Monthly Archives: July 2016

A hunting ground for cybercriminals

download-2The interconnectivity of social media means it is a perfect hunting ground for illegal activity, and increasingly people are realising that their “friend” many not actually be their friend.

Cybercrime on social networks can be broken down into three categories:

  • the traditional broad-sweep scams, trying to lure you to click on something or visit pages that will push malware on to your computer
  • searching for careless public exposure of personal data
  • using social media as a platform to connect, exchange ideas and trade stolen information

Malware, scams and ransomware

The first category is the most widespread.

“The problem with social media is that people have an inherent trust,” explains Mark James, security specialist with IT security firm ESET. “And that is what is being tapped into by those cybercriminals.”

“People still believe that you have to click on something and download a file to be infected,” he says.

“This really isn’t the case anymore. There are things like drive-by-downloads, infected adverts and things like that. It’s very easy to be compromised on your machines.”

In many cases the initial malware is just a gateway into the system. It doesn’t do any real harm, yet. But once a back door is established to the infected computer, that access may then be put up for sale.

A package of data offering, of access to thousands of infected computers, will be snapped up by another criminal for use in a variety of ways.

With access to the computers received, criminals may then install software which, say, hijacks the victim’s online banking, or reads usernames and passwords.

One of the most profitable scams is installing ransomware, malicious software that encrypts the data on a victim’s computer and then asks for payment before restoring the system to its original state.

Reconnaissance

Social media is also an ideal hunting ground for anyone who has a clear target to attack, be it an individual or a company.

If you want to see who works in which company and in which position, or who they are friends with professionally and privately, this information can often be easily picked up on social media.

Any attack on a specific individual will be much easier if the target has made a lot of private information publicly available on their profiles.

If the target is a corporation, it is easy to single out an individual or a group of employees, and then target their machines in a focused attack. And once one machine in a network is affected, getting access to the entire structure is not difficult.

“There’s such a big crossover between your personal social media accounts and the impact you can cause within a corporate environment,” warns Michael Sentonas, vice president of technology strategy at cybersecurity firm Crowdstrike.

“Most organisations allow their users to connect to Facebook, to Instagram, to Twitter and other platforms and that’s where an attack – even if it was targeted at a home user – can have a significant impact on the workplace.”

Putting up defences

“Our only effective protection is a multilayered approach,” Mr James of ESET explains. “There’s no single protection anymore, there’s no magic bullet or single piece of software that’s going to protect us.”

While security software is important, it’s only a first step. It is a cat and mouse game where the bad guys produce the malware and the good guys try to produce the means to stop it.

Chose to read this article

But in truth, you are probably manipulated into doing so by publishers using clever machine learning algorithms.

The online battle for eyeballs has gone hi-tech.

Every day the web carries about 500 million tweets, 430,000 hours of YouTube video uploads, and more than 80 million new Instagram photos. Just keeping up with our friends’ Facebook and Twitter updates can seem like a full-time job.

So publishers desperately trying to get us to read and watch their stuff in the face of competition from viral videos and pictures of cats that look like Hitler are enlisting the help of data analytics and artificial intelligence (AI).

But do these technologies actually work?

A question of timing

Recent start-up Echobox has developed a system it says takes the human guesswork out of the mix. By analysing large amounts of data, it learns how specific audiences respond to different articles at different times of the day.

It then selects the best stories to post and the best times to post them.

Echobox claims its system generates an average 71% gain in referral traffic from Facebook and a 142% increase from Twitter. The software is already being used by publishers such as Vogue, Le Figaro and Telegraph Media Group.

“Imagine a superhuman editor with an incredibly deep understanding of its audience, but 100 times faster,” says Antoine Amann, Echobox founder and chief executive.

“The data we use is both historical and real-time. For instance, our system will have a strong understanding of what type of [publishing] times worked well in the past, whilst at the same time analysing what’s currently trending on the web.”

Anne Pican, digital publisher at French daily newspaper Le Figaro, one of the firm’s clients, says they have already seen benefits.

“Social media optimisation has been a major headache,” she says. “Not only is it extremely complex but it’s a lot of guesswork and requires a more scientific approach.

“Since using Echobox we’ve seen a major upswing in our traffic and saved valuable time.”

Blossoming

Traditional newspapers facing dwindling print circulations are particularly keen to attract new digital audiences.

The New York Times (NYT), for example, has built Blossom, an intelligent “bot” constructed inside the messaging app Slack.

It uses machine learning to predict how blog posts and articles will perform on social media. It can also tell editors which ones to promote.

If a journalist sends Blossom a direct message, such as “Blossom Facebook?”, the bot will respond with a list of links to stories it believes will do well on the social media platform at that time.

According to its developers, Blossom posts get about 380% more clicks than ones it doesn’t recommend.

Which headline?

