Tag: Regularly | SpinSafe

We regularly warn of impending threats, says Sanjay Bahl, DG, CERT-In

January 9, 2022/in Computer Security

A Covid positive person could infect three or four persons, but a malware-infected cyber system can infect several times more. And “the global loss can be trillions of dollars,” warns India’s topmost cyber-warrior, Sanjay Bahl, who is director-general of the Indian Computer Emergency Response Team (CERT-In). Seated in his simple office in the Union ministry of electronics and information technology, Bahl said that last year alone India “battled more than 11 lakh cyberattacks, till October 15, 2021”.

With power, telecom, defence, finance, and health sectors facing ransomware attacks, CERT-In now trains users on defensive techniques, based on a new framework created by MITRE of the US and funded by the National Security Agency (NSA). As he was global chief security officer of Tata Consultancy Services and national security officer of Microsoft, Bahl knows well the threats faced by the private sector, too.

Excerpts from an exclusive interview:

Q/ How many incidents of cyber breaches were reported this year?

A/ In 2020, around 11.5 lakh incidents were tracked and reported. Last year, more than 11 lakh incidents were tracked and reported (as of October 15, 2021).

There are various kinds of threats ranging from state actors, cybercriminals and hackers, followed by threats from someone working inside financial institutions or other elements who went rogue.

Covid had a strike rate of three to four, when an infected person came in contact with others. An infected system will have a higher strike rate, due to the interconnected society that we live in.

CERT-In handles incident response, mitigation, and containment, and carries out drills and simulations. Training chief information security officers and network system administrators has been a major focus area for us. We have also been consistently sensitising users on the need to follow best practices. Keeping in mind the fast-changing cybersecurity threat landscape, we are constantly improvising.

Q/ How is CERT-In building capability in cyberthreat intelligence?

A/ The CERT-In threat intelligence exchange platform is based on Structured Threat Information Expression (STIX) and Trusted Automated Exchange of…

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Computer vision mobile app can regularly monitor diabetes, IT News, ET CIO

November 16, 2020/in Computer Security

London, University of Cambridge engineers has developed a computer vision technology into a free mobile phone app for regular monitoring of glucose levels in people with diabetes.

The app uses computer vision techniques to read and record the glucose levels, time and date displayed on a typical glucose test via the camera on a mobile phone.

The technology, which doesn’t require an internet or Bluetooth connection, works for any type of glucose meter, in any orientation and in a variety of light levels.

It also reduces waste by eliminating the need to replace high-quality non-Bluetooth meters, making it a cost-effective solution to the National Health Service (NHS).

Working with UK glucose testing company GlucoRx, the Cambridge researchers developed the technology into a free mobile phone app called GlucoRx Vision, which is now available on the Apple App Store and Google Play Store.

To use the app, users simply take a picture of their glucose meter and the results are automatically read and recorded, allowing much easier monitoring of blood glucose levels.

In addition to the glucose meters, many other types of digital meters are used in the medical and industrial sectors.

“These meters work perfectly well, so we don’t want them sent to landfill just because they don’t have wireless connectivity,” said Dr James Charles from Cambridge’s Department of Engineering.

“We wanted to find a way to retrofit them in an inexpensive and environmentally-friendly way using a mobile phone app”.

The computer vision technology behind the GlucoRx app is made up of two steps.

First, the screen of the glucose meter is detected.

The researchers used a single training image and augmented it with random backgrounds, particularly backgrounds with people.

This helps ensure the system is robust when the user’s face is reflected in the phone’s screen.

Second, a neural network called LeDigit detects each digit on the screen and reads it.

The network is trained with computer-generated synthetic data, avoiding the need for labour-intensive labelling of data which is commonly needed to train a neural network.

“Since the font on these meters is digital, it’s easy to train the neural network to recognise lots of…

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