Blockchain Papers

Santosh S Doifode, Anand Singh Rajawat

No abstract is available for this record.

Zhe Chen, Xiaohan Zhang, Ning Zhang, Guihua Gu · 7 authors

Cyber–physical systems (CPSs) increasingly rely on complex software components whose vulnerabilities may affect both digital services and physical processes. Fuzzing is a practical technique for discovering such vulnerabilities in CPS-facing parsers, protocol handlers, and edge services. Distributed fuzzing improves throughput, but outsourcing fuzzing tasks to multiple untrusted nodes introduces privacy risks: valuable seeds, especially crash-triggering samples, may reveal vulnerability information before affected users are protected. In this paper, we propose PrivFuzz, a privacy-preserving collaborative fuzzing framework. PrivFuzz allows organizations and individuals to collaborate and receive rewards while keeping fuzzing seeds confidential and enabling controlled encrypted seed reuse among untrusted fuzzing nodes. The key idea is to combine trusted execution environments (TEEs) with blockchain-based smart contracts to support confidentiality and fair reward settlement. We give game-based definitions and reduction-style arguments for seed confidentiality, worker soundness, outsourcer atomicity, and duplicate-claim resistance under an attested execution model. We implement a PrivFuzz prototype and evaluate it on four open-source parsing targets. Separately, native AFL++ sanity checks suggest that CPS-facing industrial protocol parsers such as Modbus and OPC UA fall within the same fuzzable target domain. Demonstrating end-to-end PrivFuzz on CPS control programs is left as future work. Using PrivFuzz, we discovered nine bugs and reported them to the developers.

Nguyen Dinh Dai, Lai Minh Tuan

With the transparency of the Ethereum platform, deployed smart contracts remain permanently public, exposing their virtual machine code to risks such as reverse engineering, control-flow analysis and malicious behavior identification. Although several obfuscation approaches for the EVM have been proposed, existing solutions often suffer from limited resistance against advanced analysis techniques, insufficient structural transformation capability or excessive execution overhead. In this work, we propose a novel obfuscation framework for EVM bytecode that enhances security by combining semantic-aware transformations with control-flow perturbation techniques. The proposed framework significantly increases structural complexity, hinders Control Flow Graph (CFG) recovery and alters discriminative virtual machine code characteristics while preserving the semantic correctness of smart contracts. Experimental results demonstrate that the proposed framework achieves a 100% obfuscation success rate with an average cyclomatic complexity of 90.80. Across all 15 evaluated transformation combinations, the framework introduces an overall mean virtual machine code size increase of 15.99% and a mean gas overhead of 7.04%. Notably, the complete multi-layer pipeline (T1+T2+T3+T4) exhibits overheads of 29.73% for virtual machine code size and 12.58% for gas, which remain acceptable considering the achieved robust resistance against reverse engineering and automated static analysis.

Staley Ian

Pipelines that pair a large language model with a static analyzer, feeding findings back as repair instructions, appear throughout recent smart contract repair research. They rest on a rarely examined assumption: that the analyzer output serving as the oracle faithfully records what the analyzer found. I report three ways that assumption fails, identified during a four-contract instrument-validation exercise preceding a planned repair study. First, Mythril v0.24.8 can exit without reaching the analysis phase while returning exit status zero, empty standard error, and a findings array byte-identical to that of a genuinely clean scan; the failure is reported in a sibling JSON field that finding-extraction code has no reason to read. Second, 12 of 23 Slither findings in my validation set fell outside the high, medium, and low impact bands, so an unfiltered count measures a composite whose components may not behave alike under repair. Third, keying finding identity on source location breaks across repair rounds. On the one contract carried through three rounds, location-based keying inflated resolved findings from 7 to 12 and introduced findings from 2 to 7. The underlying instability is established in the warning-tracking literature; my contribution is its consequence for repair metrics, where it biases both transition counts upward and can confound comparison between methods producing differently sized .patches. I separately report an executed exploit showing a specification-level authorization defect that produced no high or medium impact finding. I propose calibration procedures for each hazard and release the harness, contracts, and raw analyzer output at doi:10.5281/zenodo.21586404.

Aman Sharma, Vishvesh Revoori

Over the past few years, Wireless Sensor Networks (WSNs) have been increasingly deployed for numerous sensing and monitoring purposes in environmental monitoring, industrial automation, health monitoring, military surveillance, smart agriculture and disaster management among others. The inherent limitations in terms of processing power, memory, communication bandwidth and energy of sensor nodes make WSNs highly susceptible to malware attacks. A wide variety of malware such as sensor network worms, Trojans, viruses, botnets and ransomware can easily propagate in a network through inter node communication. Such malware can cause serious damage to communication, compromise sensitive data, consume energy of the infected nodes thereby reducing the lifetime of network among others. In the last decade, numerous approaches have been proposed for the detection of malware infecting sensor nodes. These approaches range from traditional signature-based detection and behavior-based detection to more advanced approaches such as machine learning (ML)-based, deep learning (DL) -based, blockchain-based, trust management-based and federated learning-based detection. Most of the existing approaches for malware detection in WSNs have been designed to work on WSNs and have not been tested on real scenarios. Most of the approaches have their own strengths and weaknesses and the most suitable approach for a given application depends on various factors. In this paper, we present a comprehensive review of approaches for the detection of malware infecting sensor nodes in WSNs. We present a taxonomy of reviewed approaches for detection of malware. We also present a discussion on approaches for modeling malware propagation in a WSN as well as review on various categories of malware that have been designed to attack sensor nodes in WSNs along with detection frameworks for different categories of malware. We also present a comparative study of approaches used for the detection of malware in WSNs on the basis of various parameters such as detection accuracy, computational complexity, energy efficiency, scalability, detection latency and deployability. The review and taxonomy presented in this paper will be highly beneficial for researchers and practitioners designing approaches and systems for the detection of malware in WSNs. Various open research challenges in this area have also been discussed in this paper including detection of zero-day malware, designing of intelligent models to be light enough to be deployed on sensor nodes, use of explainable artificial intelligence for detection of malware in WSNs, designing approaches for privacy-preserving collaborative learning in WSNs and designing adaptive security approaches for WSNs.