Honeypot Detection and Classification Using Xgboost Algorithm for Hyper Tuning System Performance

The purpose of this research paper is to detect and classify the hidden honeypots in Ethereum smart contracts. The novelty of the work is in hypertuning of parameters, which is the unique addition along with classification. Nowadays, blockchain technologies are the grooming technologies. In the current trend, the attackers are implementing a new strategy that is much more proactive. The attackers attempt to dupe the victims by sending seemingly vulnerable contracts containing hidden traps. Such a seemingly vulnerable contract is called a honeypot. This work aims to detect such deployed honeypots. A tool named Honeybadger has been presented. It is a tool that uses symbolic execution to detect honeypots by analyzing contract bytecode. In this system, we consider different cases such as fund movement between the contractor and contract, the transaction between sender and participant, and several other contract features in terms of source code length and compilation information. In the methodology used, the features are then trained and classified using a machine learning algorithm (XGBoost and gradient boosting with hyper tuning) into Balance Disorder, Hidden State Update, Hidden Transfer, Inheritance Disorder, Skip Empty String Literal, Straw Man Contract, Type Deduction Overflow, and Uninitialized Struct. Through this algorithm, we developed a machine-learning model that detects and classifies the hidden honeypots in Ethereum smart contracts. Hypertuning of parameters is the unique addition along with classification that separates the rest of the studies done in this area.