World security leaders rely on AI, machine learning to defend against threats

Source: Xinhua| 2018-02-22 07:42:10|Editor: pengying
Video PlayerClose

SAN FRANCISCO, Feb. 21 (Xinhua) -- Security professionals worldwide are increasingly turning to artificial intelligence (AI) and machine learning to defend against malware threats, U.S. tech leader Cisco said Wednesday in its security report.

Findings of the "Cisco 2018 Annual Cybersecurity Report" show 39 percent of organizations are reliant on automation, 34 percent on machine learning and 32 percent highly reliant on AI.

It said encryption has been used as a tool in malware production to evade detection and conceal command-and-control activity.

"While encryption is meant to enhance security, the expanded volume of encrypted web traffic (50 percent as of October 2017) -- both legitimate and malicious -- has created more challenges for defenders trying to identify and monitor potential threats," the report said.

Over time, machine learning can "learn" how to automatically detect unusual patterns in encrypted web traffic, cloud, and IoT environments, it added.

The report noted that about 3,600 chief information security officers surveyed admitted that they were eager to add tools like machine learning and AI to fight malware threats.

"Last year's evolution of malware demonstrates that our adversaries continue to learn," said John N. Stewart, senior vice president and chief security and trust Officer of Cisco.

More than 500,000 U.S. dollars have lost in damage as a result of over half of all those attacks, the report said.

While attackers are taking advantage of the lack of advanced security, the report said, 57 percent of security professionals surveyed stated that they are increasingly using cloud to protect data security.

The Cisco 2018 Annual Cybersecurity Report, now in its 11th year, highlights findings from cybersecurity trends observed over the past 12-18 months from its threat research and technology partners.

TOP STORIES
EDITOR’S CHOICE
MOST VIEWED
EXPLORE XINHUANET
010020070750000000000000011100001369900211