Abstract
The increasing threat to data over public channels has bought about a need to secure sensitive data to avoid its misuse and tampering. This makes data security an important issue in the present which will continue to pose a problem in the future.
Over the successive years a lot of work has been done in this field, with the recent developments focusing on neural networks and its application in security. Neural network algorithms for the same like Multilayer Perceptron technique, Back Propagation Technique have been implemented. Multilayer Perceptron technique aforesaid model lacks in accuracy and is complex whereas the Back Propagation Technique is more accurate but its complexity is more than Multilayer Perceptron and also has result convergence faults.
This paper deals with data encryption using a random set of keys and the key identification using Hebbian learning. Here bits of data are taken at a time and encrypted using a key from given set of keys. The key is identified using Hebbian learning and hence data is decrypted. Main advantage of this method is its simplicity and that it is error free in lossless transmission.
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© 2016 Springer Nature Singapore Pte Ltd.
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Murmu, B.I., Kumari, A., Malkani, M., Kumar, S. (2016). Key Identifications Using Hebbian Learning. In: Mueller, P., Thampi, S., Alam Bhuiyan, M., Ko, R., Doss, R., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2016. Communications in Computer and Information Science, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-2738-3_11
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DOI: https://doi.org/10.1007/978-981-10-2738-3_11
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