2019
DOI: 10.1109/tcss.2019.2927431
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Blockchain-Based Digital Forensics Investigation Framework in the Internet of Things and Social Systems

Abstract: The decentralised nature of blockchain technologies can well match the needs of integrity and provenances of evidences collecting in digital forensics across jurisdictional borders. In this work, a novel blockchain based digital forensics investigation framework in the Internet of Things (IoT) and social systems environment is proposed, which can provide proof of existence and privacy preservation for evidence items examination. To implement such features, we present a block enabled forensics framework for IoT… Show more

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Cited by 84 publications
(28 citation statements)
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References 38 publications
(44 reference statements)
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“…Deep learning (DL) is considered to be the founding pillar of modern artificial intelligence [8]. DL has been widely used in computer vision, speech recognition, robotics, and many other application areas.…”
Section: Behaviour Modelling and Analysis Of Iot Using Deep Learningmentioning
confidence: 99%
“…Deep learning (DL) is considered to be the founding pillar of modern artificial intelligence [8]. DL has been widely used in computer vision, speech recognition, robotics, and many other application areas.…”
Section: Behaviour Modelling and Analysis Of Iot Using Deep Learningmentioning
confidence: 99%
“…Firstly, two parameters are introduced into the scheme, which can effectively resist the weak randomness attack of Bitcoin ECDSA; Secondly, the scheme uses the Hamming weight of Hash function instead of Hash value to sign, which avoids the inverse operation in the signing and verification stage. Li et al [26] proposed a new digital forensics (DF) investigation framework based on blockchain in the environment of Internet of things and social systems, which can provide proof of existence and privacy protection for evidence item inspection. Based on the Internet of things forensics chain (IoTFC), the framework can provide good authenticity, invariance, traceability, flexibility and distributed trust forensics investigation between evidence obligees and reviewers.…”
Section: Blockchain and Digital Twins Techniquementioning
confidence: 99%
“…Data according to the weights and bias, in OLSR routing protocol, "sends HELLO packets" subroutines in rebuilding the single hidden layer neural network, all nodes and the entire network movement speed data as the input parameters of neural network, and can output the HELLO broadcast cycle under this movement speed which will have a kind of routing performance, including energy consumption, AEED, PDR, jitter, throughput, etc. By combining the above information and making use of multiattribute decision, the optimal HELLO broadcast cycle is obtained, and the result is taken as the time of the next HELLO package broadcast [18][19][20].…”
Section: Mac Layer Energy Consumption and Routing Protocol Algorithm mentioning
confidence: 99%