• Shalini Shukla
• Anshita Jain

The need for lightweight cryptographic algorithms that offer security with minimal computational and energy needs has increased due to the Internet of Things' (IoT) and other resource-constrained settings' increasing growth. To address these demands, the National Institute of Standards and Technology (NIST) has created a collection of low-complexity cryptographic algorithms. This study employs machine learning techniques to analyze the performance of NIST's lightweight cryptography algorithms. We assess the effectiveness, resource use, and security features of different algorithms under varied operating circumstances by utilizing machine learning models. This method enables a thorough evaluation of algorithm performance, revealing ideal setups and improving comprehension of how each algorithm strikes a balance between resource requirements and security. The results offer useful information for choosing and putting into practice lightweight cryptographic solutions in mobile devices, the Internet of Things, and other settings where security and computing economy are crucial.

Lightweight cryptographic algorithms, Security, Minimal computational needs, Energy efficiency, Internet of Things (IoT) ,Resource-constrained settings, National Institute of Standards and Technology (NIST), Low-complexity cryptography ,Machine learning techniques, Algorithm performance analysis ,Effectiveness Resource usage, Security features Operating circumstances, Thorough evaluation Ideal configurations, Resource requirements ,Computing economy Mobile devices, Cryptographic solutions

"Analysis of NIST Lightweight Cryptographic Algorithms Performance using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.d665-d692, December-2024, Available :http://www.jetir.org/papers/JETIR2412383.pdf

"Analysis of NIST Lightweight Cryptographic Algorithms Performance using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppd665-d692, December-2024, Available at : http://www.jetir.org/papers/JETIR2412383.pdf