Cybercrime groups like Mealybug, Gallmaker and Necurs are opting for off-the-shelf tools and operating system features such as PowerShell to attack targets.
Internet of Things threats on the rise Cybercriminals attack IoT devices an average of 5, times per month. The most common targets for new account fraud are mortgages, student loans, car loans and credit cards. Both individuals and enterprises are at risk for account takeovers. Increased effort to solve the year problem Similar to the Y2K problem, the problem is a bug that will affect the way computers store time-stamps.
Computer logic defines time-stamps with the current date and time, minus the number of seconds that have passed since January 1, , when computers originated. In , the number of elapsed seconds will exceed the information that can be stored in a four-byte data type, meaning most computers will need an extra byte to preserve their timing systems.
Most identity thefts are crimes of opportunity. Everyone with a Social Security number is at risk for identity theft, but two demographics are targeted aggressively and often: the very young and the very old.
While deployed, active duty members of the armed services are particularly vulnerable to identity theft because they may not notice mistakes on their credit reports or receive calls from debt collectors regarding a fraudulent charge.
According to FTC reports, military consumers are most affected by credit card and bank fraud. With this information, an identity thief can target victims for phishing and imposter scams. People who have previously been affected by identity theft are at a greater risk for future identity theft and fraud. For more information about how victims of identity theft can protect themselves from future fraud, read about the identity theft recovery process.
Let's call these field intensities x and y. Shine those two beams into the beam splitter, which will combine these two beams.
In addition to the beam splitter, this analog multiplier requires two simple electronic components—photodetectors—to measure the two output beams.
They don't measure the electric field intensity of those beams, though. They measure the power of a beam, which is proportional to the square of its electric-field intensity. Why is that relation important? To understand that requires some algebra—but nothing beyond what you learned in high school. Subtracting the latter from the former gives 2 xy. Pause now to contemplate the significance of this simple bit of math.
It means that if you encode a number as a beam of light of a certain intensity and another number as a beam of another intensity, send them through such a beam splitter, measure the two outputs with photodetectors, and negate one of the resulting electrical signals before summing them together, you will have a signal proportional to the product of your two numbers. Simulations of the integrated Mach-Zehnder interferometer found in Lightmatter's neural-network accelerator show three different conditions whereby light traveling in the two branches of the interferometer undergoes different relative phase shifts 0 degrees in a, 45 degrees in b, and 90 degrees in c.
My description has made it sound as though each of these light beams must be held steady. In fact, you can briefly pulse the light in the two input beams and measure the output pulse. Better yet, you can feed the output signal into a capacitor, which will then accumulate charge for as long as the pulse lasts. Then you can pulse the inputs again for the same duration, this time encoding two new numbers to be multiplied together.
Their product adds some more charge to the capacitor. You can repeat this process as many times as you like, each time carrying out another multiply-and-accumulate operation. Using pulsed light in this way allows you to perform many such operations in rapid-fire sequence. The most energy-intensive part of all this is reading the voltage on that capacitor, which requires an analog-to-digital converter.
But you don't have to do that after each pulse—you can wait until the end of a sequence of, say, N pulses. That means that the device can perform N multiply-and-accumulate operations using the same amount of energy to read the answer whether N is small or large. Here, N corresponds to the number of neurons per layer in your neural network, which can easily number in the thousands.
So this strategy uses very little energy. Sometimes you can save energy on the input side of things, too. That's because the same value is often used as an input to multiple neurons.
Rather than that number being converted into light multiple times—consuming energy each time—it can be transformed just once, and the light beam that is created can be split into many channels. In this way, the energy cost of input conversion is amortized over many operations.
Splitting one beam into many channels requires nothing more complicated than a lens, but lenses can be tricky to put onto a chip. So the device we are developing to perform neural-network calculations optically may well end up being a hybrid that combines highly integrated photonic chips with separate optical elements.
I've outlined here the strategy my colleagues and I have been pursuing, but there are other ways to skin an optical cat. Another promising scheme is based on something called a Mach-Zehnder interferometer, which combines two beam splitters and two fully reflecting mirrors. It, too, can be used to carry out matrix multiplication optically.
