With today’s ever-evolving threat landscape, data breaches are no longer isolated cases. Responding to and remediating data breaches calls for a proactive approach — something that managed detection and response (MDR) can provide.
Security researchers uncovered a cryptojacking campaign that exploits a vulnerability in MikroTik routers to inject a malicious version of Coinhive. Here’s what you need to know.
Security researchers uncovered that a version of Jigsaw, an old ransomware, has resurfaced as a bitcoin stealer. Its operators have already netted 8.4 bitcoins (US$66,807 as of July 24, 2018) using the repurposed malware.
Threat data — enough of it — is critical to a machine learning system’s success in cybersecurity solutions. But is data quantity the be-all and end-all of effective machine learning?
The Federal Bureau of Investigation (FBI) issued a public service announcement (PSA) regarding the continued increase of Business Email Compromise (BEC) scams, which total global losses have already reached over US$12 billion in 2018.
Addressing the need for a more efficient way to defend against spam in the early 2000s, the antispam industry turned to machine learning. The effect: Overall cyberdefense was enhanced to catch approximately 95 percent of spam.
Threat intelligence is one of the key aspects of security used to help organizations make decisions on how to combat threats. Through managed detection and response, organizations can take advantage of the threat intelligence capabilities of security experts.