DDoS detection is the process of distinguishing distributed denial of service (DDoS) attacks from normal network traffic in order to perform effective attack mitigation. The primary goal of a DDoS attack is to either limit access to an application or network service, thereby denying legitimate users access to the services.
There are many types of DDoS attack schemes that are used today and they are steadily becoming more sophisticated. However, their common goal is to overwhelm targeted network resources with traffic or requests for service from many different sources — potentially hundreds of thousands or more. This effectively makes it impossible to stop the attack simply by identifying and blocking a single IP address. The sheer distribution of attacking sources also makes it very difficult to distinguish legitimate user traffic from attack traffic when spread across so many points of origin.
The first step in avoiding or stopping a DDoS attack is knowing that an attack is taking place. To detect an attack, one has to gather a sufficient network traffic information, then perform analysis to figure out if the traffic is friend of foe. This process can be performed manually or in an automated fashion. DDoS detection is the key to quickly stopping or mitigating attacks and in order for this to happen, two success criteria need to be met:
So detection methods are a key consideration in formulating a strong DDoS defense.
There is no doubt, as evidenced in the alarming rise of DDoS attacks, that DDoS detection is an absolute necessity for businesses that rely on internet traffic in order for them to avoid disruption of applications and services, revenue loss, and brand damage. Neustar, Inc. and others regularly publish reports on DDoS attack and protection trends. A Neustar report highlights that DDoS attack volume has remained consistently high and that these attacks cause real damage to organizations. Some highlights from that report:
There are two primary means of detecting DDoS attacks: in-line examination of all packets and out-of-band detection via traffic flow record analysis. Either approach can be deployed on-premises or via cloud services. The basic in-line DDoS detection capabilities of network devices such as load balancers, firewalls or intrusion prevention systems may have once provided acceptable detection when DDoS attacks were smaller but high-volume attacks can overwhelm these devices, since they utilize memory-intensive stateful examination methods.
Dedicated DDoS mitigation appliances are the primary way to accomplish in-line detection (and remediation) today. However, they can become costly and have a short life cycle in the face of higher volume threats. These appliances are still necessary and relevant for mitigation because ASIC and network processor power is needed for deep packet inspection when scrubbing traffic but for cost-effectiveness and scaling reasons, moving detection out of mitigation devices has become the norm.
Out-of-band DDoS detection is accomplished by a process that receives flow data from NetFlow, J-Flow, sFlow, and IPFIX-enabled routers and switches, then analyzes that flow data to detect attacks. Mitigation of the attacks is then triggered manually or automatically, via routing or appliance-based methods.
The first generation of out-of-band DDoS detection solutions were based on single server software design, mostly running on standalone rack-mounted server appliances.
While far better than nothing, single servers simply don’t have the compute, memory and storage resources to track high volumes of traffic data on a network-wide basis. This is particularly true when attempting to perform dynamic baselining, which requires scanning massive amount of flow data to understand what is normal, then looking back days or weeks in order to assess whether current conditions constitute an anomaly. Regardless of whether it is deployed on-premises or in the cloud, single server DDoS detection is insufficient to accurately detect today’s attacks in a consistently reliable fashion.
By leveraging big data technologies for storing network events as they happen and by accessing this data repository in the cloud, customers can avoid DDoS detection appliances that fail to scale as their on-premise networks grow and/or re-deploy in the cloud, or avoid expensive in-house projects that require ongoing investments or obsolete as open software frameworks change.
Kentik offers the industry’s only big-data network visibility and DDoS defense solution built from the ground up on big data and delivered as a cost-effective SaaS. Kentik offers the industry’s most accurate DDoS detection, and can automatically trigger mitigation via RTBH, Radware DefensePro or A10 Thunder TPS mitigation.
For more information on how big data delivers greater DDoS detection speed and accuracy, check out the blog post on Automated, Accurate, Flexible DDoS Detection and Mitigation, watch the Superloop video, and read the Immedion case study.
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