Analyzing and Mitigating Distributed Denial-of-Service (DDoS) Attacks - A Python-Based Simulation Approach
The increasing prevalence of Distributed Denial of Service (DDoS) attacks poses a significant threat to the security and availability of online services and networks. These attacks leverage multiple compromised systems to overwhelm a target, rendering it inaccessible to legitimate users. This resear...
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| Veröffentlicht in: | Ingenious (Tirana. Online) Jg. 5; H. 1; S. 20 - 36 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.02.2025
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| ISSN: | 2789-4800, 2958-888X |
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| Abstract | The increasing prevalence of Distributed Denial of Service (DDoS) attacks poses a significant threat to the security and availability of online services and networks. These attacks leverage multiple compromised systems to overwhelm a target, rendering it inaccessible to legitimate users. This research presents an in-depth analysis of DDoS attack methodologies, their classification into volumetric, protocol-based, and application-layer attacks, and their real-world implications. To enhance understanding and mitigation strategies, this study introduces a Python-based simulation tool that replicates various DDoS attack techniques, including TCP, UDP, ICMP, and HTTP request floods. The tool leverages asynchronous programming and multiprocessing to simulate large-scale attack scenarios, enabling controlled testing of network resilience. Furthermore, this research explores state-of-the-art defensive mechanisms, including firewalls, rate limiting, DDoS scrubbing services, and AI-driven anomaly detection, emphasizing the role of automation in modern cybersecurity defenses. Additionally, an Intrusion Analysis System (IAS) powered by Python is proposed, integrating machine learning-based anomaly detection and real-time network traffic monitoring. This system provides organizations with adaptive and proactive defense capabilities, reducing downtime and mitigating service disruptions. The modular design of the system ensures seamless integration into existing network infrastructures, making it a scalable and effective solution for cybersecurity professionals. By combining theoretical analysis, practical implementation, and defensive strategies, this research contributes to the ongoing efforts in fortifying digital infrastructures against the evolving landscape of DDoS attacks. The findings underscore the importance of leveraging Python’s capabilities for both attack simulation and defense, paving the way for enhanced network security resilience in an increasingly interconnected digital world. |
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| AbstractList | The increasing prevalence of Distributed Denial of Service (DDoS) attacks poses a significant threat to the security and availability of online services and networks. These attacks leverage multiple compromised systems to overwhelm a target, rendering it inaccessible to legitimate users. This research presents an in-depth analysis of DDoS attack methodologies, their classification into volumetric, protocol-based, and application-layer attacks, and their real-world implications. To enhance understanding and mitigation strategies, this study introduces a Python-based simulation tool that replicates various DDoS attack techniques, including TCP, UDP, ICMP, and HTTP request floods. The tool leverages asynchronous programming and multiprocessing to simulate large-scale attack scenarios, enabling controlled testing of network resilience. Furthermore, this research explores state-of-the-art defensive mechanisms, including firewalls, rate limiting, DDoS scrubbing services, and AI-driven anomaly detection, emphasizing the role of automation in modern cybersecurity defenses. Additionally, an Intrusion Analysis System (IAS) powered by Python is proposed, integrating machine learning-based anomaly detection and real-time network traffic monitoring. This system provides organizations with adaptive and proactive defense capabilities, reducing downtime and mitigating service disruptions. The modular design of the system ensures seamless integration into existing network infrastructures, making it a scalable and effective solution for cybersecurity professionals. By combining theoretical analysis, practical implementation, and defensive strategies, this research contributes to the ongoing efforts in fortifying digital infrastructures against the evolving landscape of DDoS attacks. The findings underscore the importance of leveraging Python’s capabilities for both attack simulation and defense, paving the way for enhanced network security resilience in an increasingly interconnected digital world. |
| Author | MEMAJ, Elfat NIKLEKAJ, Malvina |
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