Anomaly detection via blockchained deep learning smart contracts in industry 4.0

The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. Therefore, alternative, intelligent solutions should be used to detect anomalies in the operating parameters of the infrastructures concerned, while ensuring the anonymity and confidentiality of industrial information. Blockchain is an encrypted, distributed archiving system designed to allow for the creation of real-time log files that are unequivocally linked. This ensures the security and transparency of transactions. This research presents, for the first time in the literature, an innovative Blockchain Security Architecture that aims to ensure network communication between traded Industrial Internet of Things devices, following the Industry 4.0 standard and based on Deep Learning Smart Contracts. The proposed smart contracts are implementing (via computer programming) a bilateral traffic control agreement to detect anomalies based on a trained Deep Autoencoder Neural Network. This architecture enables the creation of a secure distributed platform that can control and complete associated transactions in critical infrastructure networks, without the intervention of a single central authority. It is a novel approach that fuses artificial intelligence in the Blockchain, not as a supportive framework that enhances the capabilities of the network, but as an active structural element, indispensable and necessary for its completion.

[1]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[2]  Shuai Li,et al.  Online Clustering of Bandits , 2014, ICML.

[3]  Lifeng Zhou,et al.  Industry 4.0: Towards future industrial opportunities and challenges , 2015, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[4]  Howard Shrobe,et al.  IIoT Cybersecurity Risk Modeling for SCADA Systems , 2018, IEEE Internet of Things Journal.

[5]  M. Sartor,et al.  Addressing Industry 4.0 Cybersecurity Challenges , 2019, IEEE Engineering Management Review.

[6]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[7]  Bhabendu Kumar Mohanta,et al.  An Overview of Smart Contract and Use Cases in Blockchain Technology , 2018, 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Andrés Gago Alonso,et al.  Computing Anomaly Score Threshold with Autoencoders Pipeline , 2018, CIARP.

[10]  Shuai Li,et al.  Distributed Clustering of Linear Bandits in Peer to Peer Networks , 2016, ICML.

[11]  Thomas H. Morris,et al.  A Specification-based Intrusion Detection Framework for Cyber-physical Environment in Electric Power System , 2015, Int. J. Netw. Secur..

[12]  Khaled Salah,et al.  A Modbus traffic generator for evaluating the security of SCADA systems , 2014, 2014 9th International Symposium on Communication Systems, Networks & Digital Sign (CSNDSP).

[13]  Konstantinos Demertzis,et al.  Cyber-Typhon: An Online Multi-task Anomaly Detection Framework , 2019, AIAI.

[14]  Bu-Sung Lee,et al.  Autoencoder-based network anomaly detection , 2018, 2018 Wireless Telecommunications Symposium (WTS).

[15]  Shuai Li,et al.  Mining λ-Maximal Cliques from a Fuzzy Graph , 2016 .

[16]  Shancang Li,et al.  Blockchain Enabled Industrial Internet of Things Technology , 2019, IEEE Transactions on Computational Social Systems.

[17]  Thomas H. Morris,et al.  Machine learning for power system disturbance and cyber-attack discrimination , 2014, 2014 7th International Symposium on Resilient Control Systems (ISRCS).

[18]  A. Abouabdellah,et al.  Industry 4.0: Fundamentals and Main Challenges , 2019, 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA).

[19]  Andrea Zanella,et al.  IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices , 2019, IEEE Internet of Things Journal.

[20]  Hu Wenting,et al.  Overview of one-Class Classification , 2019, 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).

[21]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[22]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[23]  Thomas H. Morris,et al.  Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems , 2015, IEEE Transactions on Smart Grid.

[24]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[25]  Shuai Li,et al.  Collaborative Filtering Bandits , 2015, SIGIR.

[26]  Rishav Chatterjee,et al.  An Overview of the Emerging Technology: Blockchain , 2017, 2017 3rd International Conference on Computational Intelligence and Networks (CINE).

[27]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[28]  Shuai Wang,et al.  Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[29]  Lorenzo Bassi,et al.  Industry 4.0: Hope, hype or revolution? , 2017, 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI).

[30]  Konstantinos Demertzis,et al.  The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence , 2018, Big Data Cogn. Comput..

[31]  D. Culler,et al.  WAVE : A Decentralized Authorization System for IoT via Blockchain Smart Contracts , 2017 .

[32]  Rani Astya,et al.  An overview of deep learning architectures, libraries and its applications areas , 2018, 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN).

[33]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Kim Schaffer,et al.  An Overview of Anomaly Detection , 2013, IT Professional.

[35]  Tooska Dargahi,et al.  Protecting IoT and ICS Platforms Against Advanced Persistent Threat Actors: Analysis of APT1, Silent Chollima and Molerats , 2019, Handbook of Big Data and IoT Security.

[36]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[37]  Chengqi Zhang,et al.  Data preparation for data mining , 2003, Appl. Artif. Intell..

[38]  Engin Zeydan,et al.  An overview of blockchain technologies: Principles, opportunities and challenges , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[39]  C. L. Philip Chen Deep learning for pattern learning and recognition , 2015, 2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics.

[40]  Vladimir Sklyar,et al.  ENISA Documents in Cybersecurity Assurance for Industry 4.0: IIoT Threats and Attacks Scenarios , 2019, 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[41]  Claude Fachkha Cyber Threat Investigation of SCADA Modbus Activities , 2019, 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS).

[42]  Marcelo Mendoza,et al.  Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications , 2017, Lecture Notes in Computer Science.

[43]  Thomas H. Morris,et al.  Classification of Disturbances and Cyber-Attacks in Power Systems Using Heterogeneous Time-Synchronized Data , 2015, IEEE Transactions on Industrial Informatics.

[44]  Bogdan M. Wilamowski Welcome to the IEEE Transactions on Industrial Informatics, a New Journal of the Industrial Electronics Society , 2005 .

[45]  Konstantinos Demertzis,et al.  The Next Generation Cognitive Security Operations Center: Adaptive Analytic Lambda Architecture for Efficient Defense against Adversarial Attacks , 2019, Big Data Cogn. Comput..

[46]  Sung-Bong Jang,et al.  A Survey of Blockchain and Its Applications , 2019, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).