Crypto Jacking attack is a type of resource spying in which a crypto-currency mining script is run by the attacker on the victim’s machine to profit. Since 2017 it has been widely used and was previously the most serious threat to network security. Because of the number of malicious actors has increased there is a recent increase in the value of cryptocurrencies. The availability of bit-coin mining software has grown significantly. Mining for crypto-currency has a high inclination to spread. Malware can unintentionally use resources, harm interests, and cause further genuine damage to assets. Learning and identifying new malware have the traits of still being unique and self-sufficient, and they cannot be acquired adaptively in order to overcome the aforementioned concerns. Recently, other countermeasures have been introduced, each with its own set of features and performance, but each with its unique design. In order to increase the profitability of crypto-jacking, attackers are expanding their reach to browsers, network devices, and even Internet of Things (IoT) devices. Browsers, for example, are a particularly enticing target for attackers looking to obtain sensitive data from victims. The listed methods are intended to safeguard the individual user, network, and outsiders, particularly against insiders. The newness of the paper is a comprehensive overview of bitcoin along with crypto-jacking malware detection is presented in order to analyze various types of systems based on behaviour-based, host-based, network flow-based, and so on methods. The main aim of the analysis is based on the supervised and unsupervised machine learning algorithms and other algorithms used in the detection of crypto-jacking malware. In the proposed paper combination of the decision tree method (based on Behaviour, Executable) and the crying jackpot method (based on Host, Network) are examined to classify the type of which crypto-jacking attack that takes place within the target victim. The uniqueness of the paper is informative with real-world applications for malware recognition and malware categorization to detect a crypto-jacking attack.