Machine Learning in Cyber Security

The best practices of cyber security are constantly evolving to keep pace with new cyber threats. The faster cyber security companies can shore up system vulnerabilities, the better the protection.

Machine learning is poised to be the next great leap in computing. This will empower systems to adapt the programming of their applications to operate more efficiently. By leveraging the applications of machine learning in cyber security, companies can better respond to evolving threats.

About Machine Learning

Machine learning, simply defined, is the ability of computer systems to perform a task without using instructions, relying on patterns and inference instead.

At its most fundamental level, machine learning can help businesses better analyze threats and respond to attacks. It also helps small and medium-sized businesses with limited IT personnel automate tasks that can be reliably done without human input, or when time is a critical factor. This means that with machine learning, cyber security applications can identify and respond to threats automatically, and far more quickly than any human monitoring the situation.

There are several ways in which machine learning can help your business become more secure.

machine learning against hacker

Top Applications of Machine Learning In Cyber Security

Machine learning models are being embedded into several aspects of the cyber security landscape. The following network security applications are introducing new machine learning methods to better protect a company’s critical infrastructure.

  1. Intrusion Detection and Prevention – Systems that detect malicious activities across a company’s network, cloud, or mobile vector can be supplemented with machine learning models. Traditionally known signatures will detect abnormal network activities and stop intruders from accessing the system. New deep learning, convolutional neural networks (CNN) and Recurrent Neural Networks (RNN) are being used to analyze traffic with greater accuracy. This reduces the number of false alerts from abnormalities, which provides relief for the workload for the security team. Solutions including next-generation firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA) are being updated with machine learning models for the latest cyber security detection and prevention.
  2. User Behavior Analytics – While it is essential to detect and prevent attacks coming from outsiders, it is also important to track and analyze activities from users within organizations.
    algorithms used in machine learning

    Overseeing these activities can be even more challenging than monitoring traditional malicious activities against networks since employees can bypass security features which may not raise any flags and alerts. By using machine learning and cyber security modeling, companies can evaluate normal behavioral patterns and raise flags from suspicious activities. User and Entity Behavior Analytics (UEBA) is a growing area of interest for larger companies where it can be difficult to monitor suspicious behaviors from users that have different levels of IT access.

  3. Defense Against Malware – Traditional methods to detect malware attacks such as firewalls use signature-based systems. These systems run attacks against a database of existing threats that needed to be manually updated to recognize new threats. While this technique is useful for defense against attacks that have been documented, they are less successful when dealing with more advanced threats, and those that may be newer to the landscape. Cyber security methods using deep machine learning algorithms are able to detect these more advanced threats. Deep learning models can learn abnormal activities that may indicate malware or other threatening software. These models do not need to rely on known signatures, rather suggest growing risks across an organization’s connected infrastructure and devices.
  4. Analyze Network Traffic – Deep learning Artificial Neural Networks (ANN) are an emerging type of machine learning in cyber security targeting several applications. ANNs use a learning technique to perform tasks by considering examples rather than being pre-programmed with specific algorithms and rulesets. In the cyber security space, ANNs are particularly successful in analyzing HTTPS network traffic to identify malicious activities. When dealing with SQL injections and DOS attacks, ANNs can seek abnormal spikes in network traffic, suggesting an anomaly or potential infection.
  5. Spam and Social Engineering Detection – In addition to ANNs other techniques of machine learning for cyber security include Natural Language Processing (NLP). NLP is a field in machine learning that focuses on the ability of a computer to understand, analyze, manipulate, and generate human language. When dealing with cyber security threats, NLP can help your organization more easily detect spam and other forms of social engineering. NLP learns normal forms of communication and language patterns in email, messaging, and other forms of communication platforms, and uses various statistical models to block spam. Organizations that use Gmail or Outlook to send and receive emails can use NLP technologies to reduce the risk of phishing and other forms of attacks that use social engineering to infiltrate your company’s infrastructure.

The number of new and existing machine learning techniques including ANNs, NLPS, RNNs, and CNNs can be overwhelming to even the largest organizations with vast expertise in cyber security. NIC’s team of network security specialists are there to support your team and simplify the complexity of these emerging technologies.

The Future of Machine Learning in Cyber Security

securing on premise servers

Cyber security is a growing function of every company’s IT and digital strategy. Although these strategies allow companies to develop new data-based products and serve new customers, they have also created new cyber security vulnerabilities. Evolving technologies such as machine learning in applications of cyber security applications ensure that companies are more secure from outside intruders and harmful inside actors. While most machine learning methods are early in development, several technologies such as NLP are being used in many aspects of enterprise applications and cyber security tools today. NIC helps secure all vectors of a company’s networked assets including cloud, on-premise, mobile, and IoT vectors.

Securing Your Business with NIC

Securing your company’s data system is a vital aspect of any company’s IT and digital strategy. NIC’s best practices for cyber security provide a framework for your company to detect and prevent malicious activity from impacting your company’s operations.

For more information on cyber security machine learning tools, schedule a free consultation with one of our cyber security experts. You can also reach us by email at, or call us at 1-877-721-3330.

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