By actively exploring network traffic with Kentik Detect you can reveal attacks and exploits that you haven’t already anticipated in your alerts. In previous posts we showed a range of techniques that help determine whether anomalous traffic indicates that a DDoS attack is underway. This time we dig deeper, gathering the actionable intelligence required to mitigate an attack without disrupting legitimate traffic.
Kentik Detect is powered by Kentik Data Engine (KDE), a massively-scalable distributed HA database. One of the challenges of optimizing a multitenant datastore like KDE is to ensure fairness, meaning that queries by one customer don’t impact performance for other customers. In this post we look at the algorithms used in KDE to keep everyone happy and allocate a fair share of resources to every customer’s queries.
With massive data capacity and analytical flexibility, Kentik Detect makes it easy to actively explore network traffic. In this post we look at how to use this capability to rapidly discover and analyze interesting and potentially important DDoS and other attack vectors. We start with filtering by source geo, then zoom in on a time-span with anomalous traffic. By looking at unique source IPs and grouping traffic by destination IP we find both the source and the target of an attack.
If your network visibility tool lets you query only those flow details that you’ve specified in advance then you’re likely vulnerable to threats that you haven’t anticipated. In this post we’ll explore how SQL querying of Kentik Detect’s unified, full-resolution datastore enables you to drill into traffic anomalies, to identify threats, and to define alerts that notify you when similar issues recur.
DDoS attacks impact profits by interrupting revenue and undermining customer satisfaction. To minimize damage, operators must be able to rapidly determine if a traffic spike is a true attack and then to quickly gather the key information required for mitigation. Kentik Detect’s Data Explorer is ideal for precisely that sort of drill-down.
Kentik Detect handles tens of billions of network flow records and many millions of sub-queries every day using a horizontally scaled distributed system with a custom microservice architecture. Instrumentation and metrics play a key role in performance optimization.
The team at Kentik recently tweeted: “#Moneyball your network with deeper, real-time insights from #BigData NetFlow, SNMP & BGP in an easy to use #SaaS.” There are a lot of concepts packed into that statement, so we thought it would be worth unpacking for a closer look.
For many of the organizations we’ve all worked with or known, SNMP gets dumped into RRDTool, and NetFlow is captured into MySQL. This arrangement is simple, well-documented, and works for initial requirements. But simply put, it’s not cost-effective to store flow data at any scale in a traditional relational database.
Kentik Detect’s alerting system generates notifications when network traffic meets user-defined conditions. Notifications containing details about the triggering conditions and the current status may be posted as JSON to Syslog and/or URL. This post shows how to parse the JSON with PHP to enable integration with external ticketing and configuration management systems.
Taken together, three additional attributes of Kentik Detect — self-service, API-enabled, and multi-tenant — further enhance the fundamental advantages of Kentik’s cloud-based big data approach to network visibility.