Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
303 detections
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SSH Brute Force Attempt Detected
Suricata
Detects repeated failed SSH authentication attempts which may indicate brute force activity against SSH services. The rule specifically looks for 8 SSH connection attempts to port 22 within a 60-second window from the same source IP.
This rule detects attempts to modify the SSH authorized_keys file, which can be used by adversaries to maintain persistence on a compromised system, using common utilities like echo, tee, cp, mv, python, or perl.
High-fidelity correlation detecting DPRK Contagious Trader npm malware persistence: Node.js process modifying authorized_keys followed by ufw allowing SSH. Targets cryptocurrency traders via poisoned GitHub trading bot repositories.
High-fidelity correlation detecting DPRK Contagious Trader npm malware persistence: Node.js process modifying authorized_keys followed by ufw allowing SSH. Targets cryptocurrency traders via poisoned GitHub trading bot repositories.
This rule detects successful SSH login attempts from external IP addresses (not within common private IP ranges). It specifically looks for 'Accepted' messages in syslog from the 'sshd' process and filters out internal source IPs.
This rule detects potential SSH brute force attacks by monitoring syslog for multiple failed SSH login attempts, connection closures, or invalid user attempts from a single source IP address within a short time frame. It specifically looks for keywords like 'error', 'Connection closed', 'authentication failure', and 'invalid user' in SSHD logs.
This rule detects potential SSH brute force attacks by identifying multiple failed password attempts from a single source IP address to a specific computer within a 5-minute window. It specifically looks for 'Failed password' messages in syslog entries from the 'sshd' process.
Detects multiple failed SSH login attempts from a single source IP address, indicating a potential brute force attack. The rule identifies 'Invalid user' or 'Failed password' messages in syslog from the 'sshd' process and groups them by source IP and time to count unique attempted users and total attempts. A threshold of more than 5 attempted users from a single source IP within a 10-minute window triggers an alert.
This rule detects attempts to authenticate to SSH using GSSAPI (Generic Security Service Application Programming Interface). While GSSAPI can be used for legitimate authentication, its presence in logs might indicate an attacker attempting to use alternative authentication methods, potentially for lateral movement or privilege escalation, especially if unexpected in the environment.
This rule detects successful SSH authentications using the GSSAPI-with-MIC mechanism. This can indicate legitimate user logins or, in some cases, an adversary using valid credentials to access a system via SSH.
This rule detects potential indicators of compromise related to CVE-2026-20127 in Cisco Catalyst SD-WAN systems. It focuses on two main areas: 1) suspicious successful SSH authentications for the 'vmanage-admin' account, and 2) SD-WAN control plane connection events indicating a new 'up' state. The first part looks for specific log entries related to publickey authentication for 'vmanage-admin' from any source IP. The second part identifies control connection state changes to 'up' and extracts peer type, peer system IP, and public IP from the raw logs. The rule aims to surface events that require human confirmation by comparing them against known network designs and change records, as these events can indicate unexpected relationships between devices or unauthorized privileged access.
Detects successful administrative logins ('vmanage-admin') to a Cisco SD-WAN device from an IP address not on the authorized list. This could indicate exploitation of CVE-2026-20127, an authentication bypass vulnerability.
Hunts for unusual external SSH access patterns indicating Technique T1133
Hunts for unusual external SSH access patterns indicating Technique T1133
This rule detects inbound SSH connections originating from external IP addresses to systems running SSH services. It specifically looks for successful incoming network connections where the destination port is commonly associated with SSH (22, 222, 2222, 8022) or the source process name is an SSH daemon (sshd, dropbear). The rule explicitly excludes connections from private IP ranges and certain common web server processes on standard web ports to reduce false positives. The threat model includes unauthorized external SSH access, brute-force/credential-stuffing attacks, backdoor SSH daemons, compromised servers accepting attacker sessions, and reverse shell callbacks.
This rule detects successful SSH authentication attempts originating from IP addresses that are not classified as private (non-RFC1918). It specifically looks for log entries indicating 'Accepted password' or 'Accepted publickey' and then filters out source IP addresses that fall within common private IPv4 ranges (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16, 127.0.0.0/8, 169.254.0.0/16). The rule is designed to identify SSH access from external networks, which might be unusual in environments where SSH is expected to be primarily internal. It does not account for IPv6 private addresses.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
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