Design and implementation of hybrid integration of cognitive learning and chaotic countermeasures for side channel attacks
Security in embedded systems is considered to be more important and needs to be a diagnosis for every minute. Also with the advent of the Internet of Things (IoT) security in the embedded system has reached its new peak of dimension. A Mathematically secure algorithm was formulated and runs on the cryptographic chips which are embedded in the systems, but secret keys can be at risk and even information can be retrieved by the prominent side channel attacks. Fixed encryption keys, non intelligent detection of side channel attacks are some of the real time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on the integration of powerful machine learning algorithms by retrieving the secret key information with countermeasures methodology using the chaotic logistic maps and includes the following contributions: (a) Preparation of Data Sets from the Power consumption traces captured from ARTIX 7 FPGA boards while running the Elliptical Curve Cryptography(ECC) on it (b) Implementation of High Speed and High Accurate Single feed forward learning machines for the detection and classification of side channel attacks (c) Design of Chaotic Countermeasures using 3 Dlogistic maps for attacked bits. The test_bed has been developed using the integration of FPGA along with Cortex A57 architectures for experimentation of the proposed work and various evaluation parameters such as Accuracy, F calls, Precision rates, sensitivity, and correlation co efficient, entropy were calculated and analyzed. Moreover, the parameters of the proposed system which has been analyzed prove to outperform the other existing algorithms in terms of performance and detection.
J. Ambient Intell. Humaniz. Comput.