This made the malware relatively easy to spot and block.
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What criminals liked about mobiles, said Mr Mahaffey, was their intrinsic connection to a payment plan. This made it far easier to siphon off cash than with PC viruses. Malicious apps made it hard for people to realise they were being scammed, he added, because they could work surreptitiously while phone owners used a different application. Alongside the growth in mobile malware is a rise in junk or spam text messages being sent to phones - many involving fake offers in an attempt to sucker the recipient into revealing their credit card number.
In Europe, about 2. This is dwarfed by the billions of junk mail messages sent every day via email but scammers like mobile spam because junk sent to a phone is more likely to be opened, he said. Worse, from the spammers' point of view, it can take up to 24 hours for those messages to be seen.
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Ciaran Bradley, head of handset security at Adaptive Mobile, said the amount of spam mobile owners received varied widely depending on where they lived. In some countries, such as India, it was not uncommon to get up to 40 junk text messages a day. In other places such as the UK, he said, getting one or two junk mail messages was seen by most people as too many. As well as sending more spam in different countries, scammers were also tuning their campaigns to the different devices in those nations.
For instance, said Mr Bradley, in Africa many scams were centred around mobile banking and credit transfers to capitalise on the greater use of those technologies in that region.
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In places where smartphones were more predominant, fake or booby-trapped apps were getting increasingly common. Google's Android operating system for phones was proving particularly popular, he said, because it was relatively easy to take applications apart, add in some malware, re-compile them and then put them on an unofficial marketplace in a bid to snare victims. There was a particular problem in China, said Mr Bradley, as there was no official market place to acquire apps for Android phones.
As a result, he said, many people were visiting rogue marketplaces and finding fake or booby-trapped apps. Google was starting to do a better job of policing Android apps, said Mr Bradley, and had cracked down on those programs that produced adverts that looked like system messages in a bid to trick people into clicking on them. The first flights bringing holidaymakers home to the UK have landed amid some reports of disruption.
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Media playback is unsupported on your device. More on this story. In other words, as ARM seeks to put cellphone chips into our supercomputers , Intel is doing the reverse. The lines between the mobile hardware and data-center hardware are blurring. That may seem odd at first, but if you step back and look at the bigger picture, it only makes sense. Big-time data-center operations want the ulta-low-power profiles of the hardware in our cellphones, and the mobile world is hungry for the computational punch you get from much larger systems. Intel Labs technology evangelist Sean Koehl says that its core creation, first discussed in , acts as a "network" of processors on a single chip, with two cores per node.
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The nodes actually communicate to each other much the same way nodes in a cluster in a data center would. Intel Labs has been working on many-core chips since around , and the more immediate applications will probably be in servers and, yes, supercomputers, which are essentially a bunch of servers working in tandem. This is often called high-performance computing, or HPC. Whether you're dealing with a high-end supercomputer, a cluster of commodity servers running Hadoop, or a cluster built out of Legos and ultra-cheap Raspberry Pi computers , HPC depends on parallel processing — breaking down big problems into smaller problems that are solved by different processors running in parallel.
What Intel Labs is now researching is whether this approach will make sense for mobile computing. Although today's most serious big data applications run on a server and deliver information to a client, Koehl points out that there are actually many cases where a hybrid model would make the most sense.
Machine vision applications, for example. In an augmented-reality application — such as Google Goggles — you may want to overlay some information on top of video captured by the phone.
You might want to identify the faces that the camera is pointing at, or the name of the business housed in a particular building. Some of this processing is best done on a server somewhere, but some is more suited to the client — i. Such tasks might include determining where the faces or buildings are in a particular frame. It may then be best to let a server determine particular information — whose face, or which building — but the client needs to do a fair amount of work.
Other applications could include rendering 3-D graphics for games. Koehl says that even on mobile parallel applications may eventually outnumber traditional "serial" applications. One challenge for developers is that they will need to start thinking about parallelism when designing applications. As part of his job as an evangelist, Koehl is promoting parallelism education through outreach programs for educational programs at various levels, including high school. But Intel is also working on tools to make it easier for developers to work with parallelism, including some that abstract the entire problem away.