DeepProbe: Unmasking Hidden Threats in Memory with AI-Powered Intelligence

The Core Question This Article Answers

How can security teams quickly and accurately perform memory forensics to identify attacks that leave little to no trace? DeepProbe offers a groundbreaking solution through automation, intelligent correlation, and AI-enhanced analysis.

In today’s advanced threat landscape, attackers increasingly operate in memory to evade traditional disk-based forensics. Traces left in memory are often more subtle, transient, and technically challenging to analyze. While conventional memory analysis tools are powerful, they typically require deep expertise and extensive manual effort, resulting in slow analysis, missed evidence, and delayed incident response.

DeepProbe was created to address these challenges. It is not just a tool but an intelligent, automated framework that integrates the power of Volatility 3, customizable detection rules, and AI-driven insights. Whether you are a security analyst, digital forensics expert, or threat hunter, DeepProbe helps you rapidly extract, correlate, and interpret evidence of attacks in memory.

What Makes DeepProbe Unique?

The Core Question

What sets DeepProbe apart in the crowded field of digital forensics tools? Its unique value lies in intelligent correlation, AI enhancement, and a significantly improved user experience.

Traditional forensics tools often output large volumes of raw data, requiring analysts to manually connect the dots. DeepProbe’s core strength is its intelligent correlation engine. It automatically links disparate pieces of evidence—such as suspicious network connections, hidden processes, code injections, and persistence mechanisms—into a coherent attack narrative. This not only drastically reduces false positives but also truly highlights threats that require immediate attention.

Furthermore, DeepProbe integrates AI-powered analysis via the Gemini API, generating key findings, summaries, and attack chain narratives in natural language. This feature dramatically lowers the barrier to understanding technical reports, enabling non-technical decision-makers to grasp the security situation quickly.

Automation does not come at the expense of flexibility. DeepProbe’s detection rules are fully configurable through YAML files, allowing teams to tailor detection logic to their specific environment and evolving threat techniques.

Reflection and Insight:
In real-world security operations, time is often the scarcest resource. DeepProbe’s design philosophy is deeply rooted in the concept of “accelerating response”—it doesn’t replace expert judgment but rather automates preprocessing and intelligent correlation, allowing experts to focus on critical decision-making.

A Complete Overview of Core Features

Automated Memory Analysis

Built on the robust Volatility 3 framework, DeepProbe supports memory image analysis for Windows, Linux, and macOS systems. Users simply provide a memory dump, and DeepProbe automatically runs a series of predefined plugins to extract key digital evidence, including processes, network connections, loaded modules, registry entries, and more.

Example Scenario:
A company suspects a server has been compromised, but no anomalies are found on disk. By loading the server’s memory image into DeepProbe, they obtain a detailed list of all running processes, open ports, and network connections within 10 minutes. The list clearly includes an unknown, unsigned process communicating with an external IP.

Adaptive Detection Engine

Using configurable YAML rules, DeepProbe can identify various known malicious behaviors and anomalies, such as unknown code injections, unusual process parent-child relationships, hidden ports, and more. Users can flexibly adjust detection logic based on their threat intelligence and business context.

Example Scenario:
A financial institution is particularly concerned about lateral movement. They enhance the detection weighting for SMB-related processes and network connections in DeepProbe’s rule set. When an attacker attempts to move laterally via PsExec, DeepProbe immediately alerts and correlates the event with relevant process creation and network sessions.

Multi-Stage Attack Chain Reconstruction

This is DeepProbe’s standout capability. The system does not present anomalies in isolation but automatically chains related events to form an attack narrative. For example: a malicious child process spawned from Word (initial access), followed by a DNS query (C2 communication), then a binary dropped in the system directory (persistence), and finally, a connection to an external server (data exfiltration).

Example Scenario:
A spear-phishing attack leads to a network breach. While analyzing the memory of a compromised host, DeepProbe not only identifies the malicious document process but also correlates it with subsequent PowerShell download behavior, code injected into a legitimate system process, and a newly created scheduled task—fully reconstructing the attack from initial intrusion to established control.

AI-Generated Verdicts and Summaries

Integrated with the Google Gemini API, DeepProbe summarizes all forensic findings, generating easy-to-understand English summaries, attack step analyses, and final verdicts (e.g., “High Confidence—Exploit and Persistence Attack Detected”). This enables technical evidence to be quickly translated into actionable recommendations.

Interactive Streamlit User Interface

DeepProbe abstracts away the命令行 complexity of Volatility 3 through a clean web interface. Users can upload memory images, configure analysis jobs, and view real-time progress and final reports—all through a browser, without memorizing command-line parameters.

Comprehensive Reporting

After analysis, DeepProbe generates reports in multiple formats (such as HTML and JSONL), including detailed findings, AI summaries, and raw data. This facilitates team collaboration and allows for further investigation in other analysis platforms.

IP Intelligence Enrichment

If users provide an AbuseIPDB API key, DeepProbe automatically queries geographic location and historical malicious activity records for all external IPs, delivering critical context for determining whether a compromise occurred.

Getting Started: A Practical Deployment Guide

The Core Question This Section Answers

How can you quickly and securely set up a DeepProbe environment and perform your first memory forensics analysis? Here is a complete, step-by-step workflow based on Docker.

