With the increasing complexity and volume of network traffic, ensuring the security and stability of computer networks is paramount. Traditional rule-based approaches for detecting anomalies in network traffic have limitations in handling evolving threats and detecting previously unseen patterns. To address this challenge, we propose a real-time anomaly detection system leveraging machine learning techniques. The system consists of two main components: a server-side application and a client-side data generator. The server-side application receives network traffic data from clients, preprocesses the data, and applies a machine learning model for anomaly detection. The machine learning model, based on the Isolation Forest algorithm, is trained to identify deviations from normal network behavior. Detected anomalies trigger appropriate responses, such as logging security threats or activating countermeasures.
The client-side data generator simulates network traffic by generating data packets with various features, including packet size, source, destination, and timestamp. These data packets are sent to the server for real-time analysis. Additionally, the system supports integration with external sources of network data, such as ping statistics or network logs, enabling comprehensive anomaly detection.
The effectiveness of the system is evaluated through extensive testing using both simulated and real-world network data. Performance metrics, including detection accuracy, false positive rate, and response time, are measured to assess the system's reliability and efficiency. The results demonstrate the system's ability to accurately detect and respond to anomalies in real-time, enhancing network security and resilience against emerging threats.
Overall, the proposed real-time anomaly detection system offers a scalable and adaptive solution for safeguarding computer networks against malicious activities and unauthorized access, thereby ensuring the integrity and availability of critical network infrastructure.
Problem statements
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