Active and Adaptive Techniques for Handling Distributed Denial of Service by Analyzing IP Addresses

• Author(s): Amudat Monsurat
• Paper ID: 1701030
• Page: 229-235
• Published Date: 29-02-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 8 February-2020

Abstract

While they were first discovered, the Distributed Denial of Service attacks remain one of the biggest menaces to the online systems, crippling their networks and disrupting services with overloading servers with unnecessary requests. This challenge thus demands innovative and adaptive measures that detect, mitigate, and prevent such attacks on runtime. Among the emerging solution landscapes, analyzing IP addresses linked to incoming traffic had come out as a basic rule underlying effective defense mechanisms against the DDoS threat. This paper presents an active and adaptive approach using IP address analysis in mitigating DDoS attacks, with an emphasis on the use of real-time detection and anomaly analysis coupled with methods of traffic filtering. The active techniques comprise on-the-fly responses to the mitigation of attacks. Such techniques will look for incoming traffic patterns in order to identify malicious IP addresses based on factors such as request frequency, geographical dispersion, and deviation from normal usage patterns. Blacklisting malicious IPs, rate-limiting protocols, and deployment of CAPTCHAs for suspicious traffic are some of the ways generally used to reduce an ongoing attack. However, such methods require strong algorithms and processing to avoid inadvertently blocking legitimate users and minimum interference with normal network throughput. On the other hand, adaptive techniques take a proactive approach, emphasizing learning and evolution of new attack patterns recognition. These techniques employ machine learning algorithms to model typical traffic behavior and detect deviations that may indicate potential DDoS activity. For instance, clustering algorithms group similar patterns of traffic and flag anomalies, while classification models differentiate between legitimate and malicious traffic. Adaptive techniques often depend on historical data in order to refine detection rules and enhance response accuracy over time. Besides, they involve dynamic updating of firewalls and IDS firewalls based on real-time insights, making them more resilient to complex and changing DDoS tactics. The place of IP address analysis is vital in both active and adaptive strategies. While geolocation using IP can help to reveal suspect regions that contribute to unusual spikes in traffic, reverse DNS lookups also offer insight into the type of IP addresses involved: whether they come from known botnets or proxies, among others. Additionally, reputation-based systems rank the trustworthiness of each IP address based on previous activities, adding an extra layer of protection. Such techniques can be combined to enable organizations not only to counter the attacks that are currently being made but also to prepare for any future threats. One of the main issues in the practical application of these techniques is finding the right balance between security and the accessibility of the services to users. Too eager IP filtering may block legitimate users, especially when shared IP addresses are used. Similarly, real-time analysis and mitigation must be computationally light to avoid inadvertently creating network bottlenecks. For this, efficient algorithms are required together with scalable infrastructure that can handle large volumes of traffic without compromising response times. The combination of active and adaptive approaches for handling DDoS through IP address analysis forms a powerful framework for enhancing network resiliency. By integrating immediate mitigation strategies with predictive modeling and continuous learning, an organization can provide a comprehensive defense mechanism that not only combats the current attacks but also morphs into meeting future challenges. The study underlines the use of IP analysis in modern cybersecurity solutions and opens ways toward more secure and reliable network systems.

Keywords

Distributed Denial of Service (DDoS), IP Address Analysis, Active Defense Techniques, Adaptive Defense Mechanisms, Anomaly Detection, Traffic Filtering, Machine Learning in Cybersecurity, Intrusion Detection Systems (IDS), Network Resilience,Cybersecurity Threat Mitigation

Citations

IRE Journals:
Amudat Monsurat "Active and Adaptive Techniques for Handling Distributed Denial of Service by Analyzing IP Addresses" Iconic Research And Engineering Journals Volume 3 Issue 8 2020 Page 229-235

IEEE:
Amudat Monsurat "Active and Adaptive Techniques for Handling Distributed Denial of Service by Analyzing IP Addresses" Iconic Research And Engineering Journals, 3(8)