Next-Generation Antivirus (NGAV) is the next generation antivirus software that integrates a variety of techniques to protect customers’ data. It uses behavioral analytics, machine learning, and more traditional methods of analysis to detect potential threats and make sure that you never feel unsafe on your computer.
NGAV uses the latest deep learning techniques to detect zero-day threats that have never been seen before, as well as more sophisticated attacks such as ransomware. NGAV uses artificial intelligence and machine learning techniques to identify and neutralize threats like zero-day malware, ransomware, exploits, and malicious websites.
In this article, we’ll take a look at how sophisticated technology has enabled next-generation antivirus programs to protect your computers against the latest and most dangerous threats out there.
The Power of AI in the Antivirus Industry
Traditional antivirus software has long relied on heuristic analysis, which means it examines the code a program will execute and compares it against a database of known harmful samples.
This approach has been effective against malware that only targets one machine and only executes on the machine to infect it, but it can be slow, laborious, and can provide a lot of false positives when it comes to detecting malware that can easily bypass traditional defenses.
Typically, the databases of known harmful samples used by AV companies are collected from file sample uploads by product users, which means that a user had to become infected with the virus and a report sent to the company for analysis. This process was sped along with cloud database technologies, but still, heuristic analysis could never really detect zero-day threats that have never been seen before.
Enter artificial intelligence and machine learning.
Thanks to AI, next-generation antivirus is able to analyze massive amounts of data and detect extremely subtle and highly dangerous malware. Artificial intelligence accomplishes this by analyzing complex sequences of digital information to identify patterns that match specific characteristics of an attack.
For example, a pattern of test file executions (a series of 5 to 10 test files) for malware may give malware a warning for being malicious, but not for being part of a script. A pattern of file size distributions can signal that a threat has been obfuscated, which renders traditional antivirus software useless.
AI is able to detect these malicious patterns by using sophisticated algorithms that can recognize patterns in massive data sets.
Cloud-Based Endpoint Analysis
Another technology in the cybersecurity sector, known as endpoint detection and response (EDR), analyzes information from a range of network components and proxies to identify suspicious activity and provides notifications when suspicious activity is detected.
While network security solutions and antivirus software were often separate packages, next-generation antivirus software is able to combine these technologies to speed up analysis. Instead of only looking at file uploads, analysts can now monitor network traffic and explore endpoint devices for signs of activity.
In addition, cloud-based endpoint monitoring allows for real-time analysis of endpoint devices. Instead of waiting for a trigger alert, the endpoint detection and response solution can analyze data and search for a pattern that leads to malicious activity, while still providing protection for businesses.
Previously Undetected Threat Detection
The introduction of machine learning has allowed AV companies to identify previously unknown threats before they cause damage, which means that companies will now be able to detect zero-day threats without having to rely on file samples uploaded by users. This drastically decreases the amount of work required from the AV provider.
Antivirus solutions powered by machine learning have been shown to be more efficient than traditional antivirus solutions. By reviewing the data collected, machine learning AV solutions can develop new insights into malware threats that traditional antivirus products have never seen before. This means that cybercriminals will have a much harder time developing and delivering new malware threats.
