Researchers including MIT professor Daniela Rus (left) and research scientist Stephanie Gil (right) have developed a technique for preventing malicious hackers from commandeering robot teams’ communication networks. To verify the theoretical predictions, the researchers implemented their system using a battery of distributed Wi-Fi transmitters and an autonomous helicopter. Image: M. Scott Brauer.

Distributed planning, communication, and control algorithms for autonomous robots make up a major area of research in computer science. But in the literature on multirobot systems, security has gotten relatively short shrift.

In the latest issue of the journal Autonomous Robots, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and their colleagues present a new technique for preventing malicious hackers from commandeering robot teams’ communication networks. The technique could provide an added layer of security in systems that encrypt communications, or an alternative in circumstances in which encryption is impractical.

“The robotics community has focused on making multirobot systems autonomous and increasingly more capable by developing the science of autonomy. In some sense we have not done enough about systems-level issues like cybersecurity and privacy,” says Daniela Rus, an Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT and senior author on the new paper.

“But when we deploy multirobot systems in real applications, we expose them to all the issues that current computer systems are exposed to,” she adds. “If you take over a computer system, you can make it release private data — and you can do a lot of other bad things. A cybersecurity attack on a robot has all the perils of attacks on computer systems, plus the robot could be controlled to take potentially damaging action in the physical world. So in some sense there is even more urgency that we think about this problem.”

Identity theft

Most planning algorithms in multirobot systems rely on some kind of voting procedure to determine a course of action. Each robot makes a recommendation based on its own limited, local observations, and the recommendations are aggregated to yield a final decision.

A natural way for a hacker to infiltrate a multirobot system would be to impersonate a large number of robots on the network and cast enough spurious votes to tip the collective decision, a technique called “spoofing.” The researchers’ new system analyzes the distinctive ways in which robots’ wireless transmissions interact with the environment, to assign each of them its own radio “fingerprint.” If the system identifies multiple votes as coming from the same transmitter, it can discount them as probably fraudulent.

“There are two ways to think of it,” says Stephanie Gil, a research scientist in Rus’ Distributed Robotics Lab and a co-author on the new paper. “In some cases cryptography is too difficult to implement in a decentralized form. Perhaps you just don’t have that central key authority that you can secure, and you have agents continually entering or exiting the network, so that a key-passing scheme becomes much more challenging to implement. In that case, we can still provide protection.

“And in case you can implement a cryptographic scheme, then if one of the agents with the key gets compromised, we can still provide  protection by mitigating and even quantifying the maximum amount of damage that can be done by the adversary.”

Hold your ground

In their paper, the researchers consider a problem known as “coverage,” in which robots position themselves to distribute some service across a geographic area — communication links, monitoring, or the like. In this case, each robot’s “vote” is simply its report of its position, which the other robots use to determine their own.

The paper includes a theoretical analysis that compares the results of a common coverage algorithm under normal circumstances and the results produced when the new system is actively thwarting a spoofing attack. Even when 75 percent of the robots in the system have been infiltrated by such an attack, the robots’ positions are within 3 centimeters of what they should be. To verify the theoretical predictions, the researchers also implemented their system using a battery of distributed Wi-Fi transmitters and an autonomous helicopter.

“This generalizes naturally to other types of algorithms beyond coverage,” Rus says.

The new system grew out of an earlier project involving Rus, Gil, Dina Katabi — who is the other Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT — and Swarun Kumar, who earned master’s and doctoral degrees at MIT before moving to Carnegie Mellon University. That project sought to use Wi-Fi signals to determine transmitters’ locations and to repair ad hoc communication networks. On the new paper, the same quartet of researchers is joined by MIT Lincoln Laboratory’s Mark Mazumder.

Typically, radio-based location determination requires an array of receiving antennas. A radio signal traveling through the air reaches each of the antennas at a slightly different time, a difference that shows up in the phase of the received signals, or the alignment of the crests and troughs of their electromagnetic waves. From this phase information, it’s possible to determine the direction from which the signal arrived.

Space vs. time

A bank of antennas, however, is too bulky for an autonomous helicopter to ferry around. The MIT researchers found a way to make accurate location measurements using only two antennas, spaced about 8 inches apart. Those antennas must move through space in order to simulate measurements from multiple antennas. That’s a requirement that autonomous robots meet easily. In the experiments reported in the new paper, for instance, the autonomous helicopter hovered in place and rotated around its axis in order to make its measurements.

