Collateral Damage by Cyberattacks

Social media increasingly gives you access to friends and family of influential people. These digital assets are likely to be under renewed attack by cyberplayers including domestic and foreign hackers.

If DNC Campaign members can be hacked, then who stops or defends Ivanka Trump from being hacked by ideologues. The Secret Service lacks a cyber corps (as events have shown)

One man’s terrorist is another man’s freedom fighter. One man’s cyber terrorist is another man’s cyber activist.

Can self driving cars be used for terrorism

I wonder if self driving car enthusiasts have created a backdoor for failsafe driving. This is based on recent reports of ISIS using automobiles as weapons of mass destruction (esp in France et al) , as well as the convergence of self driving cars without human intervention. Same goes for drones unfortunately.

Applying Statistics to Hacking and Cyberattacks

Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true. The usual process of hypothesis testing consists of four steps.

  1. Formulate the null hypothesis  (commonly, that the observations are the result of pure chance) and the alternative hypothesis (commonly, that the observations show a real effect combined with a component of chance variation).

from http://mathworld.wolfram.com/HypothesisTesting.html

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Now let us take this way of thinking to the recent elections. Multiple scenarios can be tested.

  1. Clinton Campaign was bad in cyber security and cyber activist/ hackers breached both them as security of state and as candidate to highlight lack of cyber security
  2. Clinton as wife of ex President was not suitable to the ultra liberal cyber activists as conducive to democracy (ie. Bush,Clinton,Clinton,Bush,Bush,Obama,Obama,Clinton– would have been the Presidential roll call)
  3. Sustained hacking by cyber activists is also true for certain opponents ( Clinton had been a key opponent of Manning, Snowden et al)
  4. State players including intelligence agencies usually keep an arm’s length distance to maintain plausible deniability
  5. The CIA and NYTimes were able to firmly pinpoint the Russian Govt backed hackers only a few days after elections even though these activities seemed to have gone over a few years
  6. The FBI was investigating the Clinton (not the Trumps) for irresponsible  cyber security and publicly said BEFORE elections
  7. Both FBI and CIA will see drastic personnel and leadership changes in a new adminsitration
  8. Where is the log data for breach of networks by Russian IP addresses (which does not mean they are in Russia- remember Tor)? Why cant it be shown publicly? Why cant charges be filed in a US court for illegal activity
  9. Quis custodiet ipsos custodes? Who guards the guardians of American cyber space. Even though the US has the largest conventional and nuclear military- do the recent incidents show a colossal underinvestment in cyber warfare and cyber defence by the Pentagon
  10. In God, We trust. Every one else must bring data. Currently the whole hacking, server debate is more like an episode of Big Bang Theory combined with Hackers. Data can and should be published ( just like Enron data was published)
  11. A better statistician /hacker than me can then formulate the hypothesis on who was responsible for breaching and releasing the information from DNC

The inevitable rise in transnational cyberactivism

There are four kinds of hackers

  1. Kickass- working for NSA, and apparently China in hacking Google, and Russia in hacking Clintonian Dynasty from Castle Rock. Not to forget Israelis who did Stuxnet
  2. Dumbass- FBI paying a lot to decrypt an iPhone and using Wikileaks data to make statements a week BEFORE elections
  3. Smartass-Wikileaks
  4. Wiseass- CIA describing Russian hack efforts AFTER elections

Meanwhile in Moscow and St Petersburg and Romania, old friends are meeting over glasses of vodka and asking each other whether they did the “Trump” hack and if they got paid in gold, diamonds or casino chips

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Data Analytics post Demonetization in India

The demonetisation of ₹500 and ₹1000 banknotes was a policy enacted by the Government of India on 8 November 2016.

The announcement was made by the Prime Minister  Narendra Modi .PM  Modi declared that use of all ₹500 and ₹1000 banknotes would be invalid from midnight  and announced the issuance of new ₹500 and ₹2000 banknotes in exchange for the old banknotes.

The government claimed that the demonetisation was an effort to stop counterfeiting of the current banknotes allegedly used for funding terrorism, as well as a crack down on black money in the country. The move was described as an effort to reduce corruption, the use of drugs, and smuggling.

(source – https://en.wikipedia.org/wiki/Indian_500_and_1000_rupee_note_demonetisation )

 

This led to huge lines of people  outside banks and ATMs to withdraw new notes for daily needs and depositing cash to exchange notes f older denominations.

This also leads to a huge data analytics opportunity for data science to serve treasury and tax departments of India. The following data points would be of particular scrutiny for Indian data scientists helping or om contract to Indian Govt.

  1. Fraud– This would examine data points where inactive and dormant bank accounts suddenly had a huge inflow of cash. This data would be further matched and merged with income tax records using PAN CARD as a matching and AADHAR CARD too. Additional matching keys would be Name, Date of Birth, Address
  2. Terrorism – Terrorism in India is specific to a few geographic areas like Jammu and Kashmir and Naxalite areas. These could be further analyzed for fine tume of unusual currency patterns
  3. Cashless modes for laundering money ( Anti Money Laundering)- Plastic Money and Mobile apps saw a huge upsurge for transactions. This could be further used for additional sources of information since KYC norms of Telecom need Identification and so do Bank Accounts.
  4. Specific sectors- Land (real estate), Jewelery and other high value, high ticket items can be scrutinized

 

Overall data will be huge, so choosing the right database combination as well as the analytic (including especialy Big Data Spatial Analytics) could be key to help the current PM ‘s ambitious vision to transform India’s economy.

Comments are welcome.