Abstract As IoT has increasingly evolved in recent years, it has become more important to ensure security on IoT devices. Many of such devices are under the threat of attacks in the beyond black‐box model. To protect from the threat, the cryptographic implementation that can offer secure execution in the grey‐/white‐box model is important. However, such cryptographic implementations require a large number of clock cycles to execute and cannot fully cover resistance against various types of side‐channel attacks. In this paper, a new family of table‐based cipher dubbed Cubicle is proposed, which can offer efficient execution and sufficient security against side‐channel attacks on IoT devices powered by ARM Cortex‐M processors, which are widely deployed in IoT applications. To evaluate the security of Cubicle in the grey‐box model, the authors derive the bound of table leakage in the grey‐box model by applying space hardness, which is the notion to evaluate the security against code lifting attacks in the white‐box. The security of Cubicle in the grey‐box model is shown by using this bound. In addition, the security of Cubicle is also shown in the black‐box and white‐box models. Finally, the performance of Cubicle and other ciphers in some devices powered by ARM Cortex‐M3, ‐M4, and ‐M7 processors is evaluated. The authors show that Cubicle is significantly efficient compared to other grey‐/white‐box‐ model‐secure ciphers in target experiments for IoT applications.

Lizard is a very recently proposed lightweight stream cipher that claims 60 bit security against distinguishing (related to state recovery) and 80 bit security against key recovery attack. This cipher has 121 bit state size. In this paper, we first note that using ψ key stream bits one can recover ψ unknown bits of the state when t state bits are fixed to a specific pattern. This is made possible by guessing the remaining state bits. We present certain values of ψ, t based on the state size that helps in mounting a generic conditional TMDTO attack following the BSW sampling. For Lizard, we obtain the preprocessing complexity as 267, and the maximum of Data, Time and Memory complexity during the online phase as 254. The parameters in the online phase are significantly less than 260.