7 Types of DDoS Attacks and 5 Methods and Tools You Can Use to Detect Them

As a professional webmaster, I’ve managed around 30 websites over the years, and only two of them have ever faced serious DDoS attacks. Interestingly, both were high-traffic sites that generated significant ad revenue.

These attacks happened about 14 years ago, and at the time, I didn’t have the budget for high-level solutions to keep the sites online. The best I could do was try to migrate to more powerful servers — but unfortunately, that didn’t help much. 

Today, there are many affordable ways to protect against DDoS attacks (like using Cloudflare, for example). However, in this article, I won’t focus on protection methods. Instead, I’ll discuss the different types of DDoS attacks being used today and how to detect them effectively. 

This way, if you ever come under attack, you’ll be able to quickly identify the type of DDoS attack you're facing — and take the right steps to mitigate it.

1. Volumetric Attacks:

Volumetric DDoS attacks aim to exhaust the bandwidth of a targetsystem or    network. These attacks typically involve sending massive amounts of fake traffic using spoofed IP addresses. Common techniques include UDP flood, ICMP flood, and DNS amplification.

Detection usually involves spotting sudden traffic spikes, network delays, or service outages. These can be monitored using traffic analysis tools like Wireshark, NetFlow, or Cloudflare Analytics.

To mitigate these attacks, solutions such as CDNs, rate limiting, smart WAFs that can distinguish real from fake traffic, and anti-DDoS services like Cloudflare, AWS Shield, or Radware are commonly used.

From the attacker's side, volumetric DDoS attacks are relatively cheap and easy to launch, often relying on botnets or rented DDoS-for-hire services — yet they can cause major disruption.

2. SYN Flood (Protocol Attack):

SYN Flood attack is a type of protocol-based DDoS attack that aims to exhaust the target system’s TCP connection resources, rendering it unable to respond to legitimate users.

The attacker sends a large number of SYN (connection initiation) requests using spoofed IP addresses but intentionally does not complete the three-way handshake. Since the server allocates resources for each incoming SYN request, these resources eventually get consumed, and real users are unable to connect.

Detection involves monitoring for an unusually high number of SYN packets in the network traffic. This can be done with tools like IDS/IPS systems, Wireshark, or NetFlow analyzers.

Prevention techniques include using SYN cookies, applying rate limiting on connection attempts, configuring firewalls to limit half-open connections, and activating DDoS protection services.

From the attacker’s point of view, this type of attack is low-cost, as it consumes system resources with minimal data.

3.HTTP Flood (Layer 7 Attack):

HTTP Flood attack is a type of DDoS attack that occurs at the applicationlayer (Layer 7). It aims to overload the target web server with a high volume of seemingly legitimate but malicious HTTP requests.

These attacks are typically carried out by bots that mimic real browser behavior, making them difficult to detect. To identify such attacks, one must analyze unusual request volume on the web server, repeated traffic from specific IPs, or patterns in user behavior. Detection tools include WAFs, log analysis platforms, and behavioral analysis systems.

To mitigate HTTP Flood attacks, you can use bot filtering, CAPTCHA challenges, user authentication, WAF rule configurations, and rate limiting.

From the attacker's perspective, this method is more expensive compared to other types of DDoS attacks, as it requires more bandwidth, processing power, and sometimes advanced bot software.

4. Slowloris Attack:

Slowloris attack is a type of DDoS attack that targets a web server by sending numerous HTTP requests and deliberately keeping each one incomplete, with the goal of exhausting the server’s connection pool.

This attack is particularly effective against servers like Apache that allocate resources per connection. The attacker can tie up server threads using very little bandwidth, essentially locking the server without needing high-volume traffic.

Detection is difficult because the requests are technically valid. However, signs such as a large number of long-lasting, incomplete connections can indicate a Slowloris attack. Monitoring tools that track connection duration and request completion can help detect it.

To mitigate this kind of attack, you can shorten connection timeouts, limit the number of concurrent connections, and use a reverse proxy (like Nginx) or a web application firewall (WAF).

From the attacker's side, it's a very low-cost method — even a small device with minimal bandwidth can cause significant service disruption.

5. Ping of Death:

Ping of Death is an older but occasionally still-seen type of DDoS attack that involves sending oversized or malformed ICMP (ping) packets to a target system in an attempt to crash or destabilize it.

This attack is especially effective on systems that are vulnerable to buffer overflows during packet reassembly, potentially making the operating system unstable or causing it to crash.

Detection usually involves monitoring for unusually large ICMP packets in network traffic. This can be done using network monitoring tools or intrusion detection systems (IDS).

Modern operating systems and networking hardware typically have built-in protections against this kind of attack. However, firewalls can also be configured to filter ICMP packets and limit their size as an additional precaution.

For attackers, Ping of Death is low-cost and technically simple, but it only works if the target system is unpatched or running outdated security configurations.

6. DNS Amplification Attack:

DNS Amplification Attack is a reflection-based DDoS attack where the attacker sends small DNS queries with a spoofed source IP address (the victim’s IP) to open DNS servers. These servers then respond with large DNS replies, which are directed to the victim. The goal is to flood the target’s bandwidth with a high volume of traffic, effectively taking the system offline.

Detection involves analyzing abnormal spikes in DNS traffic and noticing a surge in DNS responses coming from multiple sources at once. This can be done through network monitoring systems and dedicated DDoS protection tools.

