Enhancing DDoS Detection in 5G Systems through Advanced Intrusion Detection Techniques
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摘要
As 5G technology continues to advance, it brings unprecedented opportunities for high-speed connectivity and data transfer. However, the proliferation of 5G also opens new avenues for cyber threats, including Distributed Denial of Service (DDoS) attacks. With the advent of 5G technology, the potential for faster and more efficient communication is undeniable. However, this progress also brings about new challenges, particularly in the realm of security. One of the major threats faced by 5G systems is Distributed Denial of Service (DDoS) attacks, which can cripple network performance and compromise user experience. This paper explores the application of advanced intrusion detection techniques for the detection and mitigation of DDoS attacks in 5G systems. The study investigates the unique characteristics of 5G networks, such as increased bandwidth, low latency, and massive device connectivity, and proposes innovative solutions to enhance DDoS detection capabilities. The research aims to contribute to the development of robust security measures, ensuring the resilience of 5G networks against evolving cyber threats. DDoS attacks can overwhelm network resources and disrupt services, making them a significant concern in 5G systems. This paper presents a comprehensive exploration of DDoS detection techniques within the context of 5G systems, with a specific focus on leveraging Intrusion Detection Techniques (IDS). We delve into the unique challenges posed by 5G networks, such as their increased complexity, massive data flows, and low-latency requirements, and how these challenges impact DDoS detection. Our research examines various IDS methods, including signature-based, anomaly-based, and machine learning-based approaches, to assess their suitability for 5G DDoS detection. Furthermore, we propose novel strategies and enhancements tailored to 5G environments to improve the accuracy and efficiency of DDoS detection. These strategies encompass real-time traffic analysis, behavior profiling, and adaptive response mechanisms. Through empirical experiments and simulations, we evaluate the performance of these techniques in detecting and mitigating DDoS attacks in 5G systems. We assess their effectiveness in terms of detection accuracy, false-positive rates, and resource utilization. In conclusion, this research contributes valuable insights into the challenges and solutions related to DDoS detection in 5G systems using Intrusion Detection Techniques. By addressing these challenges, we aim to enhance the security and resilience of 5G networks, ensuring their continued reliability in the face of evolving cyber threats.
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