Measuring the Rationality of Security Decisions
Interesting research: “Dancing Pigs or Externalities? Measuring the Rationality of
Security Decisions“:
Abstract: Accurately modeling human decision-making in security is critical to thinking about when, why, and how to recommend that users adopt certain secure behaviors. In this work, we conduct behavioral economics experiments to model the rationality of end-user security decision-making in a realistic online experimental system simulating a bank account. We ask participants to make a financially impactful security choice, in the face of transparent risks of account compromise and benefits offered by an optional security behavior (two-factor authentication). We measure the cost and utility of adopting the security behavior via measurements of time spent executing the behavior and estimates of the participant’s wage. We find that more than 50% of our participants made rational (e.g., utility optimal) decisions, and we find that participants are more likely to behave rationally in the face of higher risk. Additionally, we find that users’ decisions can be modeled well as a function of past behavior (anchoring effects), knowledge of costs, and to a lesser extent, users’ awareness of risks and context (R2=0.61). We also find evidence of endowment effects, as seen in other areas of economic and psychological decision-science literature, in our digital-security setting. Finally, using our data, we show theoretically that a “one-size-fits-all” emphasis on security can lead to market losses, but that adoption by a subset of users with higher risks or lower costs can lead to market gains
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vas pup • August 7, 2018 9:02 AM
Tag – academic paper:
@Clive and other technical gurus – you should love this:
AI device identifies objects at the speed of light
The 3D-printed artificial neural network can be used in medicine, robotics and SECURITY:
https://www.sciencedaily.com/releases/2018/08/180802130750.htm
“Electrical and computer engineers have created a physical artificial neural network — a device modeled on how the human brain works — that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer
The UCLA-developed device gets a head start. Called a “diffractive deep neural network,” it uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object. The UCLA device does not need advanced computing programs to process an image of the object and decide what the object is after its optical sensors pick it up. And no energy is consumed to run the device because it only uses diffraction of light.
The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.
Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.
The researchers then trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device. The “training” used a branch of artificial intelligence called deep learning, in which machines “learn” through repetition and over time as patterns emerge.”