Reuters: Memory Issue in Software Causes Problems in US Air Traffic Control System

A vulnerability in the US air traffic control system’s software, triggered by a military aircraft’s complicated flight plan, caused flight delays and could crash the system in the future, according to a new report by Reuters. On 30 April 2014, a US U-2 spy plane flew through the Western US. Its lack of altitude information in the flight plan as well as its complexity—which was circular rather than point-to-point as is typical for commercial flights—caused the Federal Aviation Administration’s En Route Automation Modernization (ERAM) air-traffic-control software to register an operational error and cycle on and off. This process used much of the system’s memory, leaving it unavailable for other tasks, causing problems throughout the Western US including the grounding or delaying of flights in Los Angeles. Experts cited by Reuters contend that attackers could use the same vulnerability to crash the system, although doing so would be complicated. The FAA has added memory to ERAM and changed some of its rules in an effort to prevent such problems. Weaknesses in the system will be the subject of two Def Con hacker convention talks in August 2014. (Reuters)(BBC)

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'Anternet' discovered: Behavior of harvester ants as they forage for food mirrors protocols that control Internet traffic

ScienceDaily (Aug. 28, 2012) — On the surface, ants and the Internet don't seem to have much in common. But two Stanford researchers have discovered that a species of harvester ants determine how many foragers to send out of the nest in much the same way that Internet protocols discover how much bandwidth is available for the transfer of data. The researchers are calling it the "anternet."

Deborah Gordon, a biology professor at Stanford, has been studying ants for more than 20 years. When she figured out how the harvester ant colonies she had been observing in Arizona decided when to send out more ants to get food, she called across campus to Balaji Prabhakar, a professor of computer science at Stanford and an expert on how files are transferred on a computer network. At first he didn't see any overlap between his and Gordon's work, but inspiration would soon strike.

"The next day it occurred to me, 'Oh wait, this is almost the same as how [Internet] protocols discover how much bandwidth is available for transferring a file!'" Prabhakar said. "The algorithm the ants were using to discover how much food there is available is essentially the same as that used in the Transmission Control Protocol."

Transmission Control Protocol, or TCP, is an algorithm that manages data congestion on the Internet, and as such was integral in allowing the early web to scale up from a few dozen nodes to the billions in use today. Here's how it works: As a source, A, transfers a file to a destination, B, the file is broken into numbered packets. When B receives each packet, it sends an acknowledgment, or an ack, to A, that the packet arrived.

This feedback loop allows TCP to run congestion avoidance: If acks return at a slower rate than the data was sent out, that indicates that there is little bandwidth available, and the source throttles data transmission down accordingly. If acks return quickly, the source boosts its transmission speed. The process determines how much bandwidth is available and throttles data transmission accordingly.

It turns out that harvester ants (Pogonomyrmex barbatus) behave nearly the same way when searching for food. Gordon has found that the rate at which harvester ants -- which forage for seeds as individuals -- leave the nest to search for food corresponds to food availability.

A forager won't return to the nest until it finds food. If seeds are plentiful, foragers return faster, and more ants leave the nest to forage. If, however, ants begin returning empty handed, the search is slowed, and perhaps called off.

Prabhakar wrote an ant algorithm to predict foraging behavior depending on the amount of food -- i.e., bandwidth -- available. Gordon's experiments manipulate the rate of forager return. Working with Stanford student Katie Dektar, they found that the TCP-influenced algorithm almost exactly matched the ant behavior found in Gordon's experiments.

"Ants have discovered an algorithm that we know well, and they've been doing it for millions of years," Prabhakar said.

They also found that the ants followed two other phases of TCP. One phase is known as slow start, which describes how a source sends out a large wave of packets at the beginning of a transmission to gauge bandwidth; similarly, when the harvester ants begin foraging, they send out foragers to scope out food availability before scaling up or down the rate of outgoing foragers.

Another protocol, called time-out, occurs when a data transfer link breaks or is disrupted, and the source stops sending packets. Similarly, when foragers are prevented from returning to the nest for more than 20 minutes, no more foragers leave the nest.

Prabhakar said that had this discovery been made in the 1970s, before TCP was written, harvester ants very well could have influenced the design of the Internet.

