Distributed Denial of Service (DDoS) Attack Mitigation using AI

Abstract

Distributed Denial of Service(DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emergingtechnologies, suchascloudcomputing, InternetofThings(IoT), artificialintelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Naive Bayes and Random Forest trees are two examples of artificial intelligence techniques that have been used to detect and categorize DDoS attacks using machine learning algorithms. The paper provides advice on artificial intelligence techniques to be employed in DDoS attack detection and prevention, as well as a summary of the most recent developments in DDoS attack detection utilizing AI techniques.

Introduction

Distributed Denial of Service (DDoS) attacks use multiple distributed sources to overwhelm a target, denying legitimate users access to services. These attacks target system resources and network bandwidth and are difficult to detect because attack traffic often mimics normal traffic. DDoS attacks can vary in traffic volume and duration, with evolving complexity due to cloud computing, IoT, and AI technologies.

DDoS Attack Types and Features

• IP spoofing: Attackers disguise as trusted sources.

• Flooding attacks: Overwhelm servers by sending excessive traffic, including:

  • TCP flood (sending numerous SYN requests)

  • ICMP flood (Smurf attack)

  • UDP flood

  • DNS amplification (using DNS servers to amplify traffic)

Detection and Prevention

• Detection is challenging due to similarities between attack and legitimate traffic.

• Key detection features: packet count, size, rate, and traffic variance.

• Machine learning classifiers help distinguish normal vs. attack traffic by analyzing these features.

Artificial Intelligence in DDoS Detection

• AI techniques like machine learning (SVM, neural networks, Naive Bayes) are widely used.

• Deep learning models (CNN, RNN, LSTM, GRU) show improved detection by learning traffic patterns.

• AI enables early detection and mitigation through anomaly detection and classification.

Trends in DDoS Attacks

• Increasing attack volume, speed, and complexity.

• Multi-vector attacks using multiple attack types are becoming more common, complicating detection.

Applications and Case Studies

• Various AI-based detection systems have been developed using neural networks, big data platforms (Hadoop), and machine learning algorithms.

• Approaches include real-time monitoring, spoofed traffic detection (using hop count), immune system-inspired algorithms, and hybrid multi-factor detection.

• AI models improve accuracy in identifying and classifying attack types while reducing false positives.

Conclusion

DDoS assaults are one of the biggest risks to the Internet and can cause significant financial losses for both governments and businesses. Emerging technologies like cloud computing, the Internet of things, and artificial intelligence approaches have made it more difficult to identify and mitigate DDoS assaults and allowed attackers to launch them at a low cost. Naive Bayes and Random Forest trees are two examples of artificial intelligence techniques that have been used to detect and categorize DDoS attacks using machine learning algorithms. In the paper, we provide an overview of the most recent developments in artificial intelligence-based DDoS attack detection. Features including the number of packets, average packet size, time interval variance, packet size variance, number of bytes, packet rate, and bit rate can all be utilized to identify DDoS attacks. For their superior performance, we suggest using Naive Bayes and random forest trees among those artificial intelligence algorithms to distinguish between malicious and legitimate communications. DDoS assaults can be detected more accurately and efficiently by combining multiple machine algorithms.

References

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Copyright

Copyright © 2025 Aman Kumar, Aman Roy, Ankur Babu, Himesh Chaudhary, Dr. Saumya Chaturvedi, Ms. Poonam Verma, Dr. Sureshwati . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.