Anomaly detection via blockchained deep learning smart contracts in industry 4.0

Demertzis, Konstantinos; Iliadis, Lazaros; Tziritas, Nikos; Kikiras, Panayotis

doi:10.1007/s00521-020-05189-8

Cited by 86 publications

(35 citation statements)

References 35 publications

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“…Proposals for the development and future improvements of this framework should focus on the automated optimization of the appropriate parameters of method pre-training to achieve an even more efficient, accurate, and faster categorization process. It would also be important to study the expansion of this system by implementing more complex architecture with Siamese neural networks in an environment of parallel and distributed systems [60] or over blockchain [61,62]. Finally, an additional element that could be studied in the direction of future expansion concerns the operation of a network with methods of self-improvement and re-defining its parameters, so that the process of selecting the architectural hyper-parameters can be fully automated in order to identify dark web services [63,64], exploits from the dark web [65][66][67][68], and classify malicious traffic from this network [69][70][71].…”

Section: Discussionmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

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Section: Discussionmentioning

confidence: 99%

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

et al. 2021

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“…The introduction of microprocessor-based intelligent electronic devices (IEDs), together with cost-effective communication technologies, upgrades and automates the processes of energy distribution and rapid debugging and isolation of malicious material [40][41][42]. Therefore, based on the effective automation of the distribution process, utilities have the opportunity to create new business models of highly reliable power supply for both critical and industrial applications as well as residential consumers.…”

Section: Distribution Automationmentioning

confidence: 99%

Communication Network Standards for Smart Grid Infrastructures

Demertzis

Τσίκνας

Taketzis³

et al. 2021

Network

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Upgrading the existing energy infrastructure to a smart grid necessarily goes through the provision of integrated technological solutions that ensure the interoperability of business processes and reduce the risk of devaluation of systems already in use. Considering the heterogeneity of the current infrastructures, and in order to keep pace with the dynamics of their operating environment, we should aim to the reduction of their architectural complexity and the addition of new and more efficient technologies and procedures. Furthermore, the integrated management of the overall ecosystem requires a collaborative integration strategy which should ensure the end-to-end interconnection under specific quality standards together with the establishment of strict security policies. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents and classifies the communication network standards that have been established for smart grids and should be taken into account in the process of planning and implementing new infrastructures.

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“…The introduction of microprocessor-based Intelligent Electronic Devices (IEDs) together with cost-effective communication technologies, upgrades and automates the processes of energy distribution and rapid debugging and isolation of malicious material [40]- [42]. Therefore, based on the effective automation of the distribution process, utilities have the opportunity to create new business models of highly reliable power supply for both critical industrial applications as well as residential consumers.…”

Section: Distribution Automationmentioning

confidence: 99%

Communication Networks Standards for Smart Grid Infrastructures

Demertzis¹,

Τσίκνας²,

Takezis³

et al. 2021

Preprint

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The upgrade of energy infrastructure to those of smart grids, necessarily goes through the provision of integrated technological solutions that ensure the interoperability of their business capabilities and reduce the risk of devaluation of the systems used. The heterogeneity of the infrastructures and the dynamics of their operating environment, requires the continuous reduction of the complexity, the faster execution of the processes and the easy addition of innovative counterparts. Also, the integrated management of the overall ecosystem demands the provision of end-to-end interconnection, quality assurance, the definition of strict security policies, collaborative integration and correlation of events. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents communication operating standards specific to the smart electricity networks applications, which should be taken into account in the process of planning and implementation of new infrastructures.

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Anomaly detection via blockchained deep learning smart contracts in industry 4.0

Cited by 86 publications

References 35 publications

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Darknet Traffic Big-Data Analysis and Network Management for Real-Time Automating of the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework

Communication Network Standards for Smart Grid Infrastructures

Communication Networks Standards for Smart Grid Infrastructures

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