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Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Quite a few practitioners have devoted themselves to developing tools for detecting bugs in smart contracts. One line of efforts revolve around static analysis techniques, which heavily suffer from high false-positive rates. Another line of works concentrate on fuzzing techniques. Unfortunately, current fuzzing approaches for smart contracts tend to conduct fuzzing starting from the initial state of the contract, which expends too much energy revolving around the initial state and thus is usually unable to unearth bugs triggered by other states. Moreover, most existing methods treat each branch equally, failing to take care of the branches that are rare or more likely to possess bugs. This might lead to resources wasted on normal branches. In this paper, we try to tackle these challenges from three aspects: (1) In generating function invocation sequences, we explicitly consider data dependencies between functions to facilitate exploring richer states. We further prolong a function invocation sequence S1 by appending a new sequence S2, so that S2 can start fuzzing from states that are different from the initial state. (2) We incorporate a branch distance-based measure to evolve test cases iteratively towards a target branch. (3) We engage a branch search algorithm to discover rare and vulnerable branches, and design an energy allocation mechanism to take care of exercising these crucial branches. We implement IR-Fuzz and extensively evaluate it over 12K real-world contracts. Empirical results show that: (i) IR-Fuzz achieves 28% higher branch coverage than state-of-the-art fuzzing approaches, and (ii) IR-Fuzz detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods., Comment: This paper has been accepted by IEEE Transactions On Information Forensics And Security (TIFS 2022)

Recent years have witnessed explosive growth in blockchain smart contract applications. As smart contracts become increasingly popular and carry trillion dollars worth of digital assets, they become more of an appealing target for attackers, who have exploited vulnerabilities in smart contracts to cause catastrophic economic losses. Notwithstanding a proliferation of work that has been developed to detect an impressive list of vulnerabilities, the bad randomness vulnerability is overlooked by many existing tools. In this paper, we make the first attempt to provide a systematic analysis of random numbers in Ethereum smart contracts, by investigating the principles behind pseudo-random number generation and organizing them into a taxonomy. We also lucubrate various attacks against bad random numbers and group them into four categories. Furthermore, we present RNVulDet - a tool that incorporates taint analysis techniques to automatically identify bad randomness vulnerabilities and detect corresponding attack transactions. To extensively verify the effectiveness of RNVulDet, we construct three new datasets: i) 34 well-known contracts that are reported to possess bad randomness vulnerabilities, ii) 214 popular contracts that have been rigorously audited before launch and are regarded as free of bad randomness vulnerabilities, and iii) a dataset consisting of 47,668 smart contracts and 49,951 suspicious transactions. We compare RNVulDet with three state-of-the-art smart contract vulnerability detectors, and our tool significantly outperforms them. Meanwhile, RNVulDet spends 2.98s per contract on average, in most cases orders-of-magnitude faster than other tools. RNVulDet successfully reveals 44,264 attack transactions. Our implementation and datasets are released, hoping to inspire others., This is the preprint of the paper that has been accepted by IEEE Transactions on Software Engineering (TSE)

Demand response (DR) has been widely adopted as a strategic plan of the electricity market in maintaining power grid reliability, sustainability, and stability. In a typical emergency DR (EDR) that arises in colocation data centers, participating tenants can reduce their power consumption when the supply of electricity is a shortage and be rewarded with financial compensation. In this paper, we study a mechanism design problem of motivating tenants for colocation EDR (MEDR). To solve the MEDR problem, we present a truthful Fully Polynomial-Time Approximation Scheme (FPTAS) which is theoretically proved deterministic, truthful and near-optimal, and can be approximated within $1 + \epsilon$ 1 + e for any given $\epsilon > 0$ e > 0 , while the running time is in the polynomial of the number of tenants $n$ n and $1/\epsilon$ 1 / e . To speed up the calculation of the payments, we further study the Vickrey-Clarke-Groves (VCG) based mechanism. Moreover, we build a MEDR auction system (MEDRAS) and implement all mechanism algorithms for a colocation data center. Comprehensive and detailed experiments have been implemented to validate the efficiency of our proposed mechanisms.