A blockchain security module for brain-computer interface (BCI) with Multimedia Life Cycle Framework (MLCF)

Khan, Abdullah Ayub; Laghari, Asif Ali; Shaikh, Aftab Ahmed; Dootio, Mazhar Ali; Estrela, Vania V.; Lopes, Ricardo Tadeu

doi:10.1016/j.neuri.2021.100030

Cited by 35 publications

(15 citation statements)

References 87 publications

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“…The KDDCup 99 dataset is one of the popular datasets in IoT with cybersecurity [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47]. This dataset provides labelled and unlabeled training and testing data, and it originated from the evaluation program DARPA98 IDS with corresponds to seven and two weeks [33], [41], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74]. The UNSW-NB15 dataset was created by perfectStorm (IXIA) in collaboration with the UNSW Cyber Range Lab to generate moderately aggressive activities and attacks.…”

Section: Methodsmentioning

confidence: 99%

“…The research on ML-AIDS identifies and efficiently implements the effective and efficient anomalies of networks and computers [70]. Recently, many researchers have been dedicated to developing ML with NIDs [41], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70]. The IDS faced challenges in accuracy by reducing false alarm rates.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

et al. 2023

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Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users' and systems' sensitive information during the training and testing phases.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

et al. 2023

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“…It is considered one of the costliest communication methods while training a huge scale of multi-node modeling. However, there are a lot of vulnerabilities involved that are recorded due to the weak security of federated learning, which leads to label flipping and gradient leakage-based attacks [39,40]. The process of training the general model is compromised directly by different adversaries.…”

Section: A Industrial Internet Of Things (Iiot) and Smart Industriesmentioning

confidence: 99%

Internet of Things (IoT) Security With Blockchain Technology: A State-of-the-Art Review

et al. 2022

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With the rapid enhancement in the design and development of the Internet of Things creates a new research interest in the adaptation in industrial domains. It is due to the impact of distributed emerging technology and topology of industrial Internet of Things and the security-related resource constraints of industrial 5.0. This conducts new paradigm along with critical challenges to the existing information preservation, node transactions and communication, transmission, trust and privacy, and security protection related problems. These critical aspects pose serious limitations and issues for the industry to provide industrial data integrity, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects. In addition, the intersection of blockchain and industrial IoT has gained more consideration and research interest. However, there is an emerging limitation between the inadequate performance of industrial IoT and connected nodes, and the high resource requirement of permissioned private blockchain ledger has not yet been tackled with the complete solution. Due to the introductions of NuCypher Re-Encryption infrastructure, hashing tree and allocation, and deployment of blockchain proof-of-work required more computational power as well. This paper is divided into three different folds; first, we studied various related literature of blockchain-enabling industrial Internet of Things and its critical implementation challenging aspects along with the solution. Secondly, we proposed a blockchain hyperledger sawtooth-enabled framework. This framework provides a secure and trusted execution environment, in which service delivery mechanisms and protocols are designed with an acknowledgment, including the immutable ledger storage security, along with the peer-topeer network on-chain and off-chain communication of industrial activities. Thirdly, we design pseudochain codes and consensus protocols to provide smooth industrial node streamline transactions and broadcast content. The proposed multiple proof-of-work investigated and simulated using Hyperledger Sawtooth-enabled docker for testing to exchange information between connected devices of industrial Internet of Things within the limited usage of resource constraints.

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“…After this process, the high-level knowledge states that are pertinent to wisdom are shown in Figure 1. When knowledge is truly refined and sublimated, the receiver has the potential to minimize and maximize interaction with the medical environment [2,3].…”

Section: Introductionmentioning

confidence: 99%

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

Shaikh

Khan

Teng

et al. 2022

Wireless Communications and Mobile Computing

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This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery through the process of filtration and aggregation through experience. The layered hierarchy in the domain of e-healthcare necessitates higher scheduling costs for data collection, processing wisdom, and management, which is also an insecure and untrustworthy process for progressive medical service. The medical industry faces a difficult problem in providing collected data integrity, information reliability, and knowledge trustworthiness for the service of progressive medical relationships in the face of an increasing number of day-to-day records. The blockchain consortium hyperledger (fabric) has been used in this paper to act as a bridge that bridges the gap between electronic data, information, knowledge, and wisdom (DIKW) movement and processes by enabling the process of the layered hierarchy of schedule information and management and providing security and transparency. For e-healthcare information management and privacy, the DIKW-ledger, such as patients’ consultancy information, availing medical services, personal records, appointments, treatment details, and other health-related transactions, a consortium hyperledger fabric-enabled efficient architecture is proposed. This proposed architecture creates two networks: a public network for medical stakeholders to exchange and agree on specific medical activities before being preserved on distributed storage (read-only after record registration) and a private network for complete DIKW process scheduling and management. We designed and created smart contracts for this purpose, as well as use-case diagrams to describe the overall execution process. The proposed architectural solution provides more efficient information integrity, provenance, and storage procedures to immutably preserve the medical ledger in a permissioned hash-encrypted structure.

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A blockchain security module for brain-computer interface (BCI) with Multimedia Life Cycle Framework (MLCF)

Cited by 35 publications

References 87 publications

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Detection of Real-Time Malicious Intrusions and Attacks in IoT Empowered Cybersecurity Infrastructures

Internet of Things (IoT) Security With Blockchain Technology: A State-of-the-Art Review

BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

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