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"]

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

# 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:
     {k: re.findall(v, text)  k, v  IOC_PATTERNS.items()}