A security analyst faces a digital virus, symbolizing automated malware analysis.

Decoding Malware: How AI Simplifies Threat Analysis for Everyone

Lena Voss in Tech & Innovation August 2025 • 3 min read.

"Automated malware analysis is here, making cybersecurity more accessible and efficient for analysts of all skill levels."

In today's digital landscape, malware poses a persistent and evolving threat. The challenge for cybersecurity professionals lies not only in detecting these malicious programs but also in understanding their behavior and impact quickly and accurately. Traditionally, malware analysis has been a time-consuming and complex task, requiring specialized expertise and resources. Security analysts often grapple with massive amounts of data generated by dynamic analysis tools, such as sandboxes, making it difficult to extract meaningful insights efficiently.

Recognizing this challenge, researchers have been exploring ways to automate and streamline the malware analysis process. A promising approach involves leveraging artificial intelligence (AI) to generate human-readable reports that summarize the key findings from sandbox logs and other sources. This technology aims to bridge the gap between raw technical data and actionable intelligence, empowering security analysts to make informed decisions and respond effectively to threats.

One such AI-driven system, known as AMAR-Generator, represents a significant step forward in automated malware analysis. By employing techniques like template matching, API behavior mapping, and malicious behavior databases, AMAR-Generator can produce concise, easy-to-understand reports that describe the malicious activities of malware programs. This innovative approach promises to democratize malware analysis, making it more accessible to a wider range of cybersecurity professionals.

The Power of Automated Analysis

A security analyst faces a digital virus, symbolizing automated malware analysis.

The core strength of AMAR-Generator lies in its ability to interpret and synthesize information from diverse sources. The system leverages vendor reports, which are typically written in natural language, and connects them with the detailed technical data found in sandbox logs. This process involves:

Template Matching: Abstract expressions in vendor reports are replaced using template matching, creating structured templates for a malware behavior database.

API Behavior Mapping: API calls and value names from sandbox logs are extracted using an API Behavior Map.
Behavior Correlation: The malware behavior database confirms whether API call values from sandbox logs are malicious.
Report Generation: Concise, human-readable reports are produced based on matches in the malware behavior database, using descriptions from vendor reports.

This automated interpretation not only saves time but also enhances the accuracy and consistency of malware analysis. By drawing upon the expertise of security professionals and codifying it into a machine-learning system, AMAR-Generator can provide valuable insights even when dealing with previously unseen malware variants. Experimental results have demonstrated high detection rates of malicious behaviors, making it a reliable tool for threat assessment.

Looking Ahead

While AMAR-Generator represents a significant advancement, there are ongoing efforts to enhance its capabilities. Future research will focus on expanding the types of malicious behaviors the system can detect, improving the accuracy of interpretation, and refining the report generation process. The ultimate goal is to create a comprehensive and user-friendly tool that empowers cybersecurity professionals to stay ahead of evolving malware threats. As AI continues to mature, automated malware analysis will become an increasingly indispensable component of effective cybersecurity strategies.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1587/transinf.2017icp0011, Alternate LINK

Title: Automatically Generating Malware Analysis Reports Using Sandbox Logs

Subject: Artificial Intelligence

Journal: IEICE Transactions on Information and Systems

Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)

Authors: Bo Sun, Akinori Fujino, Tatsuya Mori, Tao Ban, Takeshi Takahashi, Daisuke Inoue

Published: 2018-11-01

Everything You Need To Know

How does AMAR-Generator simplify malware analysis for cybersecurity professionals?

AMAR-Generator simplifies malware analysis by using AI to generate human-readable reports from sandbox logs and vendor reports. It uses techniques like template matching, API behavior mapping, and malicious behavior databases to identify and describe malicious activities in a clear, concise manner. This automated approach enables security analysts to quickly understand and respond to threats, regardless of their expertise level. However, it does not cover the ethical considerations of using AI in cybersecurity, such as potential biases in the AI models or the risk of misuse of the technology.

What are the key processes that enable AMAR-Generator to interpret and synthesize data effectively?

The core strength of AMAR-Generator lies in its ability to synthesize information from diverse sources, connecting vendor reports with detailed technical data from sandbox logs. It uses template matching to create structured templates for a malware behavior database, extracts API calls and value names from sandbox logs using an API Behavior Map, correlates behavior using the malware behavior database, and produces human-readable reports based on matches. This automated interpretation enhances the accuracy and consistency of malware analysis. A topic not covered is how AMAR-Generator handles zero-day exploits or completely new malware behaviors not yet cataloged in vendor reports or behavior databases.

What are the planned future enhancements for AMAR-Generator?

Future research focuses on expanding the types of malicious behaviors AMAR-Generator can detect, improving the accuracy of interpretation, and refining the report generation process. The ultimate goal is to create a comprehensive and user-friendly tool that empowers cybersecurity professionals to stay ahead of evolving malware threats. An area for future work would be to enhance AMAR-Generator with capabilities to predict future malware behavior based on historical data, enhancing proactive threat hunting.

How does AMAR-Generator contribute to democratizing malware analysis?

AMAR-Generator democratizes malware analysis by making it more accessible to a wider range of cybersecurity professionals. By automating the analysis process and providing clear, human-readable reports, it reduces the need for specialized expertise and resources. This empowers security analysts to make informed decisions and respond effectively to threats, regardless of their skill level. A key consideration is the ongoing need for human oversight to validate AI-generated reports and ensure that the system is not being misled by adversarial tactics.

What specific data sources and techniques does AMAR-Generator utilize to produce its reports?

AMAR-Generator leverages vendor reports and sandbox logs to produce concise, human-readable reports. It uses template matching to abstract expressions in vendor reports, API behavior mapping to extract API calls and value names from sandbox logs, and a malware behavior database to confirm whether API call values are malicious. Concise reports are then generated based on matches in the malware behavior database, using descriptions from vendor reports. A potential improvement would be the ability to integrate threat intelligence feeds to enrich the analysis and improve the detection of emerging threats.