Lifeboat Foundation

Taiwan’s semiconductor industry, a centerpiece of the global supply chain for smartphones and computing equipment, was the focus of a hacking campaign targeting corporate data over the last two years, Taiwan-based security firm CyCraft Technology claimed Thursday.

The hackers went after at least seven vendors in the semiconductor industry in 2018 and 2019, quietly scouring networks for source code and chip-related software, CyCraft said. Analysts say the campaign, which reportedly hit a sprawling campus of computing firms in northwest Taiwan, shows how the tech sector’s most prized data is sought out by well-resourced hacking groups.

“They’re choosing the victims very precisely,” C.K. Chen, senior researcher at CyCraft, said of the hackers. “They attack the top vendor in a market segment, and then attack their subsidiaries, their competitors, their partners and their supply chain vendors.”

Victims included Democratic presidential candidate Joe Biden, former President Barack Obama and Tesla CEO Elon Musk. Accounts for those people, and others, posted tweets asking followers to send bitcoin to a specific anonymous address.

For their efforts, the scammers received over 400 payments in bitcoin, with a total value of $121,000 at Thursday’s exchange rate, according to an analysis of the Bitcoin blockchain performed by Elliptic, a cryptocurrency compliance firm.

Elliptic co-founder Tom Robinson said it’s a low sum for what appears to be a historic hack that Twitter said involved an insider.

Kaspersky says Oilrig (APT34) group has been using DoH to silently exfiltrate data from hacked networks.

PNNL quantum algorithm theorist and developer Nathan Wiebe is applying ideas from data science and gaming hacks to quantum computing.

Everyone working on quantum computers knows the devices are error prone. The basic unit of quantum programming – the quantum gate – fails about once every hundred operations. And that error rate is too high.

While hardware developers and programming analysts are fretting over failure rates, PNNL’s Nathan Wiebe is forging ahead writing code that he is confident will run on quantum computers when they are ready. In his joint appointment role as a professor of physics at the University of Washington, Wiebe is training the next generation of quantum computing theorists and programmers.

One of the world’s most prolific hacking groups recently infected several Massively Multiplayer Online game makers, a feat that made it possible for the attackers to push malware-tainted apps to one target’s users and to steal in-game currencies of a second victim’s players.

Authorities in Florida say a 17 year old was the “mastermind” of the attack that targeted the accounts of Barack Obama, Joe Biden, Kanye West, Bill Gates and others.

Twitter’s headquarters in San Francisco on Oct. 21, 2015. David Paul Morris / Bloomberg via Getty Images file.

Computer programming has never been easy. The first coders wrote programs out by hand, scrawling symbols onto graph paper before converting them into large stacks of punched cards that could be processed by the computer. One mark out of place and the whole thing might have to be redone.

Nowadays coders use an array of powerful tools that automate much of the job, from catching errors as you type to testing the code before it’s deployed. But in other ways, little has changed. One silly mistake can still crash a whole piece of software. And as systems get more and more complex, tracking down these bugs gets more and more difficult. “It can sometimes take teams of coders days to fix a single bug,” says Justin Gottschlich, director of the machine programming research group at Intel.

Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data.

An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data.

In recent years, this type of attack has become increasingly common, highlighting the vulnerabilities and flaws of many deep neural networks. A specific type of adversarial attack that has emerged in recent years entails the addition of adversarial patches (e.g., logos) to images. This attack has so far primarily targeted models that are trained to detect objects or people in 2-D images.

More from 2020 “The Movie”

Chinese government-linked hackers targeted biotech company Moderna Inc, a leading U.S.-based coronavirus vaccine research developer, earlier this year in a bid to steal valuable data, according to a U.S. security official tracking Chinese hacking activity.

WASHINGTON (Reuters) — Chinese government-linked hackers targeted biotech company Moderna Inc, a leading U.S.-based coronavirus vaccine research developer, earlier this year in a bid to steal valuable data, according to a U.S. security official tracking Chinese hacking activity.