Average DDoS attack volumes grew by 194% in 12 months
The volume and complexity of DDoS attacks continued to grow in Europe during the final quarter of 2018, according to Link11. While Link11’s Security Operations Center (LSOC) registered 13,910 attacks in Q4 (12.7% down compared to Q3), the average attack volume grew by 8.7% to 5Gbps, and 59% of attacks used multiple attack vectors.
Key findings of Link11’s Q4 DDoS report include:
- Average attack volumes grew by 194% in 12 months: In Q4 2018, average attack volumes were 5Gbps, nearly treble the 1.7Gbps average seen in Q4 2017. Attackers are using increasingly powerful botnets comprising misused cloud servers, hijacked IoT devices and embedded devices.
- Hyper-scale attacks hitting hard: In Q4, the LSOC registered 13 attacks with volumes over 80Gbps. The biggest attack reached 173.5 Gbps – well over double the volume of the biggest attack seen in Q4 2017, which hit 70.1 Gbps.
- More complex multi-vector attacks: The majority (59%) of attacks in Q4 2018 were multi-vector attacks, compared with 45% in Q4 2017. The most complex attacks seen in Q4 used up to nine different attack vectors. The three most commonly used reflection amplification vectors were CLDAP, DNS reflection and SSDP.
The LSOC also observed that attacks occurred most frequently on Saturdays and Sundays, with the level of attacks declining during the business week. Attackers targeted organizations most frequently between 4 pm and midnight Central European Time, with attack volumes at their lowest between 6 am and 10 am CET.
“The increase in the impact and complexity of attacks continues unabated,” said Marc Wilczek, COO of Link11. “When faced with DDoS bandwidths well over 100 Gbps and multi-vector attacks, traditional IT security mechanisms are easily overwhelmed, and unprotected companies risk serious business disruption, loss of revenue and even fines. To stop these attacks disrupting business operations, organizations need proactive protection that tracks and responds to evolving attack scenarios and patterns automatically, using advanced machine-learning techniques.”