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2.5 mn IoT devices infected in Q4 2016: McAfee

2.5 mn IoT devices infected in Q4 2016: McAfeeNew Delhi, April 11 (IANS) Two and a half million Internet of Things (IoT) devices were infected by Mirai botnet by the end of fourth quarter last year, a new report by cyber security company McAfee said on Tuesday.

Mirai is a malware that turns networked devices running on obsolete versions of Linux into remotely controlled "bots" which can be used for a large-scale network attacks.

The 'McAfee Labs Threats Report April 2017' estimated that five IoT device IP addresses were infected by Mirai botnet each minute.

 

"The security industry faces critical challenges in our efforts to share threat intelligence between entities, among vendor solutions, and even within vendor portfolios," said Vincent Weafer, Vice President McAfee Labs, in a statement.

Overall count of new malware samples grew 24 per cent in 2016 to 638 million samples despite showing a drop of 17 per cent in fourth quarter.

McAfee also detected 176 new cyber-threats every minute, almost three every second in fourth quarter of 2016. Further, the total mobile malware grew 99 per cent in 2016.

"The number of total ransomware samples grew 88 per cent in 2016, despite 71 per cent drop in fourth quarter," the company said.

The findings further showed that the number of new Mac OS malware samples grew 245 per cent in fourth quarter due to adware bundling, though still small compared to Windows threats. Also, overall Mac OS malware grew 744 per cent in 2016.

(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)

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2.5 mn IoT devices infected in Q4 2016: McAfee

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Doraiah Chowdary Vundavally is a Software engineer at VTech . He is the news editor of SocialNews.XYZ and Freelance writer-contributes Telugu and English Columns on Films, Politics, and Gossips. He is the primary contributor for South Cinema Section of SocialNews.XYZ. His mission is to help to develop SocialNews.XYZ into a News website that has no bias or judgement towards any.