Prerequisites

Before you begin, ensure Docker Desktop (or Docker Engine for Linux) is installed and running on your system. Also, prepare the following files in your project’s root directory:

  • Dockerfile
  • app.py
  • runner.py
  • requirements.txt
  • detections.yaml
  • baseline.yaml
  • README, LICENSE, and other documentation files

You must also manually create two empty directories: memory/ and out/, to store memory images for analysis and output reports, respectively.

Building the Docker Image

Open a terminal, navigate to your project directory, and execute the following command to build the DeepProbe Docker image:

docker build -t deeprobe-app .

This process downloads the base image, installs Volatility 3 and Python dependencies, and may take several minutes to complete.

Running the DeepProbe Container Securely

To ensure an isolated analysis environment accessible only locally, create an isolated Docker network and launch the container within it:

docker network create isolated-net

Then run the container:

docker run --rm --network=isolated-net -p 127.0.0.1:8501:8501 \
    -v "$(pwd)"/memory:/app/memory \
    -v "$(pwd)"/out:/app/out \
    deeprobe-app

This command performs several key operations:

  • -p 127.0.0.1:8501:8501: Maps port 8501 inside the container to port 8501 on the host machine, binding it exclusively to localhost to prevent external access.
  • -v ...: Mounts the local memory and out directories into the container for input memory files and output reports.
  • --network=isolated-net: Runs the container in the isolated network, enhancing security.
  • --rm: Automatically removes the container when it exits.

Accessing and Using the Web UI

Access the DeepProbe interface by opening a web browser and navigating to:

http://localhost:8501

To use DeepProbe:

  1. Place your memory image file (e.g., .raw or .mem) in the local memory/ directory.
  2. In the UI, enter a project name and the memory file name.
  3. Optionally, provide AbuseIPDB and Gemini API keys to enable enhanced features.
  4. Click “Launch Analysis” to start the automated forensic scan.

Once analysis is complete, you can find all reports and raw data in the out/<project_name>/ directory, including an HTML overview, JSONL detailed results, and raw Volatility plugin outputs.

Reflection and Insight:
Dockerizing the tool not only simplifies deployment but also significantly improves environment consistency and security. Especially when analyzing memory samples that may contain malicious code, container isolation effectively protects the host machine from accidental impact.

Output Samples: From Data to Decisions

DeepProbe’s output is more than just a report—it is the end point of a complete analytical workflow. For example, after analyzing an attack, you might find the following in the output directory:

  • report.html: A visual overview including AI summary, attack chain diagram, and key metrics.
  • findings.jsonl: A JSON Lines file where each line is a JSON object recording a detected anomalous event and its metadata.
  • /volatility_output/: A directory containing all raw plugin outputs for deeper investigation.

This output design meets both the need for quick reporting and the demand for full原始数据保留（raw data retention）for deep dives.

Licensing and Open-Source Commitment

DeepProbe is built on Volatility 3 and therefore adopts the Volatility Software License (VSL) 1.0. This is a copyleft license that requires any derivative works based on Volatility to also be open-sourced under the same license. This ensures the project remains open, transparent, and continuously improvable by the community.

Conclusion: Why You Should Try DeepProbe

Memory forensics is no longer the exclusive domain of advanced experts. Through automation, correlation analysis, and AI enhancement, DeepProbe brings this critical security capability to a broader range of teams—whether you are a security engineer new to memory analysis or an incident response team needing fast turnaround times.

It reduces repetitive manual tasks, lowers the risk of missed detections, and accelerates the journey from evidence to decision-making through clear natural language output. In the fight against increasingly sophisticated cyber threats, such a tool is not just “nice to have” but essential.

One-Page Summary: DeepProbe Quick Start Checklist

✅ Ensure Docker is installed and running
✅ Create empty memory and out folders in the project root directory
✅ Build the image with docker build -t deeprobe-app .
✅ Run the container with port and volume mapping:

docker run --rm --network=isolated-net -p 127.0.0.1:8501:8501 \
    -v "$(pwd)"/memory:/app/memory \
    -v "$(pwd)"/out:/app/out \
    deeprobe-app

✅ Access the UI at http://localhost:8501
✅ Upload memory images to memory/ and configure analysis tasks in the UI
✅ Retrieve reports from out/<project_name>/

Frequently Asked Questions (FAQ)

Q1: Which operating system memory images does DeepProbe support?
A: It supports common memory dump formats for Windows, Linux, and macOS.

Q2: Are API keys required to use DeepProbe?
A: No. API keys for AbuseIPDB and Gemini are only needed if you wish to enable IP intelligence and AI summary features.

Q3: Can analysis results be exported?
A: Yes, all results are exported in HTML, JSONL, and raw text formats to the out directory.

Q4: Can DeepProbe detect fileless attacks?
A: Yes, its detection rules include typical fileless techniques such as code injection, hidden processes, and non-disk persistence.

Q5: Does it support batch analysis of multiple memory images?
A: The current version requires manual submission for each analysis. Batch processing may be supported in the future.

Q6: How can I customize detection rules?
A: By editing the detections.yaml file in the project, you can flexibly adjust or extend detection logic.

Q7: Is DeepProbe suitable for enterprise integration?
A: Yes, its containerized deployment and structured output make it easy to integrate into enterprise SOC or DFIR workflows.

Q8: Will analysis time be long for very large memory images?
A: Analysis time depends mainly on image size and hardware performance. DeepProbe is optimized for performance, but it is recommended to use powerful hardware for very large images.