fbpx
8 Ways Machine Learning Can be Used in Cybersecurity 8 Ways Machine Learning Can be Used in Cybersecurity
Cybersecurity has been a concern for businesses since the dawn of the internet, as hackers saw an opportunity arise instantly. Machine... 8 Ways Machine Learning Can be Used in Cybersecurity

Cybersecurity has been a concern for businesses since the dawn of the internet, as hackers saw an opportunity arise instantly. Machine learning has been around for years as well. The two go hand-in-hand, as cybersecurity benefits from the automation of data collection and defense, thus making machine learning a crucial component of any cybersecurity system. There are a few popular ways that you can use machine learning for cybersecurity, such as data protection and threat detection and response.

  1. Collecting data to make informed decisions

The first step before making any new cybersecurity software or process is to collect data, and what better way to do that than with machine learning? This means collecting information that’s not just about threats, but about users, website statistics, network sensors, and so on. By collecting as much data as possible, you can get the full picture of what you need to do moving forward.

  1. Threat detection and response

Cybersecurity is mainly needed due to threat detection in the first place, such as with malware and hackers. By using machine learning, you can set up automated processes to detect potential threats, and set up automated responses to said threats. This would involve creating training data to identify what a threat is in the first place, and once you know what the threats are like, you can develop appropriate responses.

https://odsc.com/california/#register

  1. Application security

This is a little more complicated than just threat detection. Application security means creating unique processes for each application that you may have, such as a website, an actual application, software, etc. Defending a website is different from defending software, thus different data would be required.

  1. Learning about users and user behavior modeling

Users define what kind of data is being transmitted, and may possibly be the reason why threats appear in the first place. For example, should you not require advanced passwords for your application, then users may be easily targeted. By using machine learning for cybersecurity, you can automate the data you collect from users, learn about their general behavior, and even learn if they may possibly by attempting a user or insider hack.

  1. Phishing and email spam filters

Spam is really annoying, and sometimes it’s hard to detect. We may see something from our bank, Amazon, Apple, or another site that we currently use, and wonder if it’s actually them. By using machine learning and NLP, we can now detect phishing emails and filter out spam better than ever, keeping us safe and preventing phishers from attempting again.

  1. Preventing data leaks

Data leakage is when the person creating a machine learning model uses data outside of what was supposed to be used. This could mean a model may be created with user data, such as passwords, addresses, full names, and so on, making them more vulnerable to detection and theft.

  1. Fraud detection

Fraud is one of the most common forms of attack that we see. Every day it seems like another business is hit with one of these attacks, releasing user information to the attackers. By using machine learning for cybersecurity, you can quickly identify potential fraud patterns, stopping the attacks before they even happen.

  1. Developing antivirus software

Most of us (hopefully!) use antivirus software on our computers. This software automatically detects potential viruses that you may have accidentally obtained in your internet travels. This software can isolate and remove these viruses from your computer and can identify potentially malicious web pages before you visit them.

Learn more about machine learning and cybersecurity at ODSC West 2021

The above examples are just a few ways that you can use machine learning for cybersecurity, as there are new methods being developed every day. By attending ODSC West 2021 and checking out the AI for Cybersecurity focus area, you can learn about these new techniques and how you can protect yourself and your business from threats.

While the session titles are still in the works, some speakers who will represent this focus area include:

  • Charles Givre | Co-founder / Adjunct Professor / Apache Drill Project Management Committee Chair | Stealth Startup / GTK Cyber / University of Maryland
  • Rob Lee | Founder and CEO / Senior Instructor | Dragos, Inc. / SANS Institute
  • Christopher Crowley | Senior SANS Instructor | SANS Institute
  • Jess Garcia | CEO, Security & Forensics Analyst, Incident Responder / Senior Instructor | One eSecurity / SANS Institute
  • Jeff Troy | President, CEO | Aviation ISAC (A-ISAC)

Alex Landa, ODSC

Alex drinks a lot of coffee and sometimes writes stuff. Connect with him on LinkedIn: https://www.linkedin.com/in/alexander-landa-a0889857/

1