Internet security: Breaching-point

Software developers and computer-makers do not necessarily suffer when their products go wrong

IT HAS been a cracking year for hacking. Barack Obama and the CIA accused Russia of electronic meddling in an attempt to help Donald Trump win the presidency. Details emerged of two enormous data breaches at Yahoo, one of the world’s biggest internet companies; one, in 2013, affected more than a billion people. Other highlights include the hack of the World Anti-Doping Agency; the theft of $81m from the central bank of Bangladesh (only a typo prevented the hackers from making off with much more); and the release of personal details of around 20,000 employees of the FBI. The more closely you look at the darker corners of the internet, the more the phrase “computer security” looks like a contradiction in terms.

Why, two decades after the internet began to move out of universities and into people’s homes, are things still so bad? History is one reason: the internet started life as a network for the convenient sharing of academic data. Security was an afterthought. Economics matters, too. Software developers and computer-makers do not necessarily suffer when their products go wrong or are subverted. That weakens the incentives to get security right.

Unfortunately, things are likely to get worse before they get better. The next phase of the computing revolution is the “internet of things” (IoT), in which all manner of everyday objects, from light bulbs to cars, incorporate computers connected permanently to the internet. Most of these gizmos are as insecure as any other computer, if not more so. And many of those making IoT products are not computer firms. IT companies have accumulated decades of hard-won wisdom about cyber-security; toaster-makers have rather more to learn.

In November cyber-security researchers revealed a malicious program that could take control of any smart light bulbs within 400 metres. A hacked light bulb does not sound too dangerous. But such unobtrusive computers can be recruited into remotely controlled “botnets” that can be used to flood websites with bogus traffic, knocking them offline. Routers, the small electronic boxes that connect most households to the internet, are already a popular target of bot-herders. Other targets are more worrying. At a computer-security conference in 2015, researchers demonstrated how wirelessly to hack a car made by Jeep, spinning its steering wheel or slamming on its brakes. As the era of self-driving cars approaches, the time to fix such problems is now.

One option is to leave the market to work its magic. Given the damage that cybercrime can do to companies, they have good commercial reasons to take it seriously. If firms are careless about security, they risk tarnished reputations and lost customers. A planned buy-out of Yahoo by Verizon, an American telecoms firm, may be rethought after its hacks. But these incentives are blunted when consumers cannot make informed choices. Most customers (and often, it seems, executives) are in no position to evaluate firms’ cyber-security standards. What is more, the epidemic of cybercrime is best tackled by sharing information. A successful cyber-attack on one company can be used against another. Yet it is tempting for firms to keep quiet about security breaches.

That suggests a role for government. Researchers draw an analogy with public health, where one person’s negligence can harm everyone else—which is why governments regulate everything from food hygiene to waste disposal. Some places are planning minimum computer-security standards, and will fine firms that fail to comply. The IoT has also revived the debate about ending the software industry’s long-standing exemption from legal liability for defects in its products.

Neither relax nor chill

The problem is that regulation is often fragmented. America has a proliferation of state-level rules, for example, when a single, federal regime would be better. Regulation can also go too far. From January financial institutions in New York must comply with a new cyber-security law that many think sets the bar for breach notifications too low. Changing the liability regime for software could chill innovation by discouraging coders from trying anything new.

Rule-makers can, however, set reasonable minimum expectations. Many IoT devices cannot have their software updated, which means that security flaws can never be fixed. Products should not be able to operate with factory usernames and passwords. No software program can be made impregnable, but liability regimes can reflect firms’ efforts to rectify flaws once they become apparent. Firms need to be encouraged to take internet security more seriously. But overly detailed prescriptions will just hack everyone off.

7 Critical Components of Effective Laptop Security

There’s not one single thing you can do to protect your laptop’s security; there’s seven.

#1. Employer-owned laptops. Review your company’s policy over how data is managed on this device. This includes what information you can put on the computer and any minimal security requirements.

#2. Stickers and markings really do deter theft. Just like the decals on a house’s windows about its alarm system can repel burglars, some kind of visible marking on the laptop and its case will be effective in deterring theft.

The markings should indicate the security that protects the laptop. This will likely make thieves think twice and move on to unmarked devices, which practically beckon to thieves, “Come take me!”

#3. Get down all the information about the device. And this should be done before it is stolen or lost. Record on hardcopy the following information: serial number, model number, purchase date, description, market value and any other features that would be important for a police report and insurance claim. Then store this hardcopy record in a secure place.

Do not underestimate the importance of this one-time-only chore: If your laptop is stolen and finds its way into the trunk of the thief’s car, along with all sorts of other stolen goodies that look suspicious to the cop who pulled him over for speeding, the cop can’t do anything if the serial number of your laptop isn’t in the system as one that’s been stolen.

The thief gets his speeding ticket and is free to go—with your stolen computer.

#4. Install secret tracking software. So if someone steals your laptop, they won’t know that software is silently running in the background, secretly transmitting the laptop’s location to a tracking service. It can even communicate the computer’s street address. Some covert tracking software can also delete sensitive data on the device’s hard drive while it is still in the crook’s possession. #5. Install software that covertly destroys and recovers data. So if your laptop is stolen, you can remotely eradicate sensitive data as well as recover it if you never had it backed up (which you should have).

In fact, some companies have suffered an irreversible loss of data because they had to remotely destroy it—even though it had never been backed up. This is why remote recovery, not just destruction, is so important to have.

