The expansion of Internet of Things (IoT) devices and the deployment of 5G networks have created new vulnerabilities for network operators and IT managers to contend with. One of these vulnerabilities is Distributed Denial of Service (DDoS) attacks, which are becoming increasingly sophisticated and difficult to detect and mitigate.
In the past, DDoS attacks were relatively simple and short-lived. However, modern DDoS attacks have evolved into carefully orchestrated campaigns that involve reconnaissance, tailored attacks, and real-time monitoring for effectiveness. These new types of attacks, known as “adaptive DDoS” attacks, are not limited to nation-states but are also prevalent in other sectors such as healthcare and business.
To effectively defend against adaptive DDoS attacks, organizations must be prepared and implement edge-based detection and mitigation methods. It is crucial to understand that these attacks have grown more advanced and are no longer the work of rogue threat actors. Attackers now perform extensive pre-attack scouting, exploiting weaknesses, and utilizing botnet nodes and reflectors/amplifiers that are close to the target. By minimizing the number of administrative boundaries the attack traffic must traverse, attackers can make it more difficult to detect and mitigate the attacks.
A recent example of the impact of adaptive DDoS attacks is the targeting of the U.S. healthcare industry by Russian hacktivists. The U.S. Department of Health and Human Services (HHS) has already warned about these attacks, which specifically target ventilators. The Russian hacktivist group, Killnet, has claimed responsibility for multiple DDoS attacks on U.S. healthcare organizations, including major hospital networks like Cedars-Sinai and Duke University Hospital. This demonstrates the level of planning, execution, and sustainment that goes into these attacks.
To combat the growing complexity of DDoS attacks, organizations need to adopt new strategies, including dynamic defenses that can adapt to input as attacks evolve. This requires the use of edge-based detection and mitigation solutions that can automatically identify and stop all types of DDoS attacks before they impact critical services. Such solutions should combine intelligent machine learning algorithms with up-to-date DDoS threat intelligence to effectively counter adaptive DDoS attacks.
Implementing edge-based detection and mitigation solutions is essential due to the potential damage that can be caused by short-duration attacks on critical business applications and services. These solutions sit at the edge of the network and form the foundation of a multi-layered DDoS defense against sophisticated attackers. Additionally, the solution must be integrated with upstream mitigation to handle volumetric attacks that exceed the bandwidth available at the network edge.
It is crucial to note that relying solely on static mitigations and upstream defenses is insufficient, as it can miss attacks designed to evade these defenses. Organizations must adapt their defense strategies to changing tactics and adopt solutions that can keep up with the ever-evolving threat landscape. By taking a proactive approach to DDoS protection, organizations can effectively safeguard their critical business services from increasingly organized and methodical attackers.
In conclusion, the rise of IoT devices and the continued deployment of 5G networks present new challenges for network operators and IT managers. One of these challenges is the increasing prevalence of adaptive DDoS attacks, which are becoming more sophisticated and difficult to detect and mitigate. To defend against these attacks, organizations must implement edge-based detection and mitigation solutions that can counter the evolving tactics used by attackers. By staying one step ahead, organizations can ensure effective DDoS protection and safeguard their critical business services.