Creating an Effective Sock Puppet for OSINT Investigations – Introduction – Jake Creps Introduction and Philosophy In recent light of the epic failure by Surefire Intelligence to frame Robert Mueller for sexual assault allegations, I feel it’s important to discuss and unpack how to make a good sock puppet for OSINT operations. If you aren’t familiar, just google Jacob Wohl or Surefire Intelligence and you will likely be flooded with information about the scandal. For further details on the unraveling of the socks Wohl made, check out Aric Toler’s threat on Twitter @arictoler from Bellingcat.
Data Is a Toxic Asset Thefts of personal information aren't unusual. Every week, thieves break into networks and steal data about people, often tens of millions at a time. Most of the time it's information that's needed to commit fraud, as happened in 2015 to Experian and the IRS. Sometimes it's stolen for purposes of embarrassment or coercion, as in the 2015 cases of Ashley Madison and the US Office of Personnel Management. The latter exposed highly sensitive personal data that affects security of millions of government employees, probably to the Chinese. Mastering Google Search Operators in 67 Easy Steps See Also:• Google Search Operators - Best Practices• 25 Killer Combos for Google's Site: Operator Any SEO worth their sustainably harvested pink Himalayan salt knows that Google offers a variety of advanced search operators – special commands that take you above and beyond regular text searches. Learning search operators is a bit like learning chess, though.
90% of security incidents trace back to PEBKAC and ID10T errors “Apparently, hackers really do still party like it’s 1999,” Verizon said in its 2015 Data Breach Investigations Report (DBIR) regarding how often really old vulnerabilities are exploited and result in data breaches. But the real problem is you. It’s me. It’s each and every one of us as the breakdown of security incidents in 2014 revealed that the “common denominator—accounting for nearly 90% of all incidents—is people.” How to Search Twitter - The Best Twitter Search Tricks The Twitter Archiver and Twitter Bots app fire each time a new tweet is found that match your search query. You can write simple search queries (like #Oscars) or more complex query (like obama min_retweets:10 filter:news) that uses one or more Twitter search operators. Here’s a complete list of Twitter search operators that can help you perform more accurate searches on Twitter: from:BarackObama All tweets sent by a particular Twitter user
Electrospaces.net: What is known about NSA's PRISM program (Updated: August 19, 2016) Therefore, this article presents almost everything we know about the PRISM program, combining information from my earlier postings and from other media and government sources. It shows that PRISM is not about bulk or mass surveillance, but for collecting communications of specifically identified foreign targets. NSA also has no "direct access" to the servers of companies like Microsoft, Facebook and Google - it's actually a unit of the FBI that picks up data related to specific identifiers.
Google hacking Basics One can even retrieve the username and password list from Microsoft FrontPage servers by inputting the given microscript in Google search field: "#-Frontpage-" inurl: administrators.pwd or filetype: log inurl password login Devices connected to the Internet can be found. A search string such as inurl: "ViewerFrame?Mode=" will find public web cameras.
The real story in the NSA scandal is the collapse of journalism Updated June 9 to include details of the Guardian's coverage, a link to the Post's correction policy, and a quote from the Huffington Post. Updated June 10 to include a quote from a follow-up article in the Post directly contradicting its initial claims and another observation after the release of the leaker's identity. On Thursday, June 6, the Washington Post published a bombshell of a story, alleging that nine giants of the tech industry had “knowingly participated” in a widespread program by the United States National Security Agency (NSA). One day later, with no acknowledgment except for a change in the timestamp, the Post revised the story, backing down from sensational claims it made originally.
Experts: Spy used AI-generated face to connect with targets LONDON (AP) — Katie Jones sure seemed plugged into Washington’s political scene. The 30-something redhead boasted a job at a top think tank and a who’s-who network of pundits and experts, from the centrist Brookings Institution to the right-wing Heritage Foundation. She was connected to a deputy assistant secretary of state, a senior aide to a senator and the economist Paul Winfree, who is being considered for a seat on the Federal Reserve. But Katie Jones doesn’t exist, The Associated Press has determined. Instead, the persona was part of a vast army of phantom profiles lurking on the professional networking site LinkedIn.
Google Transparency Report 8 oct. 2018 Our "Requests for user information" Transparency Report has been updated to include data for the first half of 2018. We received 57,868 government requests for information about 126,581 accounts during the first half of 2018. Visit the report to explore the new data! Boolean basics: How to write a search query for newsgathering that works When searching for newsworthy content online, you’ve got to know exactly what you’re looking for and have the skills to find it. This is where Boolean search queries help. These strings of words allow you to cut through the usual social media chatter by upgrading a default search to a multifaceted, specific search to find more precise snippets of information. In this quick guide, we run through the basics of what you need to know to search social media for effective newsgathering.
How to recognize fake AI-generated images - Kyle McDonald - Medium In 2014 machine learning researcher Ian Goodfellow introduced the idea of generative adversarial networks or GANs. “Generative” because they output things like images rather than predictions about input (like “hotdog or not”); “adversarial networks” because they use two neural networks competing with each other in a “cat-and-mouse game”, like a cashier and a counterfeiter: one trying to fool the other into thinking it can generate real examples, the other trying to distinguish real from fake. The first GAN images were easy for humans to identify.