In the present growth of the Internet, people are adjusting to early life of communication that save time and enable speedy completion of tasks, which is their main necessity. Here, the Internet has become a crucial component of today’s generation, influencing all facets of daily life, including online commerce, online schooling, and many other crucial services. The topic of user data or personnel information protection, security, and safety issues will result from the enormous rise of Internet technology. A computer system used for data transfer must meet all these requirements and be equipped with the necessary security measures to prevent hostile attacks and unauthorized access from which the user’s data can easily be compromised and the system infected. All these cyber-related dangers will significantly harm companies’ or individuals’ data, but they shouldn't reach an unrecoverable level. Therefore, the creation of a Cyber-Intrusion Detection System (CIDS) using machine learning techniques is the best option for the early detection of cyber-attacks. The suggested work is more successful at handling the many kinds of cyber-attacks that happen for specific data packets and notifying users or system engineers of the attack as soon as possible via social media tools like e-mail or telegram. As a result, we wanted to create the cyber-intrusion system by modifying ML algorithms that deal with cyber-attack issues dynamically and improve early detection rates, decrease false alarm rates, and minimize communication costs.