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Introduction:
Traditional cybersecurity defenses treat password attacks as intermittent spikes—sudden bursts of failed logins that trigger alarms. However, new data reveals a far more dangerous reality: sustained, round‑the‑clock authentication probing that never spikes, never stops, and quietly becomes part of your environment’s baseline. This shift transforms opportunistic hacking into continuous adversarial pressure, forcing organizations—especially Managed Service Providers (MSPs)—to move from reactive incident response to machine‑speed, autonomous threat adaptation.
Learning Objectives:
- Understand how persistent password attacks differ from traditional brute‑force spikes and why baseline anomalies are harder to detect.
- Implement real‑time monitoring and automated response using Linux/Windows commands and SIEM rules.
- Apply cloud hardening and identity protection techniques to mitigate continuous authentication threats.
You Should Know:
- Detecting Persistent Authentication Attacks with Linux Command Line
Persistent attacks leave a trail of repeated failed logins across system logs. Unlike spikes, these failures are evenly distributed—so a simple count over a short window won’t catch them. You need baseline analysis over weeks.
Step‑by‑step guide for Linux (using `journalctl` and `awk`):
First, collect failed SSH attempts over the last 90 days:
sudo journalctl --since="90 days ago" | grep "Failed password" > failed_logins.txt
Analyze hourly distribution to spot non‑spiky consistency:
awk '{print $1" "$2" "$3}' failed_logins.txt | cut -d: -f1 | sort | uniq -c | awk '$1 > 5 {print $0}'
This groups failures by hour; persistent attacks show a flat, moderate count every hour rather than sudden peaks.
To identify repeat offenders (IPs that probe slowly but constantly):
grep "Failed password" /var/log/auth.log | awk '{print $(NF-3)}' | sort | uniq -c | sort -n | tail -20
Look for IPs with thousands of attempts spread evenly over months, not clustered in minutes.
Windows equivalent (PowerShell as Admin):
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625} -MaxEvents 10000 | `
Group-Object -Property TimeCreated | `
Select-Object Name, Count | `
Sort-Object Count
For 90‑day analysis:
$startDate = (Get-Date).AddDays(-90)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625; StartTime=$startDate} | `
Group-Object { $<em>.TimeCreated.ToString("yyyy-MM-dd HH") } | `
ForEach-Object { [bash]@{Hour=$</em>.Name; Count=$_.Count} }
Export to CSV and plot—persistent attacks produce a nearly flat line.
2. Automating Response with Fail2Ban and Custom Jails
Since persistent attacks never stop, manual blocking is impossible. Use Fail2Ban with extended time windows and low thresholds.
Step‑by‑step guide to configure a “persistent mode” jail:
Edit `/etc/fail2ban/jail.local`:
[persistent-sshd] enabled = true port = ssh filter = sshd logpath = /var/log/auth.log maxretry = 3 findtime = 86400 24 hours – catches slow probes bantime = 2592000 30 days ban action = iptables-allports[name=persistent]
For even slower attacks (e.g., 1 attempt per hour), create a custom filter that tracks cumulative failures over 7 days:
sudo fail2ban-client set persistent-sshd maxretry 5 sudo fail2ban-client set persistent-sshd findtime 604800
To monitor the jail’s activity:
sudo fail2ban-client status persistent-sshd tail -f /var/log/fail2ban.log | grep persistent
Windows automation with PowerShell and Task Scheduler:
Create a script that runs hourly, adding IPs with >50 failures over 7 days to Windows Firewall:
$blockedIPs = @()
$events = Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625; StartTime=(Get-Date).AddDays(-7)}
$grouped = $events | Group-Object -Property { $<em>.Properties[bash].Value } IP address property
foreach ($group in $grouped) {
if ($group.Count -gt 50) {
$ip = $group.Name
New-NetFirewallRule -DisplayName "BlockPersistent</em>$ip" -Direction Inbound -RemoteAddress $ip -Action Block
$blockedIPs += $ip
}
}
$blockedIPs | Out-File "C:\Logs\blocked_ips.txt" -Append
- Cloud Hardening Against Continuous Probing (AWS & Azure)
Public cloud environments face the same persistent attacks, often targeting IAM roles and API endpoints. Use anomaly detection baselines with native tools.
AWS GuardDuty persistent threat detection:
Enable GuardDuty and customize suppression rules to ignore spikes but alert on steady probing:
aws guardduty create-filter --name persistent-auth-fails --action ARCHIVE --finding-criteria '{
"Criterion": {
"type": [{"Eq": ["UnauthorizedAccess:EC2/SSHBruteForce"]}],
"service.count": [{"Gte": 100, "Lte": 500}]
}
}'
Instead, create an alert for “low but constant” using Athena queries on CloudTrail logs:
SELECT eventtime, useridentity.username, sourceipaddress, COUNT() as attempts
FROM cloudtrail_logs
WHERE eventname IN ('ConsoleLogin', 'AssumeRole') AND errorcode = 'AccessDenied'
GROUP BY eventtime, useridentity.username, sourceipaddress
HAVING COUNT() BETWEEN 5 AND 50
ORDER BY eventtime;
Azure Sentinel baseline hunting:
SigninLogs | where ResultType == "50057" // User account is disabled or locked | where TimeGenerated > ago(90d) | summarize AttemptsPerHour = count() by bin(TimeGenerated, 1h), IPAddress, UserPrincipalName | where AttemptsPerHour < 10 and AttemptsPerHour > 0 | summarize PersistentAttempts = count() by IPAddress, UserPrincipalName | where PersistentAttempts > 500
- MFA Bypass Detection in a Persistent Threat Environment
Attackers constantly probe for MFA fatigue or push bombing. Continuous low‑volume MFA rejection requests are a key indicator.
