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How Data Science And Machine Learning Works To Counter Cyber Attacks

We are all aware of the heinous cyber-attack that took down more than 200,000 systems in 150 countries in only a few days in May 2017. This was found by the National Security Agency (NSA) and was nicknamed "WannaCry," which exploited a vulnerability and stole important resources before being distributed online. 

After successfully accessing the computer, it encrypted the machine's contents and rendered them unreadable. Now, victims of the assault were informed they needed to acquire special decryption software to retrieve their stolen material. Furthermore, the attackers marketed this software.

This ransomware outbreak targeted both people and big organizations, including the United Kingdom's National Health Business, Russian banks, Chinese schools, the Spanish telecommunications company Telefonica, and the US-based transportation service FedEx. 

The overall losses were estimated at $4 billion. Other forms of cyber intrusions, such as crypto jacking, which are more subtle and less destructive but costly, are on the rise. Even high-profile firms with sophisticated cybersecurity processes are vulnerable. 

A recent panic at Tesla in 2018 was averted owing to a diligent third-party team of cybersecurity specialists. As a result, there were over 11 billion malware infections in 2018. That is a major problem that cannot be solved solely by humans.

Fortunately, this is where machine learning may come in handy.

How Machine Learning Helps to Boost Cybersecurity? 

Machine learning is a subset of artificial intelligence that makes assumptions about a computer's behavior by using algorithms from prior datasets and statistical analysis. It allows the computer to modify its operations and even execute functions for which it was not expressly intended. Thus, the role of ML and AI in cybersecurity has been increasing. 

Machine learning is increasing in popularity to detect risks and automatically eliminate them before they can wreak mayhem. It can filter through millions of files and detect potentially dangerous ones. This was accomplished by Microsoft's software in early 2018.

According to the firm, hackers utilized Trojan spyware to infiltrate hundreds of thousands of systems and run rogue cryptocurrency miners. Microsoft's Windows Defender, a software that utilizes many layers of machine learning to identify and block potential threats, effectively blocked this attack. 

As a result, the business was able to shut off the crypto miners as soon as they began digging. Machine learning is used to search for network vulnerabilities and automate actions, in addition to detecting early threats. Machine learning excels at some tasks, such as swiftly scanning vast volumes of data and evaluating it with statistics. Cybersecurity systems create massive amounts of data, so it's no surprise that this technology is so beneficial. As a result, in the domain of cybersecurity, this is proving to be a big benefit.

Microsoft, Chronicle, Splunk, Sqrrl, BlackBerry, Demisto, and other big corporations are utilizing machine learning to strengthen their cybersecurity systems.

How Modern Data Science Powered by AI Identifies and FIxed IT Vulnerabilities

Here is how data science helps identify and resolve IT vulnerabilities:

1- Improve the Usage of Technologies

Modern Data Science has the potential to both improve and simplify the usage of such technologies. A machine-learning algorithm may be fed both current and historical data through data science. So that the system can detect possible problems accurately over time.

This allows the system to be more precise since it can predict assaults and identify potential vulnerabilities. 

2- Use Encryption

A data breach or assault can cause severe damage to your organization in terms of the loss of important data and information.

This is where data science comes in handy since it uses very sophisticated signatures or encryption to prevent anyone from delving into a dataset. 

3- Create Protocols

Data science has the potential to create impenetrable protocols. By examining the history of your cyber-attacks, you may create algorithms to detect the most often targeted pieces of data. Data science programs may assist you in harnessing the potential of data science to empower networks powered by self-improving algorithms.

Why Should Companies Hire Qualified Professionals?

Thus, the above points indicate the importance of data science and qualified data science professionals in your firm. Focus on hiring professionals who have a master’s degree in engineering in data science and the knowledge of how to decode big data.

We have access to a massive amount of data, and the data is typically telling a narrative. You should be able to identify deviations from the norm if you understand how to analyze data. and such variations can occasionally signal a threat. And, owing to the usage and advancements achieved in machine learning, dangers may now be appropriately countered in a wide range of industries. It is used for image recognition and speech recognition applications.

Even though cybersecurity has improved as a result of this process, humans remain critical. Some individuals believe that you can learn everything from data, but this is just not true. An over-reliance on AI might lead to a false sense of security. 

However, without a doubt, artificial intelligence will become increasingly widespread in maintaining security. It's maturing, and it's a feature, not a business. It will play a part in resolving a certain issue. 

Final Thoughts

However, AI cannot address every problem. It will be a tool in the toolbox. At the end of the day, humans are the overlords. 

As a result, in addition to carefully deployed algorithms, cybersecurity specialists, data scientists, and psychologists will play an important role. Human efforts, like those of all existing artificial intelligence and machine learning supplements, augment rather than replace them.

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Tags: attacks, cyber, cybersecurity, data, dsc_security, learning, machine, science

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