IoT has created new values by connecting network with various small devices, but security threat becomes more important issues in the recent reports of automobile hacking and illegal surveillance camera manipulation etc. In industry and academia alike, lightweight encryption has gained an enormous interest because of its simple operations and small size. Nowadays, IoT devices are required to use encryption to sensor devices with various restrictions. Some well established standard algorithm (e.g., AES) may not suitable for IoT as the basic requirements of these constrained devices are low power usage, low-cost hardware implementation, and latency. ARX stands for Addition/Rotation/XOR and is a family of lightweight symmetric-key encryption algorithms that are mostly designed with the very simple operations: Modular addition, bitwise rotation, exclusive-OR (XOR). ARX algorithms are generally secured against well-known attacks like linear and differential. The term ARX is very new and was introduced in 2009, but the concept of ARX is much older, and dates back to 1987—the FEAL cipher [

1] used it first time. To analyze the security of symmetric algorithms, the most powerful tools are linear [

2] and differential [

3] cryptanalysis. However, for ARX ciphers there is not any proven security bound in the literature. ARX ciphers are very fast, and therefore designers use a large number of rounds to secure against these attacks. Finding an optimal differential characteristic (or differential path) is the most critical task to perform differential cryptanalysis. For ARX ciphers, finding differential characteristics is the most challenging task and involves months of manual calculations (as done by Wang et al. for several hash functions [

4]) or to construct a heuristic search program. When applying differential cryptanalysis, one pays particular attention to non-linear operations such as an S-box or modular addition. The cryptanalysis of the substitution box (S-box) based algorithms are feasible in most of the cases. In the case of the S-P network such as the AES cipher, an S-box is typically 8- or 4-bit. Such a size allows computing the full difference distribution table (DDT) and investigating differential properties of the S-box and the algorithm. ARX-based designs use modular addition rather than S-boxes as a source of non-linearity. Word size in such ciphers are typically 32- or 64-bit and constructing a complete DDT is infeasible (it requires

${2}^{3n}\times 4$ bytes of memory for n-bit words). We face a huge number of possible difference transitions through modular addition box. Because of this, we need some efficient heuristic to circumvent this limitation. However, we have seen advancement in research to calculate a partial difference distribution table (pDDT) [

5] to reduce the search space. But using such partial difference distribution table to find the differential path without any clever heuristic is still infeasible and requires several days to calculate differential characteristic. In artificial intelligence (AI) and in other areas such issues are very common where many problems have large searching space but no good heuristic available as a guide to find moves as the best path. In this paper, we developed a binary tree based random heuristic tool that improves results in each nested iteration. The algorithm tries to optimize the move at each level of the tree. For cryptanalysis purpose, we choose the Chaskey [

6] cipher belongs to the ARX family. Chaskey cipher process a message

m of 128-bit blocks and 128-bit key size

K and very suitable algorithm for 32-bit micro-controllers.