✳flâneur — a map of the web's best reading

Reverse Engineering the Analyst: Building Machine Learning Models for the SOC | Mandiant | Google Cloud Blog

cloud.google.com · 3,305 words · saved by 1 readers

Many cyber incidents can be traced back to an original alert that was either missed or ignored by the Security Operations Center (SOC) or Incident Response (IR) team. While most analysts and SOCs are vigilant and responsive, the fact is they are often overwhelmed with alerts. If a SOC is unable to review all the alerts it generates, then sooner or later, something important will slip through the cracks. The core issue here is scalability. It is far easier to create more alerts than to create more analysts, and the cyber security industry is far better at alert generation than resolution. More intel feeds, more tools, and more visibility all add to the flood of alerts. There are things that SOCs can and should do to manage this flood, such as increasing automation of forensic tasks (pulling PCAP and acquiring files, for example) and using aggregation filters to group alerts into similar batches. These are effective strategies and will help reduce the number of required actions a SOC ana

Threat Intelligence Reverse Engineering the Analyst: Building Machine Learning Models for the SOC June 5, 2018 Mandiant Written by: Matt Berninger, Awalin Sopan Many cyber incidents can be traced back to an original alert that was either missed or ignored by the Security Operations Center (SOC) or Incident Response (IR) team. While most analysts and SOCs are vigilant and responsive, the fact is they are often overwhelmed with alerts. If a SOC is unable to review all the alerts it generates, then sooner or later, something important will slip through the cracks. The core issue here is scalabili

Explore this link on the map →

related reading