Hackernoon logoThe Crucial Role of Machine Learning in Cybersecurity by@rituraj15

The Crucial Role of Machine Learning in Cybersecurity

Rituraj Sengar Hacker Noon profile picture

@rituraj15Rituraj Sengar

The Startup Guy with a Digital Marketing head.

In 2019, more than 627 million online records were comprised due to hacking and other types of cyber attacks. This is a pretty staggering number to anyone who has made an online transaction, but the amount of attacks that were stopped is much higher, so it’s worth some optimism. As COVID-19 has pushed many companies into the remote work world, online transactions and records are growing exponentially, and most experts believe that remote work will continue to be very popular even after stay-at-home orders get lifted and life goes back to some form of normal. 

The pros of remote work for businesses are plenty, and for many employees, it is now a requirement when trying to land the best jobs they can find. Costs are lower when employees are remote, employees are generally happier, meaning less employee turnover and the associated time and costs. However, one of the biggest cons is, indeed, increased threats to a business’s cyber security. As the means by which hackers conduct their business continue to evolve, so must the ways that companies protect against them. Today, artificial intelligence and machine learning are two key components in the fight against cyber threats. 

What is Machine Learning?

Image Source: securityinfowatch.com

Most people have heard the term artificial intelligence. Machine learning, in a way, takes it a step further. To use videogames as an example: a gamer can play against the computer, but generally a pre-programmed computer doesn’t start to learn the trends of the gamer, so ultimately the gamer can improve and win against the computer with time. With machine learning, the artificial intelligence (no matter the medium… shying away from video games now) is programmed to recognize trends and “learn” from them, evolving it’s level of intelligence as time goes on. 

A good example of this is when an online order offers you up “other things you might like.” The computer in this scenario has utilized data and algorithms from other purchasers in an attempt to show you other items that similarly minded people have bought. 

Machine Learning in Cybersecurity

Machine learning is, indeed, a two-sided sword, and hackers can (and do) utilize it as well. However, it tends to thrive more when used for defensive means as opposed to offensive ones. With machine learning, regression and prediction are made easier (to be more precise, they are made effortless with the right programming) when it comes to determining projected prices for certain goods. When it comes to cybersecurity, machine learning can determine when suspicious transactions far outside of the predicted scope occur, often signaling some sort of fraudulent action. 

Classification is another sector of machine learning that can make the leap to cybersecurity and be immediately effective. If transactions are made in two different ways, we’ll say A and B, machine learning can flag any sort of transaction that was created outside of those two means, again identifying a potentially fraudulent transaction. 

As mentioned before, machine learning won’t be a cure-all for cyber threats, as it can be utilized by all sides of the scenario, but as the machines do start to recognize what kind of inputs are fraudulent, the technology can simply recognize those inputs in the same way Netflix recognizes that you really like cheesy comedies or horror movies and recommends new ones. “This was fraudulent two years ago and I see it again… is it fraudulent now?” would be the computer’s thinking in such a case. 

Network protection is more about keeping personal information safe, rather than money, but the importance relative to business is almost the same. With so many people working remotely now, network security is more difficult a task than ever, but once again machine learning can help combat the evils that surround it.

As long as your team is well-versed in how to play their own parts in network protection (password security, logging out of networks when in public places, etc.) machine learning can determine when abnormalities strike the network, ultimately preventing fraud the same way it does relative to financial transactions. 

The Future

Machine learning is already a borderline-scary technology, with computers seeming to know more about ourselves than we do on occasion. But like any great technology, if it’s used the right way and the best stay motivated to be on the “good guys’” team, it can and will continue to add to the security of networks and web transactions alike. 

Lead Image Source: SignalProcessingSociety

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