Cyber attackers entrap online users and companies by stealing their sensitive information without their knowledge. Sensitive information such as login credentials, bank card details, and centralized servers of organizations are hacked by the attackers. Some of the cyber attacks are phishing attack where attackers make the online users to trust their websites as a legitimate website and retrieve their personal information by making them as a prey for cyber attacks. Malware attack is a one where attacker will inject a malicious software into the company server or any online user’s device, without their knowledge and steal all the data in their devices and servers. Intrusion is an invasion, where attacker will attack the network and theft all the network resources. But, there are many types of cyber attacks solutions such as visual similarity-based approaches, intrusion detection system, signature-based, heuristic-based, specification-based, anomaly-based methods that are proposed, but they have some disadvantages. Because of few unsecured HTTP websites and lack of cyber knowledge, cyber attacks are increasing day by day. In our proposed system, a unified ensemble approach (UEA) is proposed by combining different machine learning algorithms using ensemble approach that gives better accuracy and detection rate. This model aims to detect the intrusion, phishing attack and prevent the malwares thereby mitigating the cyber attacks encountered by individual and organization.