Side Channel Attacks Against GIFT Lightweight Block Cipher
Additional Funding Sources
This research has been sponsored by the National Science Foundation under Award No. 1950599.
Abstract
Computing devices continue to be increasingly spread out within our everyday environments. Computers are “embedded” into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. In 2016, the National Institute of Standards and Technology (NIST) initiated the first call for new lightweight cryptographic proposals to strengthen the cryptographic defense of networked devices against cyber attacks and to protect the data created by those devices. Lightweight Cryptography allows for the encryption of sensitive information from devices with constraints of power usage, processing power, storage, and more. Side-Channel Attacks (SCA) analyze additional information devices release beyond the transmitted message, such as timing, heat, sound, or power consumption to reveal encrypted content. GIFT, a lightweight block cipher, is one of ten candidates in the final round of NIST's lightweight cryptography competition to create a new standard for constrained devices. GIFT has been shown to be vulnerable against Correlation Power Analysis (CPA), a type of non-profiled Side-Channel Attack. Deep Learning-based side-channel attacks have shown promise in overcoming countermeasures such as masking. Our research focuses on the effectiveness of Deep Learning-based Side-Channel Attack against GIFT with and without masking countermeasures. We provide a comparative study of Side-Channel Attacks against GIFT that have produced results against other block ciphers.
Side Channel Attacks Against GIFT Lightweight Block Cipher
Computing devices continue to be increasingly spread out within our everyday environments. Computers are “embedded” into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. In 2016, the National Institute of Standards and Technology (NIST) initiated the first call for new lightweight cryptographic proposals to strengthen the cryptographic defense of networked devices against cyber attacks and to protect the data created by those devices. Lightweight Cryptography allows for the encryption of sensitive information from devices with constraints of power usage, processing power, storage, and more. Side-Channel Attacks (SCA) analyze additional information devices release beyond the transmitted message, such as timing, heat, sound, or power consumption to reveal encrypted content. GIFT, a lightweight block cipher, is one of ten candidates in the final round of NIST's lightweight cryptography competition to create a new standard for constrained devices. GIFT has been shown to be vulnerable against Correlation Power Analysis (CPA), a type of non-profiled Side-Channel Attack. Deep Learning-based side-channel attacks have shown promise in overcoming countermeasures such as masking. Our research focuses on the effectiveness of Deep Learning-based Side-Channel Attack against GIFT with and without masking countermeasures. We provide a comparative study of Side-Channel Attacks against GIFT that have produced results against other block ciphers.