Detecting Deepfake Audio In Vishing Attacks

---
name: detecting-deepfake-audio-in-vishing-attacks
description: Use when detecting AI-generated deepfake audio used in voice phishing
  (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral
  contrast, zero-crossing rate) and classifying samples with machine learning models.
  Supports batch analysis of audio files, generating confidence scores, and produces
  forensic reports.
domain: cybersecurity
tags:
- deepfake-detection
- vishing
- audio-forensics
- MFCC
- spectral-analysis
- voice-cloning
subdomain: social-engineering-defense
version: 1.0.0
author: oyi77
license: Apache-2.0
atlas_techniques:
- AML.T0088
- AML.T0043
- AML.T0018
- AML.T0052
nist_ai_rmf:
- MEASURE-2.7
- GOVERN-6.2
- MAP-5.2
- MEASURE-2.5
- MAP-5.1
d3fend_techniques:
- Sender Reputation Analysis
- Content Validation
- Message Analysis
- User Behavior Analysis
- Identifier Analysis
nist_csf:
- PR.AT-01
- DE.CM-09
- RS.CO-02
category: cybersecurity
---

# Detecting Deepfake Audio In Vishing Attacks

## Overview

Cybersecurity skill for detecting deepfake audio in vishing attacks. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "detecting deepfake audio in vishing attacks"
- "Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by "


- A suspected vishing call used an AI-cloned executive voice to authorize a wire transfer
- Security operations received a voicemail that sounds like the CEO but the tone seems off
- Incident response needs to determine whether a recorded phone call contains synthetic speech
- Fraud investigation requires forensic proof that audio was AI-generated
- Red team exercises use voice cloning and blue team needs detection capability

**Do not use** for text-based phishing (email/SMS); use email header analysis or URL detonation tools instead.


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites

- Python 3.9+ with librosa, numpy, scikit-learn, and scipy installed
- Audio samples in WAV, MP3, or FLAC format (mono or stereo, any sample rate)
- Reference corpus of known genuine voice samples for the targeted individual (optional but improves accuracy)
- FFmpeg installed for audio format conversion (librosa dependency)
- Minimum 3 seconds of audio for reliable feature extraction

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Define Detection Scope** — Identify the specific deepfake audio in vishing attacks techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. **Collect Baseline Data** — Gather historical logs and establish normal behavior patterns for deepfake audio in vishing attacks.
3. **Build Detection Queries** — Write detection rules, Sigma rules, or SIEM queries targeting deepfake audio in vishing attacks indicators.
4. **Execute Hunts** — Run queries against the collected data, starting with broad filters and narrowing down.
5. **Triage Results** — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. **Document Findings** — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

## Tools

- **SIEM Platform** — Central log aggregation and query execution
- **Sigma Rules** — Vendor-agnostic detection rule format
- **MITRE ATT&CK Navigator** — Technique mapping and coverage analysis


## Process

1. **Reconnaissance** — Gather target information, identify attack surface, enumerate services
1. **Analysis/Exploitation** — Execute the technique, analyze results, document findings
1. **Reporting** — Document IOCs, write findings, provide remediation recommendations

## Verification

- [ ] All deepfake audio in vishing attacks procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |