November 30, 2020

How Machine Learning can Improve Cybersecurity

Our introduction to Machine Learning (click here) Blog

Cybersecurity is the body of technology that is designed to protect devices, networks, data, and programs from unauthorized access or attack. The various IT innovations have caused more businesses to go digital. This has increased the number of security breach cases because cybercriminals are developing better ways to access unauthorized data. In a bid to stop these cyber-attackers, businesses are investing tons of money every year into hiring security professionals. These efforts from businesses cost about $86.4 billion in 2017 alone.

Every day, new malware is being developed, making it imperative for companies to update their security protocols & policies regularly, thereby adding to the cost of security. Of course, one cannot put a price on the importance of data security. Nevertheless, it is smart to adopt methods that are more efficient. The ever-increasing volume of data utilized by businesses has increased the need for more skilled cybersecurity personnel.

Machine learning deals with giving systems the ability to learn and improve from experiences and has found its way into cybersecurity.  ML has provided businesses with more efficient ways to defend against cybercriminals.

Current challenges in cybersecurity

One of the most significant features of cybersecurity is its evolving nature. Both the defenders and the attackers are constantly innovating ways to improve their work. No wonder there are many security products and vendors available today. There are however challenges currently plaguing the cybersecurity industry.

Some of these challenges include:

New Vulnerabilities

New technologies are being developed these days at a much higher rate than what was the case a few years ago. Technologies like cloud servers, AI, data storage, and software tools have proliferated the market. With every innovation coming with potential vulnerabilities exploitable by cybercriminals. Many companies cannot keep up with this ever-changing industry, as it requires continuous need for changes to your security systems.

Human factor

Currently, most companies are starting to employ professionals to help protect their business against hackers. Time is significant with malware. You have a better chance of fighting off malware while it is being downloaded, and since the cybersecurity personnel may have not been on hand every time, it was an uphill battle after the attack has occurred.

Shortage of talent

The cybersecurity industry is currently experiencing a lack of talent, with the number of vacant positions expected to rise to about 200,000 in the US and nearly 2 million around the globe in the next few years. This talent shortage means companies are struggling to hire experts that will help shore up their security systems, contributing to the high vulnerability of businesses to cyber-attacks.

How machine learning can help improve cybersecurity

With more data being collected and transmitted daily, the need for security for these data is also increasing. Machine Learning has only recently been introduced into cybersecurity, so let’s look at how it can improve the industry.

Automating repetitive tasks

Whenever there is an attack on a system, an analyst has to go through a set of procedures & guidelines to determine how the attack happened, what was taken, how it was stolen, and how to fix the network to prevent similar attacks. All these tasks can be carried out using machine learning, thereby considerably reducing the time it takes to fix the problem. Although most algorithms require the human analyst to be notified of any threat, some ML algorithms can detect and deal with the threat on their own.

Saves the cost of security

For companies to keep up with the alarming growth of cybercrime, they spend a considerable amount of money every year on cybersecurity. There were about 8.3 million reported identity theft cases in 2005, a number that rose to approximately 17.6 million in 2014. The amount of money paid by consumers to unlock their computers from ransomware increased from $1 million to $24 million between 2005 and 2015.

As organizations keep struggling to get skilled professionals to handle their security system, the amount spent will continue to increase. Machine learning, however, offers a cheaper solution. By quickly analyzing large sets of data at a time without fatigue, with little supervision, the system will reduce the need to hire multiple security personnel for your systems.

It offers a more efficient solution

Machine learning provides a faster and more efficient solution to cybercrime. In a paper co-written by researchers from PatternEx and MIT, the researchers demonstrated that using an AI platform; you could predict cyber-attacks better than the standard systems available today. The team of researchers illustrated that the AI platform detected 85 percent of the possible attacks, a percentage that is about three times the previous benchmark. Machine learning can provide analysis round the clock, and you can be sure of a lower probability of errors occurring.

It helps prevent zero-day exploits

A zero-day exploit is an advanced cyber-attack that occurs when a cybercriminal takes advantage of a vulnerability in software before the developer releases a patch to fix the flaw. Internet of Things (IoT) devices are the biggest victims because are usually without basic security features. Whenever a cyber-attacker successfully implements the exploit code into the software, it could take months or even years for developers to discover the vulnerability that led to the attack.

Machine learning could help prevent these exploits before they take advantage of the vulnerability. No wonder more developers are incorporating machine learning in their software or web development process.


Like it or not, machine learning has come to stay, and cybersecurity will revolve around it in a few years. Therefore, it is left to you to either join the trend or be left behind by the competition. The current limitations of cyber security such as the high cost of security and the need for regular updates that will match up well with the different techniques employed by cybercriminals increase the demand for machine learning.

As the general influence of machine learning increases in technology generally and the acceptance level increases, so also will its use in the cyber security industry.

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