death of money 2014

 

James Rickards, The death of money, 2014                                    [ ]

p.32

   My first contribution was to point out that the CIA's objective was already being pursued every day by hedge funds, but for a different reason. The CIA was trying to spot terrorist traders, while hedge funds were trying to spot unannounced takeovers. But the big-data techniques applied to trading patterns were the same. 

   Spotting suspicious trading is a three-step process. Step one is to establish a baseline for normal trading, using metrics like volatility, average daily volume, put-call ratios, short interest, and momentum. Step two is to monitor trading and spot anomalies relative to the baseline. Step three is to see if there is any public information to explain the move. If a stock spikes because Warren Buffett bought a large position, that's not an anomaly; it is to be expected. The intriguing case is when a stock spikes on no news. The logical inference is that someone knows something you don't. A hedge fund might not care about the origin of the hidden information--it can just piggyback on the trade. 

pp.33-34

We also concluded the insider trade was likely to be executed in the options market less than 72 hours before the attack to minimize risk of detection. 

p.34

We created an automated threat board interface that broke the markets into sectors and displayed tickers with red, amber, and green lights, indicating the probability of insider trading. 

p.35

We routinely picked up signals that indicated insider trading. These signals were from regular market players; there was nothing yet to indicate that the insider trading was terror related. Our project had no legal enforcement powers, so we simply referred these cases to the SEC and otherwise ignored them. 

p.39

Randy Tauss, 33-year veteran, 

“Jim, let me tell you how things work around here. We'll do a great job, and this thing will work like a charm. Then it will go nowhere and be put on a shelf. One day there will be a spectacular attack, and it will be apparent there was advance insider trading. The agency will pull our work from the shelf, dust it off, and say, ‘See, we have the solution right here. We have a system that can detect this next time.’ That system will get millions in funding and be built the way we wanted. But it will be too late to save lives in the next attack.”

p.40

All we needed to do was calibrate the signal engine to focus on specially tailored target sets of securities.  

p.41

   Remember the truism No one trade alone. For every buyer, there is a seller. If one side of a trade is a threat to national security, it leaves a trace that the enemy did not intend. The enemy trader is like a fish swimming in the water; it leaves ripples. Even if the fish is invisible, the ripples can be seen, and the presence of the fish inferred. The forward-thinkers at that meeting in Omaha recognized that our signal engine could detect the ripples, that we had devised the perfect early warning device. 

   MARKINT would have a future after all. It would be not the narrow counterterrorist tool we had set out to create, but rather a broad-based system, a sort of radar for the marketplace that was designed to detect incoming financial threats. 

   (The death of money : the coming collapse of the international monetary system, James Rickards, 2014, )

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Recorded Future

 The company (Recorded Future) specializes in the collection, processing, analysis, and dissemination of intelligence. 

In 2007, co-founders Christopher Ahlberg and Staffan Truvé, both Ph.D.s in computer science from Chalmers University of Technology, filed for Recorded Future’s first patent (granted in 2013 as United States patent US8468153B2) - Data Analysis System with Automated Query and Visualization Environment Setup.[1] The patent laid the foundation for continuous collection and processing of data and information from sources across the open, deep, and dark web, facilitated by machine learning. Recorded Future was officially incorporated in 2009. 

The company received initial funding from Google and In-Q-Tel, as reported in a July 2010 introduction to Recorded Future published by Wired.[2]

In-Q-Tel (IQT) is a not-for-profit venture capital arm of United States intelligence community (CIA), investing in high-tech start-up companies.

In May 2017, Recorded Future introduced Insikt Group,[7] the company’s threat intelligence research arm. The word “insikt” is Swedish, a nod to Recorded Future's co-founders, and means “insight.” Insikt Group is responsible for delivering analyst-generated assessments, insights, and recommended actions to customers and the public. 

