functionalities. Ethereum, a decentralized, open-source blockchain featuring smart contracts, introduces an interesting ecosystem of human users and autonomous agents (the contracts). It is the most actively used blockchain platform, hosting ether, the second largest cryptocurrency by market capitalization, as its native store of value. The Ethereum blockchain contains a vast amount of user-to-user, user-to-contract, contract-to -user, and contract-to-contract interactions that can be modeled as complex networks. To mine these interactions as graphs through a preferred analytics toolbox, an end-user has to extract, transform, and load (ETL) the required data into the desired network format. However, it is costly and time-consuming to manage the ETL pipeline for the massive and complex blockchain data. To support research in this domain, we develop an end-to-end, automated tool - EtherNet, which performs ETL tasks from a single source of truth (Google BigQuery), and provides graph equivalent representations for visualization and mining on the entire Ethereum blockchain network.

With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.

Blockchains are increasingly becoming popular due to the prevalence of cryptocurrencies and decentralized applications. Ethereum is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a global state that cannot be manipulated or changed. Ethereum blockchain introduces a novel ecosystem of human users and autonomous agents (smart contracts). In this network, we are interested in all possible interactions: user-to-user, user-to-contract, contract-to-user, and contract-to-contract. This requires us to construct interaction networks from the entire Ethereum blockchain data, where vertices are accounts (users, contracts) and arcs denote interactions. Our analyses on the networks reveal new insights by combining information from the four networks. We perform an in-depth study of these networks based on several graph properties consisting of both local and global properties, discuss their similarities and differences with social networks and the Web, draw interesting conclusions, and highlight important, future research directions.

While machine learning and deep neural networks in particular, have undergone massive progress in the past years, this ubiquitous paradigm faces a relatively newly discovered challenge, adversarial attacks. An adversary can leverage a plethora of attacking algorithms to severely reduce the performance of existing models, therefore threatening the use of AI in many safety-critical applications. Several attempts have been made to try and understand the root cause behind the generation of adversarial examples. In this paper, we try to relate the geometry of the high-dimensional space in which the model operates and optimizes, and the properties and problems therein, to such adversarial attacks. We present the mathematical background, the intuition behind the existence of adversarial examples and substantiate them with empirical results from our experiments.

Offset codebook or${\mathsf {OCB}}$mode is a popular block cipher mode of operation for authenticated encryption. The latest version of this cipher, called${\mathsf {OCB3}}$, is one of the finalists in CAESAR. In this paper, we explore the scope of random read access and out-of-sequence decryption in${\mathsf {OCB}}$. We observe that the current versions of${\mathsf {OCB}}$are inefficient in this respect owing to the ineptness of the underlying mask generating function (MGF). We propose new candidates for MGF based on${\mathsf {AES}}$round function, which are efficient in direct computation and provide comparable performance in the usual setting. Our schemes are not the obvious choices for MGF in conventional sense as they do not have optimal almost XOR universal (AXU) bound. In existing${\mathsf {OCB}}$designs, the MGFs are required to have$ 2^{-n} $, i.e. optimal, AXU bound in order to upper bound the distinguishing advantage to$ O(\sigma ^{2}/2^{n}) $, where$ n $is the block size of the underlying block cipher and$ \sigma $is the total number of blocks among all queries. We find this specific requirement too restrictive. We abstract the${\mathsf {OCB}}$design, termed as${\mathsf {GOCB}}$, to look into the universal notion required from the underlying MGF. We propose a relaxed notion of AXU, called locally imperfect XOR universal (LIXU) hash, which can be of independent interest. Using LIXU as the underlying MGF, we recover reasonable security bounds for our schemes.

RC4 is the most popular stream cipher in the domain of cryptology. In this paper, we present a systematic study of the hardware implementation of RC4, and propose the fastest known architecture for the cipher. We combine the ideas of hardware pipeline and loop unrolling to design an architecture that produces 2 RC4 keystream bytes per clock cycle. We have optimized and implemented our proposed design using VHDL description, synthesized with 130, 90, and 65 nm fabrication technologies at clock frequencies 625 MHz, 1.37 GHz, and 1.92 GHz, respectively, to obtain a final RC4 keystream throughput of 10, 21.92, and 30.72 Gbps in the respective technologies.