Machine Learning for Detecting DDoS Attacks

Machine Learning for Detecting DDoS Attacks

Author Name : Hari Chodapuneedi

ABSTRACT Distributed Denial of Service (DDoS) attacks have become a major concern for businesses and organizations, as they can result in downtime, lost revenue, and damage to reputation. Traditional methods for detecting DDoS attacks, such as intrusion detection systems, have become less effective as the methods and sophistication of these attacks have advanced. Machine learning algorithms offer a promising solution for detecting DDoS attacks in real-time, by analyzing network traffic and identifying patterns and anomalies that indicate an attack. There are several machine learning algorithms that have been applied to DDoS detection, including decision trees, K-nearest neighbors, and support vector machines. These algorithms can be trained on a dataset of normal network traffic and DDoS attack traffic, allowing them to learn the characteristics that distinguish between the two. This information can then be used to classify new incoming traffic as either normal or an attack. The K-nearest Neighbour architecture has proven to be an effective method for detecting Distributed Denial of Service (DDoS) attacks.