#6. Use additional offline digital security practices. Okay, so a thief absconds with your laptop. It will be useless to him if he can’t access the information that’s stored in it. For example, if it requires your thumbprint to access the data on it, the thief will not be able to get ahold of your private information. And yes, some laptop makers do offer this biometric feature. Another offline tool of protection is a security lock.

#7. Another is a hard drive encryption. However, the thing about a hard drive encryption is that it can hinder the laptop’s owner. And while this particular tool can be very effective at halting a thief, it does not have the capability to identify that the device has been located and taken over.

So you’ll be wondering if the thief was able to crack through the encryption. Though encryption technology today pretty much guarantees that a criminal won’t be able to crack it, it is still best to employ other offline protection methods such as biometrics.

Computer Learns to Recognize Sounds by Watching Video

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.

But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That's because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.

Sound recognition may be catching up, however, thanks to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). At the Neural Information Processing Systems conference next week, they will present a sound-recognition system that outperforms its predecessors but didn’t require hand-annotated data during training.

Instead, the researchers trained the system on video. First, existing computer vision systems that recognize scenes and objects categorized the images in the video. The new system then found correlations between those visual categories and natural sounds.

"Computer vision has gotten so good that we can transfer it to other domains," says Carl Vondrick, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. "We're capitalizing on the natural synchronization between vision and sound. We scale up with tons of unlabeled video to learn to understand sound."

The researchers tested their system on two standard databases of annotated sound recordings, and it was between 13 and 15 percent more accurate than the best-performing previous system. On a data set with 10 different sound categories, it could categorize sounds with 92 percent accuracy, and on a data set with 50 categories it performed with 74 percent accuracy. On those same data sets, humans are 96 percent and 81 percent accurate, respectively.

"Even humans are ambiguous," says Yusuf Aytar, the paper's other first author and a postdoc in the lab of MIT professor of electrical engineering and computer science Antonio Torralba. Torralba is the final co-author on the paper.

"We did an experiment with Carl," Aytar says. "Carl was looking at the computer monitor, and I couldn’t see it. He would play a recording and I would try to guess what it was. It turns out this is really, really hard. I could tell indoor from outdoor, basic guesses, but when it comes to the details — ‘Is it a restaurant?’ — those details are missing. Even for annotation purposes, the task is really hard."

Complementary modalities

Because it takes far less power to collect and process audio data than it does to collect and process visual data, the researchers envision that a sound-recognition system could be used to improve the context sensitivity of mobile devices.

When coupled with GPS data, for instance, a sound-recognition system could determine that a cellphone user is in a movie theater and that the movie has started, and the phone could automatically route calls to a prerecorded outgoing message. Similarly, sound recognition could improve the situational awareness of autonomous robots.

"For instance, think of a self-driving car," Aytar says. "There's an ambulance coming, and the car doesn’t see it. If it hears it, it can make future predictions for the ambulance — which path it's going to take — just purely based on sound."

Visual language

The researchers’ machine-learning system is a neural network, so called because its architecture loosely resembles that of the human brain. A neural net consists of processing nodes that, like individual neurons, can perform only rudimentary computations but are densely interconnected. Information — say, the pixel values of a digital image — is fed to the bottom layer of nodes, which processes it and feeds it to the next layer, which processes it and feeds it to the next layer, and so on. The training process continually modifies the settings of the individual nodes, until the output of the final layer reliably performs some classification of the data — say, identifying the objects in the image.

Vondrick, Aytar, and Torralba first trained a neural net on two large, annotated sets of images: one, the ImageNet data set, contains labeled examples of images of 1,000 different objects; the other, the Places data set created by Torralba’s group, contains labeled images of 401 different scene types, such as a playground, bedroom, or conference room.

Once the network was trained, the researchers fed it the video from 26 terabytes of video data downloaded from the photo-sharing site Flickr. “It’s about 2 million unique videos,” Vondrick says. "If you were to watch all of them back to back, it would take you about two years." Then they trained a second neural network on the audio from the same videos. The second network’s goal was to correctly predict the object and scene tags produced by the first network.

The result was a network that could interpret natural sounds in terms of image categories. For instance, it might determine that the sound of birdsong tends to be associated with forest scenes and pictures of trees, birds, birdhouses, and bird feeders.

Benchmarking

To compare the sound-recognition network's performance to that of its predecessors, however, the researchers needed a way to translate its language of images into the familiar language of sound names. So they trained a simple machine-learning system to associate the outputs of the sound-recognition network with a set of standard sound labels.

For that, the researchers did use a database of annotated audio — one with 50 categories of sound and about 2,000 examples. Those annotations had been supplied by humans. But it’s much easier to label 2,000 examples than to label 2 million. And the MIT researchers’ network, trained first on unlabeled video, significantly outperformed all previous networks trained solely on the 2,000 labeled examples.

"With the modern machine-learning approaches, like deep learning, you have many, many trainable parameters in many layers in your neural-network system,” says Mark Plumbley, a professor of signal processing at the University of Surrey. “That normally means that you have to have many, many examples to train that on. And we have seen that sometimes there’s not enough data to be able to use a deep-learning system without some other help. Here the advantage is that they are using large amounts of other video information to train the network and then doing an additional step where they specialize the network for this particular task. That approach is very promising because it leverages this existing information from another field."

Plumbley says that both he and colleagues at other institutions have been involved in efforts to commercialize sound recognition software for applications such as home security, where it might, for instance, respond to the sound of breaking glass. Other uses might include eldercare, to identify potentially alarming deviations from ordinary sound patterns, or to control sound pollution in urban areas. "I really think that there’s a lot of potential in the sound-recognition area," he says.

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