A spate of high-profile thefts at automated teller machines (ATM) has sparked alarm and sent law enforcement officials in a tizzy.
But a British cybersecurity firm reckons swindlers can be stopped in their tracks with the help of machine learning and a bit of math.
ATMs have long been a target for criminals, although the style of attacks has evolved in recent years; from illegally tampering with the cash dispensing machines, many are now turning to more sophisticated means of gaining access, by infecting ATMs with malware.
Malware is a generic term for a variety of malicious software that can pose serious cybersecurity threats.
Earlier this year, a gang stole $13 million from ATMs in a three-hour, 14,000 withdrawal spree in Japan, while in Taiwan, hackers breached a major domestic bank in July and used malware to withdraw more than $2 million from dozens of ATMs, reported Reuters.