Knapsack Cipher-based metaheuristic optimization algorithms for cryptanalysis in blockchain-enabled internet of things systems

Abstract

The integration of the Internet of Things (IoT) and blockchain demand the use of public-key cryptography systems to secure network communications. In this study, one of those public-key algorithms, known as Merkle–Hellman Knapsack Cryptosystem (MHKC), is employed to cryptographically analyze its utilization for blockchain technology using a metaheuristic algorithm. To do so, eight well-known metaheuristic algorithms are employed to determine the trustworthiness of MHKC against cryptoanalysis attacks using various knapsack lengths, ranging from 8 to 32 bits. The experimental findings showed that pathfinder algorithm (PFA) and slime mold optimizer (SMA) could exploit MHKC under 8-bit ASCII code, and their performance gradually deteriorates with higher bit representations, while the performance of manta ray foraging optimization (MRFO) could be superior for the knapsack lengths higher than 8-bit. Additionally, MRFO would not attack MHKC under 32-bit; thus, some genetic operators have been integrated to manipulate the binary solutions obtained by this algorithm to promote its exploration capability in a variant, namely HMRFO. The experimental findings revealed that HMRFO is a better alternative to the existing ones for attacking the MHKC with knapsack lengths higher than 8-bit to appear their fragility points, while both SMA and PFA are competitive for 8-bit ASCII code and superior to the other algorithms.