Step‑by‑step to monitor MFA failures (Azure AD/Entra ID):
Connect-MgGraph -Scopes "AuditLog.Read.All"
$startTime = (Get-Date).AddDays(-30)
Get-MgAuditLogSignIn -Filter "status/errorCode eq 500121" | MFA required
Where-Object { $<em>.CreatedDateTime -gt $startTime } |
Group-Object { $</em>.CreatedDateTime.ToString("yyyy-MM-dd HH") } |
Sort-Object Count
If you see a constant 2–5 MFA failures per hour from the same user/IP, assume a persistent bypass attempt. Deploy conditional access policy to block after 10 failures over 24 hours:
New-MgIdentityConditionalAccessPolicy -DisplayName "BlockPersistentMFABypass" -State "enabled" -Conditions @{
Users = @{ IncludeUsers = @("all") }
SignInRiskLevels = @("medium", "high")
ClientAppTypes = @("all")
}
For on‑prem AD, monitor bad password counts with `Get-ADUser` and event log:
$badPWs = Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4625; StartTime=(Get-Date).AddDays(-1)} |
Where-Object { $<em>.Properties[bash].Value -eq 0 } 0 = bad password
$badPWs | Group-Object { $</em>.Properties[bash].Value } | Where-Object { $<em>.Count -gt 3 -and $</em>.Count -lt 20 }
5. Building Autonomous Machine‑Speed Response with SOAR Playbooks
Dor Eisner’s post emphasizes “connect the dots and act at machine speed.” Use a SOAR (Security Orchestration, Automation, Response) platform to correlate low‑level persistent events across identity, network, and cloud.
Example playbook logic (pseudo‑code for Tines / Cortex XSOAR):
def persistent_attack_detection():
Query 90 days of failed logins from SIEM
failed_logins = siem_query("SELECT src_ip, user, timestamp FROM auth_logs WHERE result='fail' AND timestamp > NOW() - INTERVAL 90 DAY")
hourly_counts = failed_logins.groupby([pd.Grouper(key='timestamp', freq='H'), 'src_ip']).size()
Detect IPs with >0 and <10 failures per hour for >80% of hours
persistent_ips = []
for ip, group in hourly_counts.groupby('src_ip'):
hours_active = group[group.between(1, 10)].count()
if hours_active / 2160 > 0.8: 90 days 24 = 2160 hours
persistent_ips.append(ip)
Auto‑block in firewall and alert SOC
for ip in persistent_ips:
firewall.block_ip(ip, duration=2592000) 30 days
send_alert(f"Persistent attacker {ip} over 90 days")
Deploy this as a cron job or cloud function every 6 hours.
Linux crontab for automated correlation:
0 /6 /usr/local/bin/persistent_attack_scanner.py && /usr/local/bin/update_firewall_rules.sh
6. Building Your Own Persistent Attack Lab
Test your defenses using a controlled simulation of sustained, low‑volume password attacks.
Step‑by‑step using Hydra with random delays:
Install Hydra
sudo apt install hydra
Create a wordlist of 100 common passwords
echo -e "password\n123456\nadmin\nwelcome" > weak.txt
Launch attack at 1 attempt per minute over 3 days (use 'sleep' in a loop)
for i in {1..4320}; do
hydra -l admin -P weak.txt ssh://192.168.1.100 -t 1 -f
sleep 60
done
Monitor your detection systems—they should not see a spike but should still trigger after cumulative thresholds.
Windows simulation using `Invoke-WebRequest` against a test IIS login form:
$credList = @("admin:1234", "admin:password", "user:qwerty")
while ($true) {
$cred = $credList | Get-Random
$pair = "$($cred.split(':')[bash]):$($cred.split(':')[bash])"
$encoded = [bash]::ToBase64String([Text.Encoding]::ASCII.GetBytes($pair))
Invoke-WebRequest -Uri "http://testapp/login" -Headers @{Authorization="Basic $encoded"}
Start-Sleep -Seconds 60
}
What Undercode Say:
- Baseline is the new alert. If your monitoring only catches spikes, you are blind to persistent, low‑volume attacks that never trigger traditional thresholds.
- Automation must be continuous and cumulative. Human‑in‑the‑loop response fails when attacks run 24/7 for months. Shift to autonomous, machine‑speed correlation and blocking.
- Identity is the battlefield. MFA fatigue, slow password spraying, and API key probing are becoming the dominant techniques—defend with behavioral baselines, not just point‑in‑time checks.
Prediction:
Within 24 months, persistent authentication attacks will outnumber traditional brute‑force spikes by 5:1, forcing every SIEM and XDR platform to incorporate “continuous baseline deviation” as a native detection primitive. MSPs that do not automate cumulative threat correlation will face undetected breaches lasting 6+ months. Conversely, organizations that adopt machine‑speed, long‑window anomaly detection will turn persistent attacks into a noisy but harmless background hum—just another routine condition to manage, not a crisis to survive. The arms race will shift from “detecting the break‑in” to “enduring the siege.”
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IT/Security Reporter URL:
Reported By: Dor Eisner – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
