Unified Shield: Ml and Dl Integration Through Ann-Svm for Imbalanced Data

Denial-of-Service (DoS) attacks can be stopped in three different ways: Machine Learning (ML), Deep Learning (DL), and a mixed design that uses both an Artificial Neural Network (ANN) and a Support Vector Machine. Denial of Service (DoS) attacks is very bad for network security because they cost a lot of money and stop services from working. In our work, we are focusing on coming up with good ways to defend against these kinds of threats. In this research paper firstly, Machine Learning methods are used to look at old network data and find and classify DoS attacks. to make a strong and effective machine learning defense system by training models and extracting features. Based on neural networks Deep Learning was used to look through network flow data and find links and small trends. This method makes it easier to find complicated and subtle trends in DDoS attacks. An Artificial Neural Network (ANN) and a Support Vector Machine are used in this system to blend Deep Learning and Machine Learning ideas. This combination model takes the best features of both support vector machines and neural networks and mixes them into one. In the field of the cybersecurity this research contributes the major role to find and improved the detection of the imbalanced data.