Using what they call a "Temporal Analytics Engine," Recorded Future provides forecasting and analysis tools to help analysts predict future events by scanning sources on the internet, and extracting, measuring, and visualizing the information to show networks and patterns in the past, present, and future.[10] As of 2015, the engine was described as "Web Intelligence Engine."[11] Likewise, the Washington Post, in an article authored by Stewart Baker - the former General Counsel of the National Security Agency (1992–1994), which had described the company as a predictive analytics web intelligence firm deleted the term upon request of RF.[12] The software analyzes sources and forms "invisible links" between documents to find links that tie them together and may possibly indicate the entities and events involved. Noah Schachtmann from WIRED – who first wrote about Google and the CIA both investing in RF – described the company in an interview as follows: "Recorded Future is a company that strips out from web pages the sort of who, what, when, where, why — sort of who’s involved, [...] where are they going, what kind of events are they going to."[13]

https://en.wikipedia.org/wiki/Recorded_Future

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June 23, 2021

Mouse movements reveal your behavior

by University of Luxembourg

[Image: computer mouse]

Credit: Pixabay/CC0 Public Domain

In two recently published research papers, computer scientists from the University of Luxembourg and international partners show how mouse movements can be used to gain additional knowledge about the user behavior. While this has many interesting applications, mouse movements can also reveal sensitive information about the users such as their age or gender. Scientists want to raise awareness about these potential privacy issues and have proposed measures to mitigate them.

Prof. Luis Leiva from the University of Luxembourg and corresponding author of the two papers explains in more details the key findings.

My mouse, my rules

"We have demonstrated how straightforward it is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. For years, recording mouse movements on websites has been easy, however to analyze them one would need advanced expertise in computer science and machine learning. Today, there are many libraries and frameworks that allows anyone with a minimum of programming knowledge to create rather sophisticated classifiers. This raises new privacy issues and users do not have an easy opt -out mechanism."

[Image: Mouse movements reveal your behaviour]

Credit: University of Luxembourg

Based on their results, the team developed a method to prevent mouse tracking by distorting the mouse coordinates in real-time. "It is inspired by recent research in adversarial machine learning, and has been implemented as a web browser extension, so that anyone can benefit from this work in practice," explains Leiva. The web browser extension called MouseFaker is available on Github.

This work has been presented at the 6th ACM SIGIR Conference on Human Information Interaction and Retrieval.

When choice happens

Nevertheless, mouse tracking has very practical applications for webmasters, and in particular for search engines. Dr. Ioannis Aparakis from Telefonica Research and co-author of both publications, clarifies: "When you search for something at Google or Bing, your mouse movements are sending a tiny signal to the search engine indicating if you are interested or not in the content you have been shown. As mouse tracking may have privacy issues, we investigated the possibility of recording only a small part of the whole movement trajectory and see if we can still infer how people make choices in web search."

The team analyzed three representative scenarios where users had to make a choice on web search engines: when they notice an advertisement, when they abandon the page, and when they become frustrated. The results are interesting: if users pay attention to an ad, it will be signaled by the initial mouse movements. In case of page abandonment, it is actually the opposite: the last movements inform whether the user has decided to leave either if they were satisfied with the search results or not, without having to click on anything. In the frustration case, results were mixed but it seemed the middle part of a mouse movement trajectory provides more information than the initial or final parts.

The researchers found that it is possible to predict the aforementioned tasks sometimes using just two or three seconds of mouse movement. Therefore, they conclude that, by only tracking the interesting parts, search engines could get useful information and improve their services while respecting the users' privacy. This work will be presented at the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Prof. Leiva says, "By efficiently recording the right amount of movement data, we can save valuable bandwidth and storage, respect the user's privacy, and increase the speed at which machine learning models can be trained and deployed. Considering the web scale, doing so will have a net benefit on our environment."

Explore further

DuckDuckGo can now block the Google Chrome tracking method, FLoC

More information: Luis A. Leiva et al, My Mouse, My Rules, Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (2021). DOI: 10.1145/3406522.3446011

When Choice Happens: A Systematic Examination of Mouse Movement Length for Decision Making in Web Search, Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), DOI: 10.1145/3404835.3463055

Provided by University of Luxembourg

Citation: Mouse movements reveal your behavior (2021, June 23) retrieved 23 June 2021 from https://techxplore.com/news/2021-06-mouse-movements-reveal-behavior.html

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Your Body, Your Login

A team of Dutch and Italian researchers has found that the way you move your phone to your ear while answering a call is as distinct as a fingerprint. You take it up at a speed and angle that’s almost impossible for others to replicate. Which makes it a more reliable password than anything you’d come up with yourself. (The most common iPhone password is “1234.”) Down the line, simple movements, like the way you shift in your chair, might also replace passwords on your computer. It could also be the master key to the seven million passwords you set up all over the Internet but keep forgetting. 

Chris Wilson

http://www.nytimes.com/interactive/2012/06/03/magazine/innovations-issue.html

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