Top Machine Learning Use Cases For Cybersecurity

Machine learning is one concept that has, in the recent past, greatly invaded the cybersecurity domain and is changing it for the better. In today’s context, it would be impossible to deploy effective cybersecurity technologies without relying on machine learning. Machine learning reduces the amount of time spent on routine tasks and helps organizations use their resources strategically. Machine learning makes cybersecurity simpler, less expensive, more proactive, and far more effective.

Let us now discuss how machine learning is applied in the cybersecurity sphere. We will start by understanding the two terms, ‘cybersecurity‘ and ‘machine learning.’

What is Cybersecurity?

Cybersecurity involves protecting inter-connected systems like hardware, software, electronic data, etc. The main purpose of cybersecurity is to prevent data breaches, identity theft, and cyberattacks which can help in risk management. If an organization has a strong sense of network security and an effective incident response plan, it will be easier for it to prevent and mitigate cyberattacks.

What is Machine Learning?

Machine learning refers to machines being able to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.

Challenges of the Cybersecurity Domain

Though machine learning is helping the field of cybersecurity prosper, it still has the following challenges to overcome.

  • Anomaly detection is challenging to define as it needs a clear definition of what is considered the normal activity.


  • The methods and tactics of cyberattacks constantly change. Due to this, models must quickly be able to adapt to new patterns and behavior.


  • False positives can be costly with respect to data privacy and infrastructure.


  • Attackers use machine learning methods to power their attacks by creating new malware, phishing content, possible flags, self-protection of infected nodes, and identifying recurring patterns.


Machine Learning Use Cases For Cybersecurity

Some of the ways in which machine learning improves cybersecurity are:

1. Risk Detection

Machine learning is used to analyze, monitor, and respond to cyberattacks and security incidents on:

  • Hardware.
  • Software.
  • Applications.
  • Networks.

Machine learning can act as the foundation stone for your cybersecurity framework by assisting in the protection, detection, identification, response, and discovery of cybercrime. SparkCognition, the Austin-based AI company, has partnered with Google Cloud Machine Learning Engine to prevent endpoint attacks and detect security threats early. As stated by Google, the engine can detect zero-day threats with an accuracy of 99.5%.

2. Malware Detection

Malware refers to one that is designed to damage or infiltrate a computer system. A traditional approach to malware detection focused on identifying features using hashes, file properties, and code fragments. Algorithmic rules are created from these to classify a file as malware or benign. One of the major challenges of malware detection is the continuous evolution of malware files and versions. Rule-based approaches will not be able to adapt to these changes. Machine learning is used to detect ransomware by analyzing files during the pre-execution phase. Another challenge faced is detecting rare attacks like high-profile targeted attacks. Nowadays, deep learning algorithms are also used to detect these types of attacks. These algorithms will continue to become an asset in malware detection in the future.

3. Phishing Detection

Phishing refers to stealing personally identifiable information such as account details, passwords, intellectual property, credit card data, and financial information. Phishing uses social engineering and technology to lure users into sharing sensitive and personal data. The common types of phishing attacks are website cloning, voice and text phishing, and deceptive linking. The three main groups of anti-phishing methods are:

  • Preventive (patch and change management, authentication).
  • Detective (content filtering, anti-spam, and monitoring).
  • Corrective (forensics, site takedown).

Using machine learning algorithms helps us detect websites or phishing emails from non-phishing ones by processing, extracting, and analyzing data from features such as number of links, HTML email formats, domain name, age, number of domains, JavaScript presence and form tags.

4. Spam Detection

Machine learning greatly improves cybersecurity through spam detection. A large portion of spam attempts is blocked from reaching inboxes thanks to the robust machine learning-powered spam filters. Machine learning methods offer more scalability and efficiency than knowledge-based methods. There are different approaches to spam detection. You can classify emails as spam based on a finite set of rules that are inflexible, not scalable, and costly or you can use the machine learning techniques.


Machine learning, in short, helps businesses better analyze threats and respond to security incidents and attacks. Using machine learning in cybersecurity is a fast-growing trend as businesses across several industries worldwide are using to help smoothen their business processes. Thanks to machine learning, many companies have shifted from a signature-based system to a machine learning system.

To enrol in machine learning certifications and become a machine learning expert, check out Global Tech Council.