When a Wi-Fi transmitter broadcasts a signal, some of it travels in a direct path toward the receiver, but much of it bounces off of obstacles in the environment, arriving at the receiver from different directions. For location determination, that’s a problem, but for radio fingerprinting, it’s an advantage: The different energies of signals arriving from different directions give each transmitter a distinctive profile.

There’s still some room for error in the receiver’s measurements, however, so the researchers’ new system doesn’t completely ignore probably fraudulent transmissions. Instead, it discounts them in proportion to its certainty that they have the same source. The new paper’s theoretical analysis shows that, for a range of reasonable assumptions about measurement ambiguities, the system will thwart spoofing attacks without unduly punishing valid transmissions that happen to have similar fingerprints.

“The work has important implications, as many systems of this type are on the horizon — networked autonomous driving cars, Amazon delivery drones, et cetera,” says David Hsu, a professor of computer science at the National University of Singapore. “Security would be a major issue for such systems, even more so than today’s networked computers. This solution is creative and departs completely from traditional defense mechanisms.”

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Robolliance is a new forum for technology partners and industry experts. Sponsored by Sharp Electronics Corporation, the program is a place for partners and experts in “robotics, surveillance and security to advance the understanding and awareness of the Autonomous Robotics marketplace.” The Robolliance program was created to bridge the gap between end-users, sales channel technology providers, and finding information in security solutions as they relate to emerging robotics technology.

Cliff Quiroga, Vice President & Deputy General Manager for Sharp Electronics Corporation (SEC), states, “There is a growing understanding that ‘Business Robotics’ will play an expanding role in the future of automation and how companies operate. The demands on the security industry, both from a manpower and technological standpoint, are growing exponentially. Robotics is a solution for organisations to expand their surveillance coverage in a safe, reliable way and liberate manpower to focus on the more demanding, intellectual aspects of security.”

“There’s no doubt, technology is disruptive,” says Alice DiSanto, Director of Marketing for SRBD. “Simplistically, every time I upgrade my smartphone there is a feature I must get comfortable using or a glitch I may have to work through. In the case of robotics for security and surveillance, the technology will be highly disruptive and we as sponsors need to be ahead of the curve in building understanding to ease adoption, streamline implementation and help organizations in search of a security solution find answers. Robolliance can be their resource.”

More information about Robolliance can be found on their website.

When a robot ethics working group met nearly three years ago one of the things we fretted about was privacy. We were concerned especially about personal companion robots. Whatever their purpose, be it healthcare for the elderly or disabled, or childcare, or simply Robot and Frank -style companionship, we debated the inevitable privacy issues of this kind of robot. Two aspects directly impact privacy, since personal companion robots are likely to (1) collect and store data and (2) be networked.

We also worried about hacking. The third of our Principles of robotics states: Robots are products: as with other products, they should be designed to be safe and secure. And the commentary for this principle has this to say about hacking:

We are aware that the public knows that software and computers can be “hacked” by outsiders, and processes need to be developed to show that robots are secure as far as possible from such attacks.

It seems our concerns were well founded. Last week a report appeared of a networked baby monitor that was apparently hacked. It was pretty distressing. The hacker was shouting abuse at the baby, chillingly using her name – it seems that he (let’s assume it was a he) was able to gain access to the baby monitor’s video feed and read the baby’s name displayed above her bed. Even more chilling (adding to the parent’s horror) the execrable hacker then turned the camera to look at them when they entered their child’s room to find out what was going on.

A WiFi IP Camera, aka Baby Monitor is, I contend, a teleoperated robot. The thing that makes it a robot is the motorised pan and tilt mechanism for steering the camera. So, despite password protection, Mr Gilbert’s networked robot was hacked. This particular robot, and I hazard tens of thousands like it, is absolutely not secure from attack.

The consequences of this particular attack were, fortunately, not much more serious than giving the Gilbert’s a fright they surely won’t forget for some time. But for me one particularly egregious aspect of this robot hack – something that the robot ethics working group did not anticipate – was the verbal abuse hurled at baby Allyson and her parents. It is with profound dismay that I ask the question: is the first case of RoboTrolling..?

Let’s say a white box showed up on your doorstep and you open it up and find a little humanoid robot made by Google. A GoogleBot! The brightly-colored pamphlet says that the little disk of a device will vacuum your floor, all for the same cost as your Gmail account: free.

