Chaos-guided neural key coordination for improving security of critical energy infrastructures

In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has several advantages, including (1) it protects IIoT devices by utilizing TLVVNN synchronization to improve CEI security. (2) Here, artificial neural coordination is utilized for the exchange of neural keys between two IIoT nodes. (3) Using this suggested methodology, chaotic synchronization can be achieved, enabling the chaos-based PRNG seed exchange. (4) Vector-valued inputs and weights are taken into consideration for TLVVNN networks. (5) The deep internal architecture is made up of three hidden layers of the neural network and a vector value as input. As a result, the attacker would have great difficulty interpreting the internal structure. Experiments to verify the performance of the proposed technique are conducted, and the findings demonstrate that the proposed technique has greater performance benefits than the existing related techniques.

[1]  Lela Mirtskhulava,et al.  Enhancing IoT Security Using Multi-Layer Feedforward Neural Network with Tree Parity Machine Elements , 2020 .

[2]  Neeraj Kumar,et al.  An Efficient Spam Detection Technique for IoT Devices Using Machine Learning , 2021, IEEE Transactions on Industrial Informatics.

[3]  Muhammad Saadi,et al.  An FPGA-Based Performance Evaluation of Artificial Neural Network Architecture Algorithm for IoT , 2021, Wireless Personal Communications.

[4]  Yan Zhang,et al.  Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT , 2020, IEEE Transactions on Industrial Informatics.

[5]  Tingwen Huang,et al.  Neural Cryptography Based on Complex-Valued Neural Network , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Walter Fuertes,et al.  On the Development of an Optimal Structure of Tree Parity Machine for the Establishment of a Cryptographic Key , 2019, Secur. Commun. Networks.

[7]  Pranita P. Hadke,et al.  Use of Neural Networks in cryptography: A review , 2016, 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave).

[8]  Marcin Niemiec,et al.  Error correction in quantum cryptography based on artificial neural networks , 2018, Quantum Inf. Process..

[9]  Xin Yang,et al.  Semantic Learning Based Cross-Platform Binary Vulnerability Search For IoT Devices , 2021, IEEE Transactions on Industrial Informatics.

[10]  Arindam Sarkar.  Multilayer neural network synchronized secured session key based encryption in wireless communication , 2019 .

[11]  Mattia Frasca,et al.  A true random bit generator based on a memristive chaotic circuit: Analysis, design and FPGA implementation , 2019, Chaos, Solitons & Fractals.

[12]  Zainab Khyioon Abdalrdha,et al.  Subject Review : Key Generation in Different Cryptography Algorithm , 2019 .

[13]  Vinod Patidar,et al.  A Pseudo Random Bit Generator Based on Chaotic Logistic Map and its Statistical Testing , 2009, Informatica.

[14]  Wazir Zada Khan,et al.  A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks , 2020, IEEE Access.

[15]  Muhammad Khurram Khan,et al.  Designing Anonymous Signature-Based Authenticated Key Exchange Scheme for IoT-Enabled Smart Grid Systems , 2020 .

[16]  Dowon Hong,et al.  Neural Cryptography Based on Generalized Tree Parity Machine for Real-Life Systems , 2021, Secur. Commun. Networks.

[17]  Neeraj Kumar,et al.  Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks , 2020, IEEE Transactions on Vehicular Technology.

[18]  Andreas Ruttor,et al.  Dynamics of neural cryptography. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Wolfgang Kinzel,et al.  Cryptography based on neural networks—analytical results , 2002 .

[20]  Wolfgang Kinzel,et al.  Neural cryptography , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[21]  Veelasha Moonsamy,et al.  Towards an understanding of the impact of advertising on data leaks , 2012, Int. J. Secur. Networks.

[22]  Shubham Mishra,et al.  An TPM Based Approach for Generation of Secret Key , 2019, International Journal of Computer Network and Information Security.

[23]  Lingfeng Liu,et al.  Pseudorandom bit generator based on non-stationary logistic maps , 2016, IET Inf. Secur..

[24]  K Lavanya,et al.  VECTORIZED NEURAL KEY EXCHANGE USING TREE PARITY MACHINE , 2019 .

[25]  Wolfgang Kinzel,et al.  Cooperating attackers in neural cryptography. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Andreas Ruttor,et al.  Genetic attack on neural cryptography. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Miroslav Voznak,et al.  Security Verification of Artificial Neural Networks Used to Error Correction in Quantum Cryptography , 2018, 2018 26th Telecommunications Forum (TELFOR).