What this type of historical and real-time analysis shows is that certain headlines, photos and topics attract more attention than others on different devices at different times of the day with different audiences.

Predicting this without the help of machine learning computers is very tricky.

Programs such as Chartbeat and Echobox also give publishers the ability to test different headlines and promotional tweets for the same story in real time.

And programs like SocialFlow – used by some sections of the BBC website – apply algorithms to try to anticipate when the social media audience will be most receptive to an update.

It can then automatically post the message at the “optimum” time, measure how many people look at the post, and crucially, how many bother to click through to the original article.

Marketing hype?

But does using data analytics to learn about reader and viewer behaviour, then make publishing decisions based on that analysis, really count as AI?

The NYT is staying tight-lipped about the exact workings of the bot, citing intellectual property reasons, but Colin Russel, a senior data scientist at the newspaper and Blossom’s main designer, says: “We do characterise it as AI.

“We’re emulating what a team of editors would do if they had the time enough and a whiteboard big enough to observe and enumerate all the stories, all their history of posting, and all possible places they could be posted.

“It’s definitely an artificial intelligence.”

Echobox also describes its service as “artificial intelligence meets online publishing”.

But Tom Cheesewright, a futurist and head of consultancy firm Book of the Future, describes such tech as “more of a tool than an intelligence”.

“I’d argue this is probably the very outer edges of what might be called AI. Here, a more prosaic term like machine learning or predictive analytics might be more appropriate.”

Semantics aside, Richard Reeves, managing director at the Association of Online Publishers, believes this kind of tech could have a positive impact on the industry.

“Publishers are faced with the dual challenge of increased competition for user attention and a diminishing pool of resources.

Athletes are using tech to win medals

Likewise, when German sailor Philipp Buhl takes to the water, he will be able to predict accurately how the current will affect his boat as he whips along Rio’s Guanabara Bay.

This is because technology – and data analytics in particular – has made great strides since London 2012.

“Real-time data analytics may not seem like a big leap from an innovation point of view, but it has the potential to enable yet more records to be broken in 2016,” says Dr Helen Meese, head of healthcare at the Institution of Mechanical Engineers.

Data collection and analysis is having an impact on almost every sport.

Fighting fit

For example, Team GB’s boxers have benefited from this type of analysis, using “iBoxer” software developed in conjunction with Sheffield Hallam University.

The performance analysis system makes use of a wealth of data on Team GB’s boxers and their opponents, including detailed fight analysis that reveals threats and opportunities for the fighters, helping them refine their tactics.

And the Australian Institute of Sport (AIS) has co-developed a database in which Australia’s National Sporting Organisations closely monitor approximately 2,000 athletes each week.

“In athlete groups where there is really high engagement with data entry, we have been able to provide coaches with advice on training loads that have seen a reduction in injury and illness,” says Nick Brown, deputy director, performance science and innovation, at the AIS.

“The tech part of this solution is the database, smart data analytics, and in some cases, the use of wearable sensors to bring in training data.”

Two wheels good

Professor Steve Haake, director of Sheffield Hallam’s Advanced Wellbeing Research Centre, has been working with UK Sport since 2000.

He says his team’s work has moved increasingly towards this kind of data-driven performance analysis.

For example, in cycling there is less need to focus on the mechanical aspects, he says, because “bikes are optimised now – they have bearings and gear sets which are 99% efficient”.

Instead, “most of the work we have done in the last two Olympiads has been interrogating how well these things actually work when you get out there in the field.

“It’s about data acquisition and complex databases that draw information from lots of places,” Prof Haake says.

Team USA track cyclists have even been interrogating live data during training using augmented reality glasses developed by Solos.

Data collected from bike sensors, such as power, speed and pedal revolutions, are beamed wirelessly to the cyclist’s glasses via IBM’s cloud platform. As the athletes pedal furiously they can view their key stats without taking their eyes off the track.

“With the ability for the athlete to receive real-time feedback via the Solos smart glasses, they can now adjust on the fly,” says Ernesto Martinez, a director at Solos.

“For example, if Sarah [Hammer] or Kelly [Catlin] need to meet a specific lap time or power metric during an exchange or portion of the race, they will be able to see whether they are hitting the mark or not and adjust accordingly.

“This real-time feedback and adjustment capability will enable faster riding times.”

But the cyclists will not be able to wear the glasses during the Games themselves.

Team effort

Sports the world over are looking to other industries for inspiration, not least the technology and engineering sectors.

For example, Sailing Team Germany (STG) partnered with business software firm SAP in 2011 in an effort to arrest the nation’s decline in the sport after Sydney 2000.

“The goal is to develop technology that helps the sailors train better, perform better and learn quicker,” says Marcus Baur, STG’s head of technology.

A key component of this is building virtual models based on real data, enabling tricky, ever-changing conditions such as currents and wind to be analysed.