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Researchers ‘Extract’ Data From Junked Tesla Cars

The electric car maker was fairly quick to fix vulnerabilities exposed by white hat hackers

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According to the report, data stored on Tesla models is not automatically erased when the car is hauled away from an accident site or sold at auction. Pixabay

In a shocking revelation, security researchers have extracted personal and unencrypted data — videos, phonebooks, calendar items — of Tesla users from crashed models sold at junkyards and auctions.

According to a CNBC report, a security researcher who goes by the name GreenTheOnly extracted data from the computers in salvaged Tesla Model S, Model X and two Model 3 vehicles.

“The computers on Tesla vehicles keep everything that drivers have voluntarily stored on their cars, plus tons of other information generated by the vehicles, including video, location and navigational data showing exactly what happened leading up to a crash,” the report claimed on Friday, citing researchers.

A Tesla spokesperson told CNBC the company offers options that customers can use to protect personal data stored on their car. “It includes a factory reset option for deleting personal data and restoring customised settings to factory defaults, and a Valet Mode for hiding personal data (among other functions) when giving their keys to a valet,” the spokesperson was quoted as saying.

“We are committed to finding and improving upon the right balance between technical vehicle needs and the privacy of customers,” the Tesla spokesperson said.

According to the report, data stored on Tesla models is not automatically erased when the car is hauled away from an accident site or sold at auction.

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Tesla recently had to give away one of their Model 3 cars and $35,000 prize money to a group of hackers after they cracked its system at a hacking event. PIxabay

GreenTheOnly and his fellow white-hat hacker “Theo” bought a wrecked Model 3 to evaluate the data that remains in the car’s computers after a crash. They extracted records that showed the car’s computers had stored data from at least 17 different devices.

“Mobile phones or tablets had paired to the car around 170 times. The Model 3 held 11 phonebooks’ worth of contact information from drivers or passengers who had paired their devices, and calendar entries with descriptions of planned appointments, and e-mail addresses of those invited,” the report said.

Tesla recently had to give away one of their Model 3 cars and $35,000 prize money to a group of hackers after they cracked its system at a hacking event.

Amat Cama and Richard Zhu of team Fluoroacetate exposed vulnerability in the vehicle system during the Pwn2Own 2019 hacking competition, organised by Trend Micro’s “Zero Day Initiative (ZDI)”, in Vancouver, Canada, this week.

Also Read- Apple Calls off its ‘AirPower’ Product: Report

As part of Tesla’s bug bounty programme, the company has paid hundreds of thousands of dollars in rewards to hackers who exposed vulnerabilities in its systems.

The electric car maker was fairly quick to fix vulnerabilities exposed by white hat hackers. (IANS)

Next Story

Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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FILE - A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

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Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

Also Read: Facebook Announces Some New Features to its Video Capabilities

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)