FastNetMon DDoS Attack: 1.5 Billion Packets Per Second Strike on Scrubbing Vendor
FastNetMon DDoS Attack: 1.5 Billion Packets Per Second Strike on Scrubbing Vendor
FastNetMon, a global leader in DDoS detection, has confirmed it successfully identified and mitigated a record-scale distributed denial-of-service (DDoS) attack targeting the website of a leading scrubbing provider in Western Europe.
The assault peaked at 1.5 billion packets per second (1.5 Gpps), making it one of the largest packet-rate floods ever disclosed publicly. Unlike bandwidth-driven attacks, this one focused on packet volume, where each packet requires routers and firewalls to expend processing power, rapidly exhausting resources.
The attack was powered by a massive botnet of hijacked customer-premises equipment (CPE), including Internet of Things (IoT) devices and home routers. Spread across over 11,000 unique networks worldwide, these compromised devices generated an overwhelming flood of UDP packets, crippling defenses and straining regional internet links.
What makes this incident significant is the methodology: a flood of smaller UDP packets can in many cases be more damaging than high-bandwidth floods. Each packet requires inspection and processing, creating a choke point even on advanced hardware.
This disclosure comes shortly after Cloudflare’s mitigation of an 11.5 Tbps attack, highlighting how adversaries are escalating both volume and rate. While Cloudflare’s event was measured in terabits of traffic, FastNetMon’s case shows the destructive power of packet-rate floods.
Pavel Odintsov, Founder of FastNetMon, emphasized the broader risks:
“When tens of thousands of CPE devices can be hijacked and used in coordinated packet floods of this magnitude, the risks for network operators grow exponentially. The industry must act to implement detection logic at the ISP level to stop outgoing attacks before they scale.”
FastNetMon’s Advanced platform, built with optimized C++ algorithms for real-time network visibility, was able to spot the anomaly within seconds. Automated mitigation measures were triggered, rerouting or dropping malicious traffic before it could degrade services. Thanks to these real-time defenses, the targeted vendor experienced no downtime or performance loss.
The incident illustrates the growing sophistication of DDoS threats. While scrubbing providers are designed to resist such attacks, adversaries are now weaponizing everyday consumer electronics to launch massive coordinated floods.
The takeaway is clear: multi-layered defense strategies are essential. Detection at the ISP level, coupled with high-speed automated platforms like FastNetMon, will be critical in countering future waves of DDoS escalation.
More info here – Have a Story? Address it to the Editor and submit it here
About FastNetMon
FastNetMon is a global cybersecurity company specializing in real-time detection and mitigation of distributed denial-of-service (DDoS) attacks. Founded by Pavel Odintsov, the platform is widely used by internet service providers, hosting companies, and enterprises that require high-speed defense against network threats. FastNetMon’s core strength lies in its advanced algorithms written in optimized C++, enabling it to analyze massive volumes of network traffic within seconds. This capability allows customers to instantly detect anomalies, such as large-scale packet floods, and trigger automated mitigation before critical services are disrupted.
The system is designed to integrate seamlessly with major mitigation solutions and provides full visibility across complex infrastructures. By focusing on packet-per-second detection rather than just bandwidth monitoring, FastNetMon helps organizations withstand even the most sophisticated attacks. With the growing use of IoT devices in botnets, FastNetMon plays a crucial role in securing networks against the escalating threat landscape.
Featured Image Source: MSN
Disclaimer
The information provided in this article is for general informational purposes only and is derived from publicly available sources. While every effort is made to ensure accuracy, we make no representations or warranties, express or implied, regarding the completeness, reliability, or validity of the content. This article does not assert or verify any claims about specific companies, individuals, or organizations. References to external reports, studies, or sources are for contextual purposes only and do not imply endorsement or confirmation of any specific allegations. Readers are advised to conduct their own due diligence and seek professional advice before making business or investment decisions. We disclaim any liability for losses or damages incurred as a result of reliance on the information provided.