# Modified Genetic Algorithm with Deep Learning for Fraud Transactions of Ethereum Smart Contract
## 2 Literature Review
[16] presented a model that utilizes web crawlers to collect tagged bogus addresses, after which a transaction network is rebuilt using the public transaction book. Then, to extract node attributes for detecting fraudulent transactions, an amount-based network embedding approach is developed. Ultimately, a graph convolutional network is used to differentiate between legal and counterfeit addresses. Results showaccuracy of 95%, which indicates the system's high effectiveness in identifying fraudulent Ethereum transactions. [PERSON] et al. [17] proposed a model to discriminate between phishing and non-phishing nodes, and an SVM approach was used. The detection objective was 1259 phishing nodes, and 1259 unlabeled nodes at random as outliers were chosen. To build a subnetwork, the aforementioned nodes' first-order neighbors and transaction records were crawled. After obtaining a massive Ethereum transaction network, nodes were depicted using a network embedding approach. Word-embedding-inspired random-walk-based network embedding approaches, which try to maintain the local structural properties, have been used, and node2 vec, an outstanding random-walk-based method, was employed to represent nodes. At this stage, the SVM classifier is used. Results showed that non-embedding approaches are ineffective in detecting phishing. Non-embedding methods are methods for manually extracting characteristics that, unlike embedding methods, cannot use structural information. Therefore, to obtain transaction network characteristics, the network embedding technique is crucial. It has been stated that Ethereum scams generate large profits and represent a severe threat to the Ethereum network's financial security. [PERSON] et al. [18] investigated unlawful accounts on the Ethereum blockchain and proposed a fraud detection model with three different algorithms: decision tree, random forest, and k-nearest neighbors (KNN). A dataset was used from Kaggle, with 42 features; later, it was reduced to only six features with the help of correlation coefficients, which selected only the important features. Results demonstrate a considerable improvement in time measurements when utilizing the three algorithms, as well as an improvement in the F measure when using the random forest approach. An effective solution for fraud detection is desperately needed to ensure a secure investment environment. In this research, a three-step methodology for mining Ethereum transaction data to detect phishing frauds is offered. [PERSON] et al. [19] advocated for utilizing a distributed database to minimize storage space by not placing firmware information directly within it. The correctness and integrity of the acquired firmware of IoT devices may be assured after downloading the firmware in the suggested system. Instead of consulting the manufacturer, IoT devices obtain firmware from the distributed database's download point. By utilizing blockchain technology, it is possible to lessen the strain on the file server while keeping system information safe. This research may lessen the requirement for storage space while also improving system security. The suggested solution performs well in several respects, such as firmware integrity, IoT device connection security, security mechanisms, and device anonymity. Due to the properties of this technology, such as immutability and transparency, the technology has been lengthened further than cryptocurrency and is being used in various sectors, such as education, healthcare, finance, energy, government, and IoT, to provide more privacy, increased efficiency, and increased protection.