Gordon thinks that scientists have just scratched the surface for how ant colony behavior could help us in the design of networked systems.

There are 11,000 species of ants, living in every habitat and dealing with every type of ecological problem, Gordon said. "Ants have evolved ways of doing things that we haven't thought up, but could apply in computer systems. Computationally speaking, each ant has limited capabilities, but the collective can perform complex tasks.

"So ant algorithms have to be simple, distributed and scalable -- the very qualities that we need in large engineered distributed systems," she said. "I think as we start understanding more about how species of ants regulate their behavior, we'll find many more useful applications for network algorithms."

The work is published in the Aug. 23 issue of PLoS Computational Biology.

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The above story is reprinted from materials provided by Stanford University. The original article was written by Bjorn Carey.

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Journal Reference:

Balaji Prabhakar, Katherine N. Dektar, Deborah M. Gordon. The Regulation of Ant Colony Foraging Activity without Spatial Information. PLoS Computational Biology, 2012; 8 (8): e1002670 DOI: 10.1371/journal.pcbi.1002670

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A simple way to help cities monitor traffic more accurately

ScienceDaily (Aug. 7, 2012) — Cities count the number of cars on the road in order to plan everything from the timing of stoplights to road repairs. But the in-road metal detectors that do the counting can make errors -- most often by registering that a car is present when one isn't.

One common error is called "splashover" because it usually involves an over-sensitive detector picking up the presence a vehicle in the next lane over -- as if the signal from the car "splashed over" into the adjacent lane.

Now Ohio State University researchers have developed software to help city managers easily identify detectors that are prone to splashover and reprogram them to get more accurate numbers.

Benjamin Coifman, associate professor of Civil, Environmental and Geodetic Engineering at Ohio State, and doctoral student Ho Lee describe the software in the October 2012 issue of the journal Transportation Research Part C: Emerging Technologies.

For the study, Coifman and Lee monitored 68 in-road detectors in Columbus, Ohio. They found six detectors that were prone to erroneously detecting cars in adjacent lanes. Error rates ranged from less than 1 percent to 52 percent.

"A host of city services rely on these data. We've known about splashover for decades, but up until now, nobody had an effective automatic test for finding it," said Coifman. "With this software, we can help transportation departments know which detectors to trust when deciding how they should put their limited dollars to work."

People may not be familiar with the commonly used loop detectors, which are often present at intersections to activate a stoplight. When the detectors are visible, they look like rectangular cutouts in the road surface, where underground wiring connects the detector to a traffic box at the side of the road. The same detectors are often present at freeway onramps and exits, to help cities monitor congestion.

To see how often splashover occurred in the 68 detectors in the study, the researchers went to the sites, and noted whether a car was truly present each time a detector counted a car. Then they used those data to construct computer algorithms that would automatically identify the patterns of error.

In tests, the software correctly identified four of the six detectors that exhibited splashover. The two it missed were sites with error rates less than 1 percent -- specifically 0.6 percent and 0.9 percent.

"We might not catch detectors in which one in 100 or one in 1,000 vehicles trigger splashover," Coifman said, "but for the detectors where the rate is one in 20, we'll catch it."

The discovery comes just as many American cities are moving toward the use of different technologies, such as roadside radar detectors, to monitor traffic.

"The world is moving away from loop detectors," Coifman added. "And the radar sensors that are replacing loop detectors are actually more prone to splashover-like errors."

These radar detectors bounce a signal off a car and measure the time it takes for the signal to return. Because the detectors are on the side of the road, small measurement errors often cause a single vehicle to be counted in two separate lanes by the radar.

The same algorithms they developed for loop detectors should work for radar detectors, Coifman said. The makers of radar detectors keep their software proprietary, so he can't readily test that hypothesis, though he points out that all of the details of the Ohio State algorithms are fully explained in the article, should radar makers wish to incorporate it into their products.

This study was facilitated by the Ohio Department of Transportation, and funded by NEXTRANS, the U.S. Department of Transportation Region V Regional University Transportation Center; and by the California PATH (Partners for Advanced Highways and Transit) Program of the University of California, in cooperation with the State of California Business, Transportation and Housing Agency, Department of Transportation.

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The above story is reprinted from materials provided by Ohio State University. The original article was written by Pam Frost Gorder.

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