Two MIT-based startups, Lightmatter and Lightelligence , are developing optical neural-network accelerators based on this approach. Lightmatter has already built a prototype that uses an optical chip it has fabricated. And the company expects to begin selling an optical accelerator board that uses that chip later this year. Another startup using optics for computing is Optalysis , which hopes to revive a rather old concept. One of the first uses of optical computing back in the s was for the processing of synthetic-aperture radar data.
A key part of the challenge was to apply to the measured data a mathematical operation called the Fourier transform. Digital computers of the time struggled with such things. Even now, applying the Fourier transform to large amounts of data can be computationally intensive. But a Fourier transform can be carried out optically with nothing more complicated than a lens, which for some years was how engineers processed synthetic-aperture data.
Optalysis hopes to bring this approach up to date and apply it more widely. There is also a company called Luminous , spun out of Princeton University , which is working to create spiking neural networks based on something it calls a laser neuron.
Spiking neural networks more closely mimic how biological neural networks work and, like our own brains, are able to compute using very little energy. Luminous's hardware is still in the early phase of development, but the promise of combining two energy-saving approaches—spiking and optics—is quite exciting. There are, of course, still many technical challenges to be overcome. One is to improve the accuracy and dynamic range of the analog optical calculations, which are nowhere near as good as what can be achieved with digital electronics.
That's because these optical processors suffer from various sources of noise and because the digital-to-analog and analog-to-digital converters used to get the data in and out are of limited accuracy. Indeed, it's difficult to imagine an optical neural network operating with more than 8 to 10 bits of precision. While 8-bit electronic deep-learning hardware exists the Google TPU is a good example , this industry demands higher precision, especially for neural-network training.
There is also the difficulty integrating optical components onto a chip. Because those components are tens of micrometers in size, they can't be packed nearly as tightly as transistors, so the required chip area adds up quickly. A demonstration of this approach by MIT researchers involved a chip that was 1. Even the biggest chips are no larger than several square centimeters, which places limits on the sizes of matrices that can be processed in parallel this way.
There are many additional questions on the computer-architecture side that photonics researchers tend to sweep under the rug. What's clear though is that, at least theoretically, photonics has the potential to accelerate deep learning by several orders of magnitude. Based on the technology that's currently available for the various components optical modulators, detectors, amplifiers, analog-to-digital converters , it's reasonable to think that the energy efficiency of neural-network calculations could be made 1, times better than today's electronic processors.
Making more aggressive assumptions about emerging optical technology, that factor might be as large as a million. And because electronic processors are power-limited, these improvements in energy efficiency will likely translate into corresponding improvements in speed.
Many of the concepts in analog optical computing are decades old. Some even predate silicon computers. Schemes for optical matrix multiplication, and even for optical neural networks , were first demonstrated in the s.
But this approach didn't catch on. Americans are most likely to have their identities stolen. Over 1 million children become victims of identity crimes each year.
The total loss for identity theft in the U. Getting a Feel for Identity Fraud Primarily concerning online activity, identity theft statistics for paint an unsurprisingly bleak picture.
Worse still: This somewhat untested landscape can make it tricky even for experts to get their heads around. Around 1 in 15 people experience some form of identity theft. In , there were 3,, total reports for identity fraud in the U. To make matters worse: 3. Credit card fraud accounted for What you might find a little more disconcerting is the fact that the list then continues with: Tax identity theft - In fact: 5.
There are an average of , incidents of identity fraud yearly in the UK. In fact, over , victims fell foul there, too. Moreover, eight out of ten identity fraud cases occur online.
On a wider scale: 6. According to the most recent identity theft statistics, there were 41, data breaches across Europe between mid and early But did it work?
Not entirely. That could still leave you with many questions, most notably: What is the true cost of identity theft? This is a rise of 6. This is huge! A fantastic example of this is the fact that: 9. In the U. Over one million children fall foul to identity theft every year According to the latest cybercrime statistics from , an average of 1 million children fall foul to identity theft every year. Identity theft affects The simple fact is: Anyone shopping online is at a significant risk of ID theft issues, according to the latest identity theft stats.
Bottom line: Use these identity theft statistics the right way, and they might just keep the whole family safe from fraud for years to come! Jenifer Kuadli. Table of contents.
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