DETECTING MALICIOUS PACKAGES AND DDOS ATTACKS IN NETWORK TRAFFICE USING DEEP CONVOLUTIONAL NEURAL NETWORKS

Abstract

Deep convolutional neural networks (CNNs) have become a powerful tool in the network security arsenal, proving adept at detecting malicious packets and countering distributed denial of service (DDoS) attacks. The synergy between CNN and machine learning methodologies has ushered in a new era of effectiveness in threat detection.

The traffic analysis process involves a complex interplay of techniques for preprocessing incoming network traffic data, converting it into patterns that can be recognized by a neural network, algorithmic optimization, and rigorous model evaluation, often using large datasets such as KDD Cup 99, to create robust detection models. This approach is a key step towards strengthening network infrastructure against an increasingly diverse range of cyber threats and with the ability to expand and further train the model.

The proposed system embodies adaptability, characterized by a continuous learning system that improves models over time with new input data. Its well-thought-out design gives users the flexibility to choose network adapters and fine-tune learning parameters, providing a responsive and customizable operating environment. By integrating a user-friendly WinForms interface and comprehensive reporting mechanisms, the system strikes a harmonious balance between usability and reliability.

To confirm its effectiveness, additional software was developed to simulate various traffic scenarios and stress test the model's performance. The results not only confirmed the effectiveness of the model, but also highlighted the need for continuous improvement of the model to maintain resilience in the face of emerging threats. This research highlights the enormous potential of deep convolutional neural networks in network traffic analysis, signaling a continued evolution toward higher standards of network security.