Detecting Deepfake Audio In Vishing Attacks

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:
    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
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. 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