DETECTING AND MITIGATING BOT NET ATTACKS IN SOFTWARE-DEFINED NETWORKS USING DEEP LEARNINGThe rapid growth of networked systems and cloud-based infrastructures has led to an increase in sophisticated cyber threats, particularly botnet attacks, which pose s

Abstract

The rapid growth of networked systems and cloud-based infrastructures has led to an increase in sophisticated cyber threats, particularly botnet attacks, which pose significant risks to network security and performance. Botnets consist of compromised devices controlled by malicious actors to launch large-scale attacks such as Distributed Denial of Service (DDoS), data theft, and network disruption. Traditional security mechanisms often struggle to detect these attacks due to their dynamic and evolving nature. This project, “Detecting and Mitigating Botnet Attacks in Software-Defined Networks Using Deep Learning,” proposes an intelligent and adaptive framework that leverages Deep Learning techniques within a Software-Defined Networking (SDN) environment to enhance threat detection and mitigation. The proposed system utilizes the centralized control architecture of SDN to monitor network traffic in real time and extract relevant features. A deep learning model, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), is employed to analyze traffic patterns and identify anomalies associated with botnet activities. The system incorporates preprocessing steps such as feature extraction, normalization, and data labeling to improve model accuracy. Upon detection of malicious traffic, the SDN controller dynamically applies mitigation strategies such as traffic filtering, flow rule updates, and isolation of compromised nodes. The performance of the system is evaluated using metrics such as accuracy, precision, recall, F1-score, and detection rate. By combining the programmability of SDN with the predictive capabilities of deep learning, the proposed framework provides a scalable, flexible, and efficient solution for real-time botnet detection and mitigation. This research contributes to the development of advanced cybersecurity systems capable of adapting to emerging threats and ensuring secure network operations.