Live Updates: COVID-19 Cases
  • World 11,033,879
    Confirmed: 11,033,879
    Active: 4,321,990
    Recovered: 6,186,741
    Death: 525,148
  • USA 2,839,292
    Confirmed: 2,839,292
    Active: 1,515,862
    Recovered: 1,191,886
    Death: 131,544
  • Brazil 1,502,424
    Confirmed: 1,502,424
    Active: 524,232
    Recovered: 916,147
    Death: 62,045
  • Russia 667,883
    Confirmed: 667,883
    Active: 220,131
    Recovered: 437,893
    Death: 9,859
  • India 633,381
    Confirmed: 633,381
    Active: 231,125
    Recovered: 383,936
    Death: 18,320
  • Spain 297,183
    Confirmed: 297,183
    Active: 268,815
    Recovered: ?
    Death: 28,368
  • Peru 292,004
    Confirmed: 292,004
    Active: 99,862
    Recovered: 182,097
    Death: 10,045
  • Chile 284,541
    Confirmed: 284,541
    Active: 29,374
    Recovered: 249,247
    Death: 5,920
  • UK 283,757
    Confirmed: 283,757
    Active: 239,762
    Recovered: ?
    Death: 43,995
  • Italy 240,961
    Confirmed: 240,961
    Active: 15,060
    Recovered: 191,083
    Death: 34,818
  • Mexico 238,511
    Confirmed: 238,511
    Active: 66,729
    Recovered: 142,593
    Death: 29,189
  • Iran 235,429
    Confirmed: 235,429
    Active: 27,723
    Recovered: 196,446
    Death: 11,260
  • Pakistan 221,896
    Confirmed: 221,896
    Active: 103,722
    Recovered: 113,623
    Death: 4,551
  • Turkey 202,284
    Confirmed: 202,284
    Active: 20,152
    Recovered: 176,965
    Death: 5,167
  • Saudi Arabia 201,801
    Saudi Arabia
    Confirmed: 201,801
    Active: 59,385
    Recovered: 140,614
    Death: 1,802
  • Germany 196,738
    Confirmed: 196,738
    Active: 6,674
    Recovered: 181,000
    Death: 9,064
  • South Africa 168,061
    South Africa
    Confirmed: 168,061
    Active: 83,218
    Recovered: 81,999
    Death: 2,844
  • France 166,378
    Confirmed: 166,378
    Active: 59,701
    Recovered: 76,802
    Death: 29,875
  • Bangladesh 156,391
    Confirmed: 156,391
    Active: 86,375
    Recovered: 68,048
    Death: 1,968
  • Canada 104,772
    Confirmed: 104,772
    Active: 27,783
    Recovered: 68,347
    Death: 8,642
  • China 83,542
    Confirmed: 83,542
    Active: 409
    Recovered: 78,499
    Death: 4,634
  • Netherlands 50,335
    Confirmed: 50,335
    Active: 44,222
    Recovered: ?
    Death: 6,113
  • S. Korea 12,967
    S. Korea
    Confirmed: 12,967
    Active: 926
    Recovered: 11,759
    Death: 282
  • Australia 8,255
    Confirmed: 8,255
    Active: 832
    Recovered: 7,319
    Death: 104
  • New Zealand 1,530
    New Zealand
    Confirmed: 1,530
    Active: 18
    Recovered: 1,490
    Death: 22

How tech giants employ machine learning against cybercriminals

Author at TechGenyz Contributor
Machine Learning

Google and Microsoft are raising everyone’s hopes by perfecting another AI-based application. Only this time, the technology is not being used to tell the weather or play music. It’s being used for something human beings cannot really do themselves - keep cybercriminals under control.

A Different Kind of Use Case

“Machine learning is a very powerful technique for security—it’s dynamic, while rules-based systems are very rigid,” explains Dawn Song from the University of California. With hackers becoming more inventive and adaptable every day, this flexibility is of crucial importance.

Neural Network

But it’s even more important that AI is finding new productive fields of application. Machine learning and its defining capability have been used for data analysis many times before. However, it wasn’t until recently that we’ve started hearing about AI-powered cybersecurity at this scale.  

Information security officers at Google and Microsoft as well as in other industry giants such as Amazon are hopeful that machine learning may finally change things for the better. Cybercrime can hardly be uprooted, but it can be controlled with greater confidence.

Before Machine Learning  

In February 2019, Microsoft was able to retroactively discover a series of attacks on political think tanks from the EU. Security teams were two months late to prevent it, but that’s not the point. There’s now the technology to detect similar attempts on political targets.  

The chances are better than ever before, according to Stephen Schmidt from Amazon. Before AI, the most we could hope for was to avoid phishing scams using our good judgment or to be notified when someone tried to access our bank accounts from a suspicious place.

Not only were these systems insufficient, but they were also inconvenient for the users themselves. Before AI, having our credit cards locked while on vacation meant that our banks’ security teams were cutting-edge. Random geo-blocking was the best we could ask for.

Customized Cybersecurity

This posed a great challenge to modern-day cybersecurity scientists as well. Blocking a user from the system under suspicion of unauthorized access is easy; it’s discerning a user from a hacker that’s hard. AI and machine learning are currently helping tech leaders untie this knot.

That’s how Microsoft’s security team was able to prevent a cyber attack on one of the company’s major clients. An attempted login from Romania instead of the usual New York address raised a red flag. This prompted experts from Azure to block the entrance to their cloud.

But usual login locations are only a small part of greater behavioral patterns tracked by AI. Thanks to machine learning, technology can learn from user data. Besides, it can customize security protocols for each individual user based on their typical online behavior and history.

Inhumane Amounts of Data

Learning to differentiate a legitimate user from hackers requires a security team to generate and analyze a massive amount of raw data. The latter “keeps growing at a rate that is too large for humans to write rules one by one,'' says Mark Risher from Google.

Considering that millions of people log into Gmail every day, and taking into account that this is only one of many of Google’s services, Risher’s observation may actually be an understatement. It’s simply impossible for a human to track every login and monitor every user.


Luckily, no amount of data is too big for machine-learning algorithms to crunch. In Google, AI sorts out data not only on logins and on-site behavior but also on previous cyber attacks. The latter allows it to stay one step ahead of the hackers, no matter how inventive they are.

Improved Internal Control

While Google keeps training algorithms to leverage hackers’ most powerful weapons against them, Microsoft and Amazon are developing AI-powered technology and security protocols for their biggest clients to use internally. So far, the results have been more than satisfying.

For instance, Microsoft’s Advanced Threat Protection helps Dutch insurance company NN Group NV manage access to around 27,000 employees and partners without compromising a bit of data. Over at Amazon, AI-based GuardDuty does the same for all customers’ systems.

Machine learning helps corporations improve internal control, but their security teams are right to worry. Just like failed hacking attempts can be used against cybercriminals, AI tools can be employed by hackers to bypass state-of-the-art security designed to fend them off.

But still, AI is making things much harder for hackers.
Cybercrime won’t be eliminated anytime soon, even though the future is looking much brighter with every new pattern detected and learned. In addition to regular antivirus, users are advised to protect themselves with the fastest VPN solutions just in case, given that nobody’s really safe for now.

We Are Hiring