Stay on Top of the Trends: Machine Learning and Cybersecurity | By Ester Brierley

Stay on Top of the Trends: Machine Learning and Cybersecurity

We all live in a digital world that changes and develops at a rapid pace, all the time. Thanks to digitalization, many processes have become faster than ever, changing the way we work, introducing new products, trends, and new types of entertainment. People are getting used to getting everything they want instantly. However, our digital era has also introduced numerous challenges, and one of the biggest problems is private data protection. Cyberattacks make people lose their information, money, and even identities.

In 2017, more than 200,000 computers in 150 countries became victims of WannaCry. This ransomware made files on a computer unreadable by using encryption. Victims were forced to buy decryption software from the attackers. This virus affected not only individuals but also FedEx, the U.K. National Health Service, Russian banks, and Chinese schools. In 2018, the number of malware attacks exceeded 10.5 billion. While some cybercriminals attack personal data, others may just use your PC’s computing power to mine cryptocurrency, which increases electricity consumption and causes overheating problems.

Information Security and Machine Learning

Fortunately, technology also gives us new tools to fight cybercrime, and one of the most revolutionary tools is machine learning. Machine learning is a subset of artificial intelligence that focuses on the use of complex algorithms that enable machines to learn from massive amounts of data. For instance, machine learning enables computers to deal with natural language processing or facial recognition. The fast computation speed and an unbiased approach make machine learning a great tool for fighting crime. For instance, facial recognition can help fight money laundering and human trafficking.

As for cybersecurity, artificial intelligence and machine learning offers even more benefits, especially considering the fact that cybercriminals also start to use such technologies in their attacks. Although AI-based solutions still cannot protect computers without any help from humans, they can simplify numerous tasks associated with cybersecurity. Here are just a few benefits of machine learning:

  • AI-powered programs can classify data depending on preset parameters.
  • If some data doesn’t fit the predetermined parameters, AI can group it based on anomalies and similar patterns.
  • Software that uses machine learning algorithms can also work with generative frameworks. These programs can generate possibilities based on data inputs. It also becomes possible to obtain forecasts based on previous data sets and choices because such software can learn from previous decisions and outcomes.

The Role of Machine Learning in Information Security

  • Anomaly detection
    Machine learning algorithms can analyze data on millions of events that occur during a day and select the events that need to be analyzed by a security analyst. However, not all anomalies are security threats. Machine learning algorithms can remember unusual events that were considered safe so that they won’t trigger an alert in the future.
  • Fighting ransomware
    Ransomware encrypts and blocks files on a computer or website so that the user or organization can only access them after paying a ransom. Machine learning enables AI to detect dangerous micro-events and fight ransomware as soon as it starts to encrypt files.
  • Phishing detection
    Phishing is another common method of cyberattacks. Most often, phishing attacks involve emails: one in every 99 emails contains phishing threats. Machine learning can track thousands of active phishing sources and react much quicker than human specialists. Besides, machine learning solutions are not limited to a particular geographical area, being able to analyze threats from all over the world.
  • Authentication and password management
    When it comes to cybersecurity, passwords are one of the weakest points. Quite often, a password is the only thing that protects a user from criminals. Effective passwords must be very long and have complex combinations of letters and numbers, which makes them hard to remember. Besides, they need to be updated regularly. However, most users choose simple passwords and use the same passwords on different platforms, being an easy target. Although biometric authentication is more reliable, it still can be hacked, while facial recognition technologies are still far from perfect.
    Machine learning can improve facial recognition and biometric authentication, using complicated algorithms that can recognize the right face even if the user changes hairstyle or wears a hat.
  • Predicting threats
    Given that machine learning enables AI to learn from historical data, such solutions can identify a security threat even before it uses a vulnerability in your system. Machine learning serves as the basis for adaptive algorithms that quickly analyze the data received and can suggest the necessary actions. This way, computers can predict possible threats, which is a very difficult task for humans, given the volume of cyber threats that occur every day.

Conclusion

The world of cybersecurity is changing all the time. New technologies appear every day and hackers never miss out on an opportunity to use them against individuals and organizations. Although it’s still virtually impossible to protect computers from all kinds of cyberattacks, machine learning offers a considerable advantage because it allows computers to quickly adapt to the new threats.

Thanks to machine learning, artificial intelligence can work with massive amounts of data and learn from previous experiences. It can detect suspicious activity at early stages and even predict threats, offering suggestions on how to avoid them.

About the Author
Ester Brierley is a talented QA Engineer in a software outsourcing company at full-time employment, but freelancing as a virtual assistant and a creative content creator for College-Writers. Find her on Twitter.

November 22, 2019

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