Since you already use Google’s search engine, have a Gmail address, and use GoogleDocs, Voice, Earth, Wallet, wear a Google Glass and have been driving around one of those autonomous cars you’ve been selected as a beta tester! Whee! A free robot!

So what do you do with this thing?

Vacuum the floor, of course. Off it goes, bumping into walls, building its little map as it sucks up Doritos crumbs and dust bunnies.

Google’s greatest hits, like Search, Email, Docs, Voice, or Translation, are founded on, or fodder for, Natural Language Processing technologies (NLP).  NLP and other semantic technologies are also why Facebook and, in some cases Apple, has provided us with such great services. They analyze what we write and say.

Meanwhile, others are further ahead of this linguistic learning curve. The National Security Agency (NSA) of the United States was using primitive forms of NLP at least a decade before Google was using it for search.  This is common with technologies: Government agencies get theirs first, then the rest of us get to use the stuff later, whether it is global maps of the earth (1650s), satellite photograph (1950s), or GPS (1970s).

Anyway, there are four NSA data collection programs: MAINWAY, MARINA, NUCLEON and – specifically for Internet content, and the most important source of collection – the PRISM project, which by now we’ve all heard about.

On June 6 of 2013 The Washington Post and the Guardian reported that the system draws data from Google, Yahoo, Microsoft and other Silicon Valley giants, keeping the folks in Fort Meade, Maryland busy mining what is the richest depository of personal information in all human history. Evidently the NSA is sucking up 2.1 million gigabytes of data every hour of every day. All information is stored, all communication is monitored, and the fact that someone is peeping in on you has been held secret.

This, of course, has caused some concerns.

These technologies can be used in a predictive manner, as well.  What you want, what you think, and what you will do can be predicted with a 93% rate of accuracy, and that includes where you are now and where you will be in twenty-four hours.
We move across a daily landscape of emotions just as we move across a daily landscape of city streets. Semantic analysis (or, more accurately, probabilistic latent semantic analysis, and other technologies that look at our trends) shows where we’ll be and what we are likely to think and feel.

In early December of 2007, Marissa Mayer, now CEO of Yahoo and then the vice president of Google’s Search Products and User Experience division, showed how Google Trends can be used to predict presidential elections. She showed how Google Trends accurately predicted George W. Bush’s win over John Kerry in 2004, and also pointed out how Trends accurately predicted Nicolas Sarkozy’s win in May of 2007, over Segolene Royal in the French elections.

These trickles of valuable personal data add up to rivers of money.

Valuable enough to grow a company into the size of Google, or valuable enough to justify a $10 billion annual NSA budget, those little strings of text add up to lots and lots of real money.
Let’s not forget this Google robot that has shown up on our doorstep.  Our big hockey-puck robot (or “connected system” as it is more fashionably called) is buzzing around the floor doing the job for free.  This concept is now a prototype and has been operating for three years.

Though iRobot swatted the ConnectR remote-controlled domestic robot in the start of 2009, ending the product line, the concept refused to die. A year later, in March of 2010, the concept was picked up by a group of Swedish hackers who launched a project similar to what I outline above, named GåågleBot (pronounced “GoogleBot”). These Roomba hackers dissected the robot and inserted various items such as a camera, a Wi-Fi card, a small computer, and a good squirt of the AJAX programming language. After sewing it all back up, the thing still maintained operations as a normal Roomba.

The GåågleBot can also read. It comes with optical character recognition (OCR) software so that you (or someone else) can photograph and translate text that happens to be lying around the house, drop it into a database, and run searches on that text. GåågleBot source code and instructions are available for download.

A vacuum is just a vacuum, but these days it is part of what is commonly being called The Internet of Things.  Smart Houses, Connected Systems, or Robots … the basic idea is that we use networked computers in objects around us.  If we consider the Internet of Things as a physical extension of the Internet we find a new method emerging – as precise as existing analytics systems – that can measure how we each live and spend our money.  If you purchase a product, like a watch, and it communicates via a WiFi or Bluetooth network, then whoever or whatever it is communicating with will know where you are, who or what is nearby, your name, movements, past, present and, by prediction, future. Other objects near it will identify themselves and as you synchronize a device with a user account, and the overlapping data increases the relative value of both datasets. Your identity and the identity of you live with will be known by the company you bought it from, of course, and with it will come all of your purchases, likes, dislikes, relationships, and documents that are hanging heavy with fruit to be plucked by the pinchers of NLP and semantic analysis.

