A review study on blockchain-based IoT security and forensics

Kamal, Randa; Hemdan, Ezz El‐Din; El‐Fishway, Nawal

doi:10.1007/s11042-021-11350-9

Cited by 24 publications

(5 citation statements)

References 114 publications

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“…The intersection of Internet of Things (IoT) and blockchain technology, exploring how blockchain can play a crucial role in ensuring the reliability and legal admissibility of digital forensic processes within the IoT domain, is also an important aspect. Studies such as [16][17][18][19][20][21][22][23][24][25][26][27][28] dove into the realm of the Internet of Things (IoT) and how blockchain can assist in ensuring the reliability of digital forensic processes within this domain. Brotsis et al [16] specifically targeted smart home environments, using blockchain to ensure the legal admissibility of evidence.…”

Section: Blockchain In Iot Digital Forensicsmentioning

confidence: 99%

“…Brotsis et al [16] specifically targeted smart home environments, using blockchain to ensure the legal admissibility of evidence. Liao et al [17] and Kamal et al [18] provided comprehensive overviews of the intersection of IoT, forensics, and blockchain, highlighting security challenges and potential future directions. Li et al [19] presented the IoT Forensic Chain, emphasizing the importance of a transparent audit trail in forensic investigations for IoT.…”

Section: Blockchain In Iot Digital Forensicsmentioning

confidence: 99%

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ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

Alqahtany,

Syed

2024

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In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations.

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Section: Blockchain In Iot Digital Forensicsmentioning

confidence: 99%

Section: Blockchain In Iot Digital Forensicsmentioning

confidence: 99%

ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

Alqahtany,

Syed

2024

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“…Beyond specific solutions, blockchain is progressively being integrated into comprehensive healthcare systems. Kamal et al [52] described "Care4U", an integrated healthcare system founded on blockchain. Such integrations demonstrate the technology's capability to holistically address healthcare challenges, from patient data management to treatment traceability.…”

Section: Blockchain In Healthcare and Iomtmentioning

confidence: 99%

Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning

Khan,

Abaoud

2023

IEEE Access

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The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As with all technological progressions, IoMT introduces a suite of complex challenges, predominantly centered on security. In particular, ensuring the integrity, confidentiality, and availability of health data in real-time communication stands paramount, given the sensitivity of the information and the ramifications of potential breaches or misuse. In light of these challenges, existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple with comprehensive anomaly detection, effective resistance to replay attacks, and robust protection against threats like man-in-the-middle attacks, eavesdropping, data tampering, and identity spoofing. The proposed framework integrates state-of-the-art encryption techniques, cuttingedge pattern recognition modules, and adaptive learning mechanisms. These components collaboratively ensure data integrity during transmission, provide robust resistance against conventional and novel attack vectors, and adapt to evolving threats through continuous learning. Moreover, the framework incorporates sophisticated checksum techniques and advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements in anomaly detection and attack resistance metrics, consistently outperforming benchmark solutions like MRMS and BACKM-EHA.

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“…3 Security is vital in different applications such as cloud computing, Internet-of-things (IoT), and medical systems. [4][5][6][7][8][9][10][11] Imbalanced data fed to IDS within SDN environments is a significant challenge that security professionals and data scientists need to address. In the realm of cybersecurity, intrusion detection plays a critical role in identifying and mitigating malicious activities within a network.…”

Section: Introductionmentioning

confidence: 99%

“…This characteristic allows sophisticated tasks to be implemented, such as flexible system management, less human intervention, and low cost of implementation 3 . Security is vital in different applications such as cloud computing, Internet‐of‐things (IoT), and medical systems 4‐11 …”

Section: Introductionmentioning

confidence: 99%

Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods

Hassan,

Hemdan,

El‐Shafai

et al. 2023

Security and Privacy

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Software‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%.

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A review study on blockchain-based IoT security and forensics

Cited by 24 publications

References 114 publications

ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning

Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods

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