The vulnerability of smart contracts has been demonstrated by an increasing number of multi-million exploitation incidents in public blockchains. Several works propose applying runtime verification to protect smart contracts post-deployment. However, none discuss the induced onchain overhead that may preclude its deployment, leaving smart contracts unprotected. A prominent solution to the onchain overhead is outsourcing the analysis off-chain. In this work, we analytically study the potential efficiency of off-chain smart contract runtime verification. We present a generic queueing network model of the off-chain runtime verification and the block generation process. The queuing model approach allows us to efficiently and flexibly capture the non-deterministic behavior of blockchain, estimating the number of transactions in the pool and their corresponding waiting times. We analyze the onchain overhead and evaluate off-chain RV, providing numerical indicators of transaction processing latency and throughput.

Runtime-verification of smart contracts ensures the absence of exploitations within a transaction during execution. It is a crucial security aspect that is often omitted due to its high onchain overhead. The lack of runtime-verification in public blockchains allowed attackers to compromise vulnerable contracts and cause significant monetary losses. Although several runtime protection solutions have been proposed, they do not discuss the onchain overhead limitation, which may hinder their deployment and undermine their effectiveness. To address this problem, we propose an efficient Smart contract Runtime Protection framework, called SRP, that minimizes the onchain burden of runtime-verification by integrating an off-chain mechanism with onchain contract execution. The proposed hybrid architecture is designed to protect already-deployed smart contracts from attacks in real-time while maintaining the throughput of the underlying blockchain. We first present SRP from a design perspective proposing a protocol customized for off-chain runtime-verification interoperability. Then, we evaluate our approach empirically and demonstrate the applicability of SRP using a proof-of-concept implementation on a local instance of the Ethereum network. Our empirical and experimental results indicate the feasibility and efficiency of our approach, where SRP outperforms the onchain-only mechanism in terms of service time and throughput, for increasing workloads.

Abstract Classifying a weapon based on its muzzle blast is a challenging task that has significant applications in various security and military fields. Most of the existing works rely on ad-hoc deployment of spatially diverse microphone sensors to capture multiple replicas of the same gunshot, which enables accurate detection and identification of the acoustic source. However, carefully controlled setups are difficult to obtain in scenarios such as crime scene forensics, making the aforementioned techniques inapplicable and impractical. We introduce a novel technique that requires zero knowledge about the recording setup and is completely agnostic to the relative positions of both the microphone and shooter. Our solution can identify the category, caliber, and model of the gun, reaching over 90% accuracy on a dataset composed of 3655 samples that are extracted from YouTube videos. Our results demonstrate the effectiveness and efficiency of applying Convolutional Neural Network (CNN) in gunshot classification eliminating the need for an ad-hoc setup while significantly improving the classification performance.