Meanwhile, the robot Google sent you rolls around on the floor, sucks up dust, and blinks a network indicator. It seems so humble.  But this is how Google, Yahoo, PRISM, the NSA, and therefor the US Government collects data that can be potentially very dangerous information. All from a little vacuum robot.  After all, dangerous bombs often come in humble packages.

Now, I don’t mean to sound like the Unabomber here, so I will point you to someone that has thought about this topic far more, and whom I am parrotting, and that is Bruce Schneier.  He has written a nice article on this topic, which you can find here.

Let’s not be paranoid. This vacuum, like an email service, may come in handy, and it doesn’t cost anything.  So we pull the robot out of the box, flip it on, poke in a password, and go back to eating Doritos.

Now let’s consider another possibility, which is that Google has mailed us not a vacuum hockey-puck, but a little humanoid robot that can walk and grab stuff. GoogleBot is an android that comes up to your knee, just a little guy with big feet and a cute head, kind of like NAO. So you set it down and it begins to explore the house. Sure, for the first few days it is a touch disturbing as it waddles around the house and reads the books on your shelves, and runs optical character recognition on all of your magazines, and seems to be studying the contents of your refrigerator and cupboards and dog food labels and clothing labels, and very carefully watches your television. Once you even see it touching your computer and you chase it away.  But eventually it settles down and you think, “That was kind of cute.” Then, finally you get used to it. It does the laundry and feeds the dog, and along the way, GoogleBot gets another cluster of data about your life – and you get your dishes done.

This system may be financed with the same model that Google uses today for things like email, search, voice and other services.  Since Google is publicly traded we can see that Amazon paid out $55m in 2011 to advertise for books or whatever.  So they would want to know what you buy, what you read, and what you have on the coffee table.  Home Depot paid out almost as much, so they would want to know what you have in the yard. State Farm and Progressive insurance companies paid Google nearly $90m, so they would have a great interest in knowing your health habits. All this cascades down into a long list of retail information, health, hygiene, and habits that covers what you are reading, what’s in your yard, what’s in your home, your refrigerator, etc. And so – to be useful – your GoogleBot should be busy dusting your bookshelves, washing your dishes, doing your laundry, and being an active participant in your personal health by cleaning the shower, toilet, and floor.

If someone types in “Self-Employed Health Insurance” and clicks on an ad, Google earns about $43. There are dozens of small robots that cost less than that, which means a home care companion robot of about $500 would amortize GoogleBot’s production cost and shipping in just a few weeks, and become wildly profitable after that robot had spent a year in your home.

Because our personal information is coveted by both corporations and governments, it seems inevitable that this home service robotics model will arrive some day in the coming decade.

Hopefully the GoogleBot or YahooBot or whateverBot will come with bulletproof SHA privacy switches built in, switches we can trust. Hopefully privacy options will allow us to put curtains on the parts of our lives we consider personal. That way we don’t have to stuff the robot in the freezer when the girlfriend comes over.  And perhaps even revenue options will let us collect money for the information Google collects about us (Google still wins, we just pay less usage tax). Or perhaps these are added features that we will have to pay for, like in-app purchases today.  Perhaps we will have to pay for our privacy.

There are lots of questions here. Least among them are how the NSA and companies like Google and Yahoo work together – and Google in particular has been quick to reply to these questions, as have other companies. They’re now on a mission to regain our confidence. But the NSA remains spooky, holds the reigns, and hides in the shadows. It seems they are collecting as much information as they can for long-term storage. The chief technology officer at the CIA, Gus Hunt, said last March; “We fundamentally try to collect everything and hang onto it forever.”

Any time citizen information is secretly stored by a government, it means that government is collecting the tools of a dictatorship.  Just ask Mussolini.  Or take a look at how Germans, familiar with the Nacht-und-Nebel program, or Sicherheitsdienst, are reacting this week. Information is generally collected so that it can be used.

Of course the common response is, “I’ve got nothing to hide,” but the bottom line isn’t about having nothing to hide.  It’s about not wanting to show everything.

Privacy is about having the decency to wear pants, the right to have curtains, and the right to whisper sweet coo-coos in your lover’s ear without someone else, like GoogleBot, YahooBot and the NSA leaning in for a little listen.