Opportunities and challenges of applying AI to DDoS attack mitigation

Latest Update: 08/07/2024

Opportunities and challenges of applying AI to DDoS attack mitigation

"AI is a dual-use technology, employed for both offensive and defensive purposes. The most significant threat lies in hackers leveraging AI to craft more sophisticated attacks, develop malware, scan for vulnerabilities, and bypass security measures to infiltrate information systems," stated Mr. Tran Dang Khoa, Deputy Director General in charge of the Information Security Department (ATTT - Ministry of Information and Communications) (March 2024). DDoS attacks exemplify this trend. With the increasing sophistication and severity of these attacks, artificial intelligence (AI) has emerged as a promising weapon to combat this threat. However, AI can also be a double-edged sword if not utilized effectively and securely. In this article, VNETWORK delves into the opportunities and challenges presented by AI in the realm of DDoS mitigation, introducing a comprehensive security solution to safeguard businesses from this threat.

What is DDoS attack?

Distributed Denial-of-Service (DDoS) attacks are malicious attempts to disrupt or disable the normal operation of a targeted server, service, or network by overwhelming it with a massive influx of traffic. Attackers typically employ botnets - networks of compromised computers controlled remotely - to send a barrage of requests to the target.

DDoS attacks can be categorized into three primary types:

  1. Volume-based attacks: Encompass ICMP floods, UDP floods, and other spoofed-packet attacks. The primary objective is to saturate the bandwidth of the targeted website.
  2. Protocol attacks: Include SYN floods, fragmented packet attacks, Ping of Death, and Smurf DDoS. These attacks deplete resources on the server or intermediary networking devices, such as firewalls and load balancers.
  3. Application layer attacks: Typically target specific web services by generating legitimate but abnormally high-frequency requests. Common forms include HTTP floods, Slowloris, and GET/POST floods. These attacks can be particularly severe as they target the application layer, making detection and prevention more challenging.

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AI-powered DDoS attacks: A looming threat to cybersecurity

The advent of artificial intelligence (AI) has opened new doors for cybercriminals, who are increasingly incorporating AI into their attack tools. AI-driven systems, empowered by vast amounts of data, enable cybercriminals to launch distributed denial-of-service (DDoS) attacks with unprecedented effectiveness. The rapid decision-making, problem-solving, and even predictive capabilities of AI elevate DDoS attacks to a whole new level of danger. Here are some of the key reasons:

  • Elimination of the human factor: AI automates the attack process entirely, making it more challenging for traditional defense measures to identify and track down the perpetrators. 24/7 operation: Machines are not bound by fatigue or rest requirements like humans, allowing them to sustain continuous attacks (24/7) for extended periods without interruption.
  • High precision: AI systems trained on massive datasets can execute attack actions with high precision and near-zero error rates.
  • Predictive and adaptive capabilities: AI can analyze the behavior of the target system and predict defensive responses, adjusting attack strategies dynamically to optimize effectiveness.

AI in DDoS Mitigation: An inevitable trend

As DDoS attacks grow more sophisticated and large-scale, the integration of artificial intelligence (AI) into DDoS mitigation solutions has become an imperative. AI not only automates the process of attack detection and prevention but also provides high adaptability to emerging threats while optimizing security costs. According to Zscaler ThreatLabz's ThreatLabz 2024 AI Security Report, AI-powered DDoS mitigation offers the following benefits:

  • Reduced threat detection time from 197 to 48 days: With superior data analysis and machine learning capabilities, AI can detect potential threats faster and more accurately, enabling businesses to take proactive measures before damage occurs.
  • 30% reduction in security costs: AI automates costly tasks, enhances operational efficiency, and optimizes resource utilization, leading to significant cost savings for businesses.

Given AI's ability to learn from patterns and detect anomalies, it is an ideal technology for enhancing DDoS mitigation strategies. AI implementation in DDoS attack prevention can be categorized into several key aspects:

1. Anomaly detection

According to the Cisco 2020 Cybersecurity Report, AI-powered systems can reduce threat detection time to mere seconds compared to traditional methods that take minutes or hours. Additionally, an MIT study revealed that AI systems can detect attacks with up to 85% accuracy by analyzing historical data.

AI algorithms can analyze network traffic in real-time to identify patterns that deviate from the norm. Machine learning models, particularly those employing supervised learning techniques, can be trained on historical data to recognize the signs of a DDoS attack. By continuously monitoring traffic, AI can detect sudden spikes or abnormal patterns indicative of an ongoing attack and trigger appropriate countermeasures.

2. Behavior analysis

An RSA Security study found that AI systems can reduce false alarm rates by up to 90% through behavior analysis. AI can distinguish between the behavior of regular users and malicious activities. This involves establishing a baseline of normal traffic patterns and identifying deviations that could indicate an attack. Techniques like clustering and classification can aid in understanding and predicting attack vectors based on user behavior analysis.

3. Automated response

An IBM Security study revealed that using AI for automated response can minimize downtime by up to 95%. Once an attack is detected, AI systems can automate the response to mitigate its impact. This includes adjusting firewall rules, rerouting traffic, or activating additional security protocols. AI-driven automation ensures rapid response, minimizing downtime and the damage caused by the attack.

4. Predictive analytics

According to Accenture, AI systems can reduce the number of successful attacks by up to 30% by predicting and preventing them before they occur. Additionally, a Capgemini report found that 69% of organizations using AI for prediction have seen significant improvements in network attack detection and prevention.

AI can also be used to predict potential DDoS attacks before they happen. By analyzing trends and using predictive modeling, AI systems can identify vulnerabilities and suggest proactive measures to strengthen defenses. This proactive approach helps prepare for potential threats rather than simply reacting to them.

