How Machine Learning(ML) can help increase cybersecurity?

The amount of data that is being generated individually and collectively is multiplying rapidly. When it comes to cybersecurity, it's not any different. As a result, this presents a new opportunity to reevaluate IT security and management strategies.

Today, cybersecurity tools and ML have started to work in tandem to help us become more proactive, detective, and reactive. If we look at most of the security breaches in recent years, they have mostly been financially motivated. Most cyber criminals are stealing identities and money daily and they are always coming up with some new ways to do it.

This leaves corporate and personal data extremely vulnerable to network breaches via phishing attacks, malware, ransomware and much more. It also leaves organizations with a never ending struggle to keep up with the speed and quantities of these attacks.

One of the major issues in the industry is the shortage of cyber analysts, configuration alerts, and manual coding to efficiently counter these attacks. As a result, Artificial Intelligence (AI) can be the missing link.

It has also become vital now for the survival of businesses as government watchdogs and security firms name and shame companies that are failing at cybersecurity.

ML grew out of pattern recognition and computational learning to computers that can learn through experience rather than being explicitly programmed. The core of ML lies in programming and data science. So with new learning algorithms rapidly evolving, ML enables cybersecurity apparatuses to scale much faster.

As a result, it's not a surprise that large number of companies are heavily investing in ML. If an algorithm can do what would take humans several hours (or even days) to accomplish, it's more than an attractive solution to automate various business processes.

There aren't enough people with the skills and experiences to process and analyze the data that is being generated. With automated processes focusing on recognizing valuable patterns in security data, companies can focus on hiring only those needed to fill critical positions.

Further, it can also be a highly cost-effective solution as the organization don't have to hire a number of individuals to build teams to analyze the data.

To react to a cybersecurity breach, the company have to first identify it. ML will enable to do this quickly as the algorithms can keep performing statistical analyses to identify abnormalities in the network.

It works by identifying normal vs. unusual behavior. If one has signed up for Gmail's two-step verification, he/she would have experienced this in action. This type of algorithm looks at the following:

Login location
Unusual time
Multiple IP addresses
Security threats
Device failures
Software patches
ISP bandwidth

All of this enormous data together can generate granular industry intelligence. As a result, enterprises can now predict weaknesses in IT security infrastructure and enable rapid reaction.

Malicious software that can restrict access to files or devices until a ransom is paid is activated long before big data analysis is conducted. So, ML can be the key to turning this major problem of profile creation into something small like anomaly detection.

As a result, AI can significantly speed up the process and react to it much quicker. If there is maintenance of a large network, it can be impossible to achieve this even if there are many IT professionals working on it around the clock.

ML is a great advancement in technology, but it's not perfect. AI /ML alone helps in identifying cyberattacks because the system tends to yield loads of false positives. So regardless of the technological advancement, we still require human intervention to make critical decisions on whether an actual breach has taken place.

Although attended learning means that humans will be required in this field, it also means that the problem of trying to keep up with the massive volume of data that needs to be analyzed isn't going away. As a result, there will be a large number of undetected cyberattacks and delayed reactions, even with ML.

With the current shortage of security experts in this field, we can expect the devious minds out there to continue to find new ways to breach even the most secure systems. The only solution here is to try and find the best combination of ML and IT professionals to try and combat the high volumes of security breaches.

But even with AI and an army of IT professionals, there is no magic formula to build a network fortress for enhanced protection. Although ML will help increase cybersecurity, enterprises won't be any closer to being free of cyberattacks and the resulting scandals. But, its better to be equipped with ML algorithms to fight against cyber attacks if arised.

Source: HOB

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