Deep Neural networks based TensorFlow Model for IoT lightweight cipher attack

The internet of Things (IoT) technology is present in all aspects of our modern lives, and its standard usage is increasing remarkably. But their inherent limitations in size, storage memory, and power consumption limit its specific functionality in the secure transmission of sensitive information, where the development of lightweight ciphers responds adequately to these limitations. However, the conventional cryptanalysis of these modern ciphers can be impractical or demonstrate apparent limitations to be generalized. Because they frequently require a large amount of considerable time, known plain texts, and big storage memory, they are typically performed without the restriction of key space, or only the reduced round variants are attacked. This work proposes a deep learning (DL) model-based approach for a successful attack that discovers the plain text from cipher text one, it’s demonstrated that the proposed DL-based cryptanalysis represents a promising step towards a more efficient and automated test to verify the security of emerging lightweight ciphers. We directly attack the encryption independently of the key or the number of rounds using the TensorFlow platform in google collaboratory notebook environment that runs in the cloud and stores the results on Google Drive, the results are communicated to demonstrate precisely the effective performance of the attack, and numerous experiments were performed to confirm the study.

• Tensorflow · Deep learning · neural networks · Cryptanalysis · lightweight cipher · attack · Internet of Things

• Tensorflow · Deep learning · neural networks · Cryptanalysis · lightweight cipher · attack · Internet of Things