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Artificial Intelligence in Trend Micro Maximum Security

Machine learning is a security tool that helps prevent unknown threats. Pre-execution machine learning analyzes static file features while run-time machine learning catches processes that exhibit malicious behavior.

Trend Micro has used machine learning since 2005 and is a leader in detection of BEC attacks with writing style DNA and other tools. Its Vision One platform recently added generative AI with Companion.

Predictive Machine Learning Engine

Powered by the Trend Micro Smart Protection Network, the machine learning engine detects unknown threats with minimal impact on network performance. It identifies malware through digital DNA fingerprinting, API function identification and other analysis methods. The engine also uses behavioral malware modeling to determine the probable threat type.

In our tests, the product wiped out a good chunk of samples but was less effective in blocking downloads. However, it did a good job of flagging suspicious URLs and warning users to be careful when clicking links.

Mobile security includes multi-layered protection against malware, data leakage and phishing attacks on Android and iOS devices. It can also detect repacked apps and is able to capture device activity logs, enabling real-time detection of potential threats.

It also provides family control tools such as parental controls, which can set internet access times and enforce device usage limits. Its centralized management of multiple devices is particularly useful for businesses with remote employees. Trend Micro also offers 24/7 phone support for its premium customers.

Deep Learning Engine

Trend Micro Maximum Security uses a combination of machine learning and traditional security techniques to protect systems. Employing these multiple layers of defense helps to keep false positive rates low, and allows the most suspicious files to be investigated more thoroughly.

This includes detection of unknown threats using digital DNA fingerprinting, API mapping, and other file features. Combined with the malware modeling engine, it provides constant unknown threat protection against portable executable files on your computer.

The product also utilizes machine learning to detect web reputation risk by examining websites for patterns of malicious behavior and comparing them with data from other Trend Micro customers’ systems. This feature has been part of the product since 2005, well before machine learning became a big cybersecurity buzzword.

The product’s interface has a friendly look, with large icons, a big scan button, and clear descriptions of your security status. You can even customize the console with a new background image.

Generative Artificial Intelligence

The more generative AI can interpret and sift through large amounts of data, the faster it can identify potential threats. This allows SOC analysts to prioritize risks quickly and reduce mean-time-to-detection (MTTD), according to Trend Micro.

Generative AI can also de-obfuscate malicious script, enabling the software to break it down and determine what its intent is—which helps white hats detect threats more easily. It can also help shorten the threat hunting learning curve, which could help free up senior security professionals to focus on more challenging issues and make a bigger impact on the overall cybersecurity posture of the organization.

Unlike ML and simpler forms of AI, which work with predefined algorithms fed into them, generative AI is more creative. This gives bad actors a more compelling attack tool that can create new attacks and evolve to resist detection, Trend Micro notes. The company has integrated generative AI into its flagship Vision One platform with the emergence of Companion, an AI assistant that’s designed to amplify security operations, improve accessibility and efficiency, and quicken threat-hunting speeds for analysts of all skill levels.

Natural Language Processing

The vast amount of collected unstructured data is formatted as text-based documents like emails and transcribed speech. Natural language processing translates this data into usable information for computational systems such as artificial intelligence to discern and categorize. This reduces the time and effort it takes for humans to analyze a large number of documents or gigabytes of sound.

A big challenge for natural language processing is the ambiguity in human language. AI researchers are working on this problem to make it easier for machines to understand what is being said. This is a critical element of AI that could radically transform business and society.

Text-to-speech apps and smart speakers like Amazon Echo are a couple of examples of natural language processing in action. Virus-blocking tools also use this technology to detect anomalies in writing styles. This helps to prevent phishing, business email compromise (BEC) and other threats from being detected by anti-virus programs. Trend Micro’s Writing Style DNA technology, for example, learns “normal” email writing and flags when an attacker’s message deviates from this pattern.

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