Small, Bit-and-Piece Cyber Attacks increased by 233% in 2021

Small bit-and-piece DDoS attacks (distributed denial of service) boomed by 233% in the first half of 2021, revealed Nexusguard researchers after gathered data from CSPs, honeypots, botnet scanning and research on traffic moving between attackers and their targets. As the pandemic carried on into 2021, hackers experimented with new attack patterns to avoid signature-based detection. In the first half of 2021, more than 99% of all DDoS attacks were smaller than 10Gbps.These small, nimble attacks can cripple communications service providers and Internet service providers if they leave detection to threshold or signature-based methods alone.

Attackers are continuing to diversify their approaches with bit-and-piece attacks to bring down target networks and infrastructures. More than 95% of attacks were smaller than 1Gbps each, and a majority were launched using readily available and inexpensive DDoS-for-hire services. Rather than launching large bandwidth attacks against their targets, researchers noted that perpetrators chose to employ attacks using high packet-rate loads of small-sized traffic from DDoS-for-hire services, with the aim of evading DDoS mitigation detection systems.

The high level of intricacies behind communications service provider networks causes them to generally allow all types of traffic to pass through, which leads to smaller or spoofed types of attacks to strike undetected. Hence, Behavioural detection and mitigation approaches are strongly recommended for targeted networks since they can compare peacetime with battles and take a wider range of factors into consideration than anomalous thresholds or attack signatures.

The researchers witnessed several service providers bear the brunt of DDoS attacks, including China Telecom, China Unicom, Vodafone Türkiye, Türk Telekom, Turkcell İletişim Hizmetleri A.S. receiving the highest concentrations of malicious traffic. It further advises, CSPs to use deep learning-based detection methods, which can help CSPs analyze huge amounts of data quickly and accurately while overcoming the inefficiencies inherent in threshold or signature-based methods.