5. Traffic filtering

Juniper Research predicts that AI will help reduce traffic filtering costs by up to $10 billion annually by 2023. Additionally, according to a Symantec report, AI systems can enhance the ability to differentiate between legitimate and malicious traffic with up to 95% accuracy.

AI can improve traditional traffic filtering methods by applying advanced filtering algorithms that more accurately distinguish between legitimate and malicious traffic. Machine learning models can be trained to identify and filter out malicious traffic, reducing network load and preventing attacks from causing significant damage.

ai-applications-in-ddos-mitigation.png AI applications in DDoS mitigation

With the aforementioned advantages, cybersecurity systems that seamlessly integrate AI into their security frameworks can significantly enhance the ability to identify, respond to, and mitigate DDoS attacks when they occur. Artificial intelligence (AI) provides a robust solution against DDoS attacks, offering the capability to detect subtle anomalies that signal a DDoS attack, coupled with continuous monitoring to promptly block malicious traffic.

According to AI Global Media, amidst businesses' continuous efforts to bolster their security defenses, seeking an AI-powered DDoS solution is the most effective approach to safeguarding their operations. Businesses should prioritize security solutions that integrate emerging technologies to enhance their security posture, expand threat intelligence, and establish a comprehensive security architecture.

VNIS - An AI-powered security solution providing comprehensive protection for businesses against all DDoS attacks

In the digital era, DDoS attacks are becoming an increasingly prevalent threat, particularly AI-powered application attacks with growing sophistication and scale. Recognizing this, VNETWORK has pioneered the development of the VNIS platform - an advanced AI-powered DDoS protection solution that provides comprehensive protection for your systems against all attack risks.

VNIS safeguards your business systems with:

VNIS comprehensive security model VNIS platform's comprehensive security model

Robust Global Infrastructure

VNIS integrates and manages the world's leading CDNs on a single platform, offering robust system capabilities and flexible scalability. With over 280 PoPs (connection points) of CDN globally, domestic bandwidth exceeding 9 Tbps, a load capacity of over 8,000,000 CCU (concurrent users), and handling over 9 billion requests daily, VNIS ensures business websites remain stable even under attack, optimizing user experience and business security.

AI and Machine Learning integration into management systems

VNIS is equipped with an advanced Multi WAF system with multiple Cloud WAF clusters strategically positioned worldwide. Leveraging its dense cloud infrastructure, VNIS can quickly isolate threats when website traffic surges. The WAF network monitoring system (Scrubbing Center) coordinates the operation of Cloud WAF clusters across multiple countries, effectively combating Layer 7 DDoS attacks.

Capitalizing on AI advancements, VNETWORK has developed an Intelligent Load Balancing System (AI Load Balancing) combined with Real User Monitoring (RUM) to provide detailed analysis of attack sources, real user interaction reports, and traffic routing to the website. AI Load Balancing, in conjunction with CDN, RUM, and Synthetic Monitoring, automatically detects the shortest path between the server and the user, optimizing traffic routing. Additionally, AI Load Balancing enables load balancing across multiple servers with options including IP hash, round-robin switching, and failover.

vnis_homepage_protected.svg VNIS solution leverages AI advancements

AI Load Balancing offers numerous advantages:

  • Multi-CDN performance optimization: AI considers user geolocation, response speed, current load, and capacity of each CDN node to make optimal traffic routing decisions. This not only balances the load but also ensures the best user experience.
  • Traffic surge prediction and preparation: AI analyzes historical data and trends to predict traffic surges, allowing the system to prepare in advance by adjusting configuration for traffic allocation.
  • WAF integration: AI can adjust traffic distribution based on threats detected by WAF.
  • Continuous learning: AI can continuously learn from new situations, improving decision-making over time. This is crucial in addressing emerging security threats and changes in user behavior.
  • Real-time response: AI enables the system to respond almost instantaneously to changes in network conditions, attacks, or user demands, ensuring service continuity and stability.

By integrating AI into load balancing, VNIS not only effectively distributes the load and protects against attacks but also has the ability to self-improve and adapt to new challenges in the future, encompassing aspects such as: Optimal performance: Intelligent load balancing based on real-time access data ensures smooth system operation, meeting all access demands regardless of fluctuations.

  • Comprehensive security: Protects against all sophisticated DDoS attacks, safeguarding systems from all risks. Effectively detects and blocks malicious access attempts. Minimizes damage caused by cyberattacks.
  • Self-learning and adaptability: VNIS continuously learns from past DDoS mitigation experiences, automatically updating defense strategies to counter emerging threats, ensuring sustainable system security for businesses.

24/7 SOC Expert Support

Recognizing the urgency and timeliness of security, VNETWORK has established Security Operation Centers (SOCs) with a readiness to respond in emergencies to minimize losses. VNETWORK's SOC teams are now present in Vietnam, Hong Kong, Taiwan, Singapore, and the UK to support and collaborate with businesses against any attacks.


Technological advancements have fueled increasingly sophisticated and dangerous DDoS attacks. Attackers continuously refine techniques and tools to bypass defenses. Therefore, staying up-to-date and deploying advanced security solutions is crucial to protect systems from DDoS threats, minimize and prevent system downtime, impact user experience, and even lead to financial losses for businesses.

VNIS, VNETWORK's advanced AI-powered DDoS protection solution, offers comprehensive, effective, and easy-to-deploy protection, making it the perfect choice for businesses seeking to safeguard their systems against all attack risks. Contact VNETWORK today for free VNIS solution consultation and protect your systems from any DDoS attack via hotline: +84 (028) 7306 8789 or email:

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