Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Conference Object
    Citation - Scopus: 3
    Ethereum Blockchain Smart Contract Vulnerability Detection Using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Demir,H.O.; Parlat,S.Z.; Gumus,A.
    Blockchain technology, employing advanced cryptography, stands as an optimal means to establish trust among unfamiliar online counterparts. It facilitates secure transactions and consensus among participants. Ethereum, a prominent blockchain network, extends this utility by introducing smart contracts. These are predefined programs containing data and methods for execution. Once deployed, these contracts remain unalterable due to blockchain's immutable nature. However, unlike conventional software that can be readily patched, they may harbor vulnerabilities. Smart contracts operate with the Ethereum cryptocurrency Ether, rendering fixes intricate and economically impactful. Static analyzers exist to spot vulnerabilities in smart contacts during development, but they are time-intensive. We propose a machine learning-based approach for detecting reentrancy vulnerabilities in smart contracts. Our system comprises three components: data preparation, Op2Vec, and an LSTM model. We collected 30,000 smart contracts, dividing them into two sets of 15,000 each for Op2Vec generation and LSTM training, respectively. We mapped opcode keywords to vector representations using a Skip-Gram algorithm, resulting in a 100-dimensional dictionary with 72 unique opcodes. Labeling was done using the Slither static analyzer, with 116 contracts identified as vulnerable and an additional 132 clean contracts for dataset balance. A Bidirectional LSTM (Bi-LSTM) model was devised by employing assembly data to detect flaws. The developed Bi-LSTM model demonstrated promise in reentrancy vulnerability detection, achieving a 96% accuracy rate in testing and reducing the analysis time to less than a fifth of that required by static analyzers. The codes and data are shared on GitHub as an open-source software package in a way that benefits everyone interested: https://github.com/miralabai/blockchain-vulnerability-detection. © 2023 IEEE.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 3
    Secure Iot Update Using Blockchain
    (IEEE, 2021) Kaptan, Melike; Tomur, Emrah; Ayav, Tolga; Erten, Yusuf Murat
    In this study a platform is devised to send automatic remote updates for embedded devices. In this scenario there are Original Equipment Manufacturers (OEMs), Software suppliers, blockchain nodes, Gateways and embedded devices. OEMs and software suppliers are there to keep their software on Inter Planetary File System (IPFS) and send the meta-data and hashes of their software to the blockchain nodes in order to keep this information distributed and ready to be requested and used. There are also gateways which are the members of the blockchain and the IPFS network. Gateways are responsible for asking for a specific update for specific devices from IPFS database using the meta-data kept on the blockchain, and they will send those hashed secure updates to the devices. In order to provide a traceable data keeping platform, gateway update operations are handled as transactions in a second blockchain network which is the clockchain of the gateways. The system was implemented as of the two separate blockchain networks and it has been shown that, despite the calculation overhead of the member devices, by separating the functions between the two blockchain networks a more reliable and secure platform can be achieved.