Artificial intelligence, Technology

The growing role of Machine Learning in Cybersecurity

The technological intervention has certainly opened a gateway to growth for organizations. It has increased their productivity and enabled them to reach...

The technological intervention has certainly opened a gateway to growth for organizations. It has increased their productivity and enabled them to reach out to the target audience. But, the world wide web is vulnerable and prone to data threats. In recent years, we have seen a rise in data breach attempts. The number of cybercrimes has increased from 1 per hour in 2001 to 97 per hour (at the time of writing). Hence it becomes imperative for organizations to focus on enhancing cyber security. Does machine learning offer a solution to this? Let’s explore the future of machine learning in cybersecurity.

What is machine learning?

Machine learning is a sub-division of artificial intelligence. It focuses on using data to make the machine imitate human intelligence. Machine learning finds several applications, focusing on making the processes more effective and error-free.

Machine Learning in Cybersecurity

Key statistics

  • In 2021, cyber-attacks increased by 125% globally.
  • Around 20 million cyber breach attempts have been made in the first three quarters of 2022
  • As per Forbes, around 61% of organizations claim that without AI and ML, they cannot perform intrusion detection, so they have to focus on implementing this technology.
  • AI has a faster response to cybersecurity.

Exploring more on how machine learning can enhance cybersecurity

As mentioned above, machine learning works by making the machine more intelligent. For this, they rely on mystical analysis of data. With this insight, machine learning engineers bolster the cybersecurity infrastructure.

When it comes to the application of Machine learning in the field of cyber security, it has incredible benefits to offer. From detecting potential threats to the vulnerabilities of the system and mending it, machine learning applications and use cases are innumerable. This is because machine learning can analyze large volumes of data and identify patterns which helps in decoding the pattern of the data breaches.

Moreover, the machine learning system can also learn and improvise over a period of time, thereby preventing future security threats. The role of a machine learning engineer has become significant here. They are responsible for creating systems and algorithms which are more advanced and well-equipped to detect potential threats as and when it is exposed to data.

For example:

Advanced Persistent Threats (APTs)- AI and machine learning techniques are powerful in detecting malicious insiders and attacks like an advanced persistent threat.

User and Entity Behavioral Analytics (UEBA) – This technique can help detect animal activities in the system.

Network Detection and Response (NDR)– In this, the AI and ML algorithm can help monitor traffic and determine if there are malicious activities.

As mentioned above, machine learning is a subset of artificial intelligence. It uses algorithms derived from the previous data sets and helps the computer analyze the malicious attempt. With the help of powerful algorithms, the computer can adjust its action, and so machine learning becomes crucial as such to cyber security.

Top 5 benefits of machine learning in cybersecurity

The growth of machine learning is inevitable, and its application is also growing. There are several ways machine learning algorithms can help in enhancing cyber security systems. A few of the benefits are enlisted below:

1. Identifying and blocking potential security threats-

One of the primary applications of machine learning in cyber security is to identify potential threats. Organizations fail to check the preliminary attempts, and then it turns out to be a massive blow to the entire cyber security infrastructure. With the machine learning algorithm, computers can be programmed to detect unusual logins or unusual network traffic. These patterns will help the system identify the potential threat and block them at the initial stages.

Also Read: Programming languages for artificial intelligence machine learning

2. Unfolding system vulnerabilities-

Another application of machine learning in cyber security is unfolding the system’s vulnerabilities. And this is a preventive measure that organization needs to adopt such that they can reduce the breaching attempts in the system. With penetration testing techniques, machine learning engineers can locate the weak areas of the network and the firewall system. This task can be executed by applying software patches, fixing the code, and other measures, thereby reducing the system’s penetration probability.

Your machine learning also has the feature of analyzing historical data. With this, it can comprehend malicious user behavior and thus enhances the system’s security by halting such attempts.

3. Detecting and responding to malware

Machine learning also analyzes the behavior of the software. Since technology is advancing and organization is now dependent on several software and tools, it becomes important for them to use only the best software. Such software also brings in malware and viruses that can impact the system. With machine learning, it becomes easier to identify such malware and

4. Time and cost-saving

Updating the data security system, completing the penetration testing, and monitoring all the devices to determine any loopholes are time-consuming. While every organization has a dedicated IT team to take charge of the same but having an automated machine learning algorithm takes up the task, it is the workload.

Moreover, with these algorithms, it becomes easier to monitor a multitude of changes in one go. These algorithms are flawless and error-free. However, he will supervision is always an added benefit. The organization can cut down on cost and time by applying such software.

5. Protecting against DDoS attacks

A machine learning algorithm analyzes the network traffic and identifies unusual patterns that can potentially threaten the system. These are indicative of a distributed denial of services. With this, appropriate action is halted in the initial stages, thereby preventing it from disturbing the entire network.

These are some of the key benefits of machine learning in cyber security. Owing to this, there has been a constant rise in demand for cybersecurity professionals. The machine learning algorithms are based on data. Having an understanding of data and knowing the right tools will help you in better implementation of this technology.

Because of this, there has been a constant rise in demand for Data Science for working professionals. You can find several online course providers that will help you learn more about data science and its applications.

Wrapping it up !!!

As the world digitizes, adopting the right security measures is paramount. Machine learning and artificial intelligence play a significant role in this.


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Written by Pickl AI
With its extensive range of Data Science courses and Job Guarantee Program, Pickl.AI is the platform where you can pursue your dream to become a Data Scientist.

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