# Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract
## 2 Literature Review  
One of the most well-known types of Ethereum fraud is the Ponzi scheme. [PERSON] et al. [10] developed a data mining methodology to identify Ponzi fraud, a system in which only early investors gain from the money collected from later investors and, after a period of time, the plan collapses, leaving later investors with nothing. They constructed their dataset by using open-source Ethereum smart contracts, which included previously identified Ponzi smart contracts, and discovered that their pyramid scheme fraud detection model based on data mining has precision of 0.99 and recall of 0.99 and can protect against these deceitful contracts. These misleading pyramid schemes were thoroughly investigated by [PERSON] et al. [11], who also evaluated their nature and effects from a variety of angles. By retrieving their Solidity codes and then confirming them with the blockchain EVM codes, they created a dataset of these fraud schemes. The most likely scams, according to a thorough investigation of these schemes, are those that promise large profits, explain their dependability with the justification of open access to the source code, and have a Gini coefficient of more than 80%. [PERSON] et al. [12] presented a categorization model not relying on the source code to automatically identify fraudulent schemes using smart contracts with labels and extracting essential features without source code. They constructed their dataset by manually checking from etherscan after collecting open-source smart contracts. They concluded that their proposed model can identify fraudulent schemes at the time of their creation, and their suggested method (RF) increases the precision to 0.95 and outperforms other methods such as XGBoost and one-class SVM. [PERSON] et al. [13] studied the extensive network of fraudulent pyramid schemes that entice Bitcoin users and looked at the availability and demand of these schemes. By collecting information on victims' dialogues with scammers, they also examined the demand side and supply side of these scams. They collected data by researching the full history of scam allegations, gambling, and games involving investments, as well as information from the locations wherein these fraudulent incidents were marketed. They came to the conclusion that if the scammer maintains contact with the victim, the scam lifetime increases, and if the victim maintains more frequent daily updates, the fraud lifetime falls. [PERSON] et al. [14] suggested using secure smart contracts built on the ERC20 interface on a blockchain network with the necessary functions and procedures, to provide a comprehensive framework for safeguarding manufacturing activities based on the Cloud. It is very difficult to uncover features that allow the precise identification of anomalous contracts, and statistical analysis based on these attributes is equally unsuccessful. Second, they disregard the instabilities and internal consistency of smart contract accounts, which largely contributes to model obversion. [PERSON] et al. [15] employed a web crawler to collect data and obtained unique bytecodes for contracts confirmed by etherscan.io, which they then translated into opcodes using the pyevmasm disassembler. A feature vector was built by combining transaction characteristics, opcode n-grams, and document frequency. To construct a powerful classifier, the ensemble learning model was used as a feature vector together with the extra trees and gradient boosting machine learning techniques. This entire model was then utilized to categorize anomalous contracts. In contrast to other algorithms, such as DT, XGB, LGBM, and GBM, the results revealed accuracy of 89.67%, demonstrating the effectiveness of the proposed model. Combining characteristics significantly improves detection. IoT devices are more often secured through firmware updates. Traditional updating systems have drawbacks, including bandwidth constraints and attacks by hackers' distributed denial of service (DDoS). [PERSON] et al.