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Over $7.7 billion stolen in crypto scams in 2021 and 4 other updates you should know




The top cryptocurrencies by market value remain in the red this week.

Bitcoin is currently trading at around $46,766, according to Coin Metrics, and Ether is trading at around $3,880. Both are down around 6% in the last seven days.

But a few cryptocurrencies in the top 10 managed to withstand the downturn. Terra’s LUNA is up over 29% in the last week and is now trading at $80, according to Coin Gecko. Avalanche’s AVAX is also up 29% in that time frame and is currently trading at $113.

Along with price movement, here are more important things that happened in the crypto space last week.

1. Elon Musk said that Tesla will accept dogecoin as payment for merch

2. Cryptocurrency prices listed on Coinbase and CoinMarketCap briefly glitch

Also on Tuesday, a glitch on exchange Coinbase and price-tracker CoinMarketCap listed inaccurate prices of cryptocurrencies, most of which appeared to be overblown and inflated in value.

Coinbase and CoinMarketCap users were confused, as this issue drastically altered many wallet balances on the platforms.

The issue has since been resolved on both Coinbase and CoinMarketCap.

Coinbase blamed CoinMarketCap and its pricing data for its glitch, but the the exact cause is still unclear.


3. Senate Banking Committee members mention concern over stablecoins and DeFi

During a Senate Banking Committee hearing on Tuesday, members discussed stablecoins, which are cryptocurrencies that are meant to be pegged to a reserve asset like the U.S. dollar.

Chairman Sherrod Brown, D-Ohio, shared his thoughts on stablecoins and their risks in his opening statement.

“Let’s be clear about one thing: If you put your money in stablecoins, there’s no guarantee you’re going to get it back,” Sen. Brown said. “Stablecoins and crypto markets aren’t actually an alternative to our banking system. They’re a mirror of the same broken system — with even less accountability and no rules at all.”

Senator Elizabeth Warren, D-Mass., agreed. “Stablecoins pose risks to consumers and to our economy,” she tweeted following the hearing.

“They’re propping up one of the shadiest parts of the crypto world, DeFi, where consumers are least protected from getting scammed. Our regulators need to get serious about clamping down before it is too late.”

4. $7.7 billion was stolen in crypto scams in 2021, report says

Over $7.7 billion was stolen in cryptocurrency scams worldwide in 2021, according to a new report by blockchain analytics firm Chainalysis. That’s an 81% rise compared to 2020.

Rug pulls, a type of scam where developers abandon a project and leave with investors’ funds, became the “go-to scam” of the decentralized finance, or DeFi, ecosystem, Chainalysis wrote in its report.

In 2021, rug pulls accounted for over $2.8 billion stolen, or 37% of all cryptocurrency scam revenue, compared to 1% in 2020. 



How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning



How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.


Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.


Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.


No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.

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