Botnet Attack Detection using Machine Learning

Description

We live in a digital era where a sea of data and information is produced and processed every day. Hence, understanding, reading, and subsequently classifying this vast amount of data becomes increasingly difficult. Moreover, when done manually, it is also susceptible to mistakes and human errors. Consequently, superfluous information occupies the space meant for essential data. It is indeed a herculean task for human beings to manually read, process, and generate summaries from large volumes of information in documents or text. To address the complexities discussed above, there is a need for a solution that can summarize important text or information. In this proposed mechanism, we are implementing a text summarization model that combines TF-IDF and Textrank algorithms with various natural language processing methods. This approach is expected to yield more precise results compared to previous models of a similar nature. Additionally, this will aid in the swift and efficient identification of harmful botnets that can potentially steal your data and infect your system with dangerous viruses and malware. Furthermore, the time saved by this solution we are developing will conserve human effort and result in substantial cost savings