## 1 Introduction  
Security in the blockchain has become a significant concern because of the recent developments in the field. One of the most common and easiest attacks carried out by the attacker is the phishing attack, where the attacker tricks the miner into mining a malicious block into the blockchain using social engineering techniques. Many blockchain projects with high-grade security implications were found to be susceptible to phishing attacks, and many members of the community and its investors have lost large sums of money [1]. A report in 2017 showed that more than 50% of attacks carried out on Ethereum were phishing attacks [2]. Phishing attacks are so common that they threaten the three pillars of security: privacy, integrity, and availability. A phishing attack is usually undertaken with a series of aims; the attacker either has the goal of monetary gain or installing malicious code into the environment. The attack is mainly carried out using social engineering. In it, the miner is tricked into thinking that the malicious block is genuine and that linking it to the blockchain would be of benefit. If the miner links the malicious block, it is considered incorrect mining, and the miner is not rewarded. This makes that chain of the main blockchain dysfunctional, and further blocks link to that chain impossible. Currently, many methodologies are available to prevent this kind of attack from being carried out. The principal method is the consensus protocol, wherein a block is only linked to the blockchain if all the block owners agree upon its addition. However, it is a very time-consuming method [3], with drawbacks and biases. This has led to zero-trust policies in the blockchain and research on the same. These policies have been implemented in major blockchains such as Bitcoin and Ethereum [4] and have shown promising results. Nonetheless, it is a highly complex mechanism to deploy, and many new blockchains are still trying to set foot in the same territory. Zero-trust policies are still being researched and might also hold a promising future. However, the attackers will not wait until these policies are implemented to start their attacks. A newer and more efficient methodology is required to prevent phishing attempts. This can be achieved using machine-learning models and methods. This paper defines several models with promising results that may be helpful for the same. Using certain features, the researchers reached 98.28% accuracy in blockchain phishing attempt detection. The project's novelty lies in using different machine-learning algorithms and deviating from the original process of thought. Whilst using machine learning, the researchers have preferred to use graph-based mapping methods and neural networks for prediction; however, the best accuracy they achieved lay in the range of 85-90%. This project also aims at a higher accuracy using other machine-learning models. Data preprocessing was carried out at the minute level to remove any discrepancies, contributing to higher accuracy. This paper focuses on blockchain phishing detection on Ethereum and achieving accuracy in that environment. This paper is divided into nine sections. Section 1 is the introduction to the project and the topic of interest. Section 2 covers the literature review, and Section 3 defines the dataset. Section 4 outlines the procedure of the experiment. Section 5 defines the models used in the investigation. Section 6 describes the observations made during the experiment, and Section 7 concludes the achieved research, Section 8 points to the research limitations and possible future work. This paper presents detailed research on machine-learning algorithms, which can accurately predict phishing attempts in the blockchain. The researchers developed models using their prior knowledge and the help of several experts. Hence, the paper centers on the following research questions:  
RQ1 How accurately can machine-learning algorithms predict a phishing attempt in a blockchain environment, and which gives the best results?