Cryptanalysis of the SIMON Cypher Using Neo4j

TL;DR

This paper presents a novel approach using knowledge graphs to analyze the SIMON lightweight encryption algorithm, enhancing the effectiveness of differential cryptanalysis by identifying optimal paths in the differential distribution table.

Contribution

It introduces the innovative use of knowledge graphs to improve cryptanalysis of SIMON, providing a more efficient method for differential analysis compared to heuristic approaches.

Findings

Knowledge graphs reveal relationships between differentials in SIMON.

Enhanced differential analysis efficiency through optimal path identification.

Potential for improved cryptanalysis of lightweight encryption algorithms.

Abstract

The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.