To prevent this type of attack, administrators can disable recursive DNS queries on public servers, apply rate limiting, and implement filters to prevent IP spoofing within their network.

From the attacker’s perspective, DNS amplification is low-cost and highly efficient — small input queries generate large output responses, making it a powerful way to exhaust the victim's resources.

7. Teardrop Attack:

Teardrop attack is a type of DDoS attack where the attacker sends malformed and fragmented IP packets to a target system. These packets are crafted in a way that causes errors or memory overflow during reassembly, potentially crashing the system or forcing it to reboot.

This attack mainly affects older operating systems and network stacks that can’t properly handle such irregular packet structures.

Detection involves analyzing inconsistencies in the fragment offset values of incoming IP packets. This can be done using advanced IDS/IPS systems or packet analysis tools.

To prevent this kind of attack, most modern operating systems and network devices are already patched against it. Additionally, network filters can be configured to block invalid or suspicious fragmented packets.

From the attacker’s point of view, a teardrop attack is low-cost and technically simple. However, it’s largely ineffective against up-to-date systems, so its success depends on the target being outdated or vulnerable.

5 Methods & Tools to Detect DDoS Attacks

1. Traffic Analysis & Anomaly Detection

Traffic Analysis & Anomaly Detection is a method used to continuously monitor network traffic and detect deviations from normal behavior. It’s one of the most widely used techniques for identifying DDoS attacks in their early stages.

This method involves analyzing metrics such as the number of connections, packet sizes, protocol usage, and source IP distribution. Sudden traffic spikes, repeated requests, or unusual patterns may indicate an ongoing attack.

Common tools used for this type of analysis include Wireshark, NetFlowAnalyzer, Zabbix, ntopng, and Darktrace. Some are open-source and free, while others  especially enterprise or AI-powered versions  can be more costly.

However, these tools alone aren’t enough. For effective defense, they need to work in conjunction with firewalls, WAFs, rate limiting, and anti-DDoS services. This approach is crucial for identifying both the type and source of the attack, but for real-time protection, it should be part of a multi-layered security strategy.

2. Real-Time Monitoring & Alerting Systems

Real-Time Monitoring & Alerting Systems are designed to instantly detect issues such as downtime, slow response times, or unusual traffic patterns in networks, servers, or web services — and immediately notify the user.

This method helps identify DDoS attacks the moment they begin, minimizing response time. When certain thresholds or response times are exceeded, the system automatically triggers alerts.

Tools like  RobotAlp, Pingdom, Better Uptime, and HetrixTools offer these types of monitoring and alerting services. Many have free basic plans, while more advanced features require a paid subscription.

However, these systems are built to detect attacks, not to stop them or filter the traffic. That’s why they should be used alongside preventive layers like firewalls, anti-DDoS services, and proper network configurations.

Real-time monitoring is a critical component of an effective defense strategy, but it is not sufficient on its own.

3. Firewalls and IDS/IPS Solutions

Firewalls and IDS/IPS Solutions are security technologies used to detect and block DDoS attacks at the network level.

Firewalls control access based on ports, IP addresses, or traffic types, while IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) analyze suspicious traffic patterns and can automatically intervene if needed.

These systems are particularly effective in detecting DDoS attacks like SYN Floods, DNS Amplification, and HTTP Floods.

Popular tools in this category include Fortinet, Palo Alto Networks, Snort (open-source), Suricata, and Cisco ASA. While enterprise-level solutions are usually license-based and more expensive, open-source options offer more affordable alternatives.

However, these systems alone may not always be sufficient — especially against advanced or distributed attacks. For more comprehensive protection, they should be combined with real-time monitoring, traffic analysis, and CDN-backed DDoS protection services.

4. Log Monitoring and Analysis

Log Monitoring and Analysis is a method used to detect unusual activities related to DDoS attacks by examining log data generated by servers, network devices, and applications.

This approach involves analyzing signs such as a high number of failed connection attempts in a short time, unexpected traffic spikes, or heavy request loads coming from specific IP addresses. These patterns can help identify a DDoS attack in its early stages.

Common tools for this method include ELK Stack (Elasticsearch, Logstash, Kibana), Graylog, Splunk, and Fluentd. Some of these are open-source and free, while others—especially enterprise-grade versions—can be quite costly.

Log analysis is very effective in understanding the source, duration, and impact of an attack. However, it is generally not sufficient for prevention on its own. For best results, it should be integrated with real-time monitoring systems, firewalls, and anti-DDoS solutions.

5. AI-Powered Detection Systems

AI-Powered Detection Systems are advanced methods that use artificial intelligence and machine learning to detect DDoS attacks in real time by learning both normal and abnormal patterns in network traffic.

These systems analyze historical traffic data to establish a baseline of what is considered “normal” behavior. When traffic deviates from these patterns, the system flags it as a potential threat and issues alerts accordingly.

Some of the leading tools in this space include Darktrace, Cloudflare Spectrum (AI-assisted analysis), Radware DefensePro, and Fortinet FortiDDoS. While these enterprise-grade solutions offer high detection accuracy, they also come with significant licensing and setup costs.

AI-powered systems are particularly valuable in large and complex networks, where they can detect even unknown types of attacks before they escalate. However, they are most effective when integrated with traditional security measures. As part of a multi-layered defense strategy, they offer strong protection against DDoS attacks.