Catching malware outbreaks early keeps users, communities, enterprises, and governments safe. But if malware samples are scarce, can machine learning help analyze, detect, and end an outbreak?
As of 2018, global losses to BEC have exceeded US$12 billion. To keep abreast of the landscape that scammers are operating in, we look back on some of the noteworthy incidents and trends that made BEC a headline staple this year.
As people prepare for Thanksgiving meals and Black Friday deals, cybercriminals prep to take advantage of the holidays to spread malware — and not holiday cheer — to unsuspecting victims with a spam campaign and a Black Friday phishing scam.
Cybercriminals hack legitimate email accounts to enter the IT premises of an organization and carry out attacks ranging from fraud and spying to information and identity theft. Find out how AI and machine learning can be used to outsmart email hackers.
A Nigerian man was convicted after using phishing scams in an attempt to steal over $6 million dollars from employees of several targeted US colleges and universities.
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.