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.
In an increasing trend since the implementation of the EU’s General Data Protection Regulation, several more companies have disclosed system data breaches that resulted in stolen information.
A new Rakhni variant was found with the ability to decide whether to install ransomware or cryptominers. It also has a worm component, installs spyware, and can disable Windows Defender.