Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This analytic detects intrusion events from known threat activity using Cisco Secure Firewall Intrusion Events. It leverages Cisco Secure Firewall Threat Defense IntrusionEvent logs to identify cases where one or multiple Snort signatures associated with a known threat or threat actor activity have been triggered within a one-hour time window. The detection uses a lookup table (cisco_snort_ids_to_threat_mapping) to map Snort signature IDs to known threat actors and their techniques. When multiple signatures associated with the same threat actor are triggered within the time window, and the count of unique signatures matches or exceeds the expected number of signatures for that threat technique, an alert is generated. This helps identify potential coordinated threat activity in your network environment by correlating related intrusion events that occur in close temporal proximity. Currently, this detection will alert on the following threat actors or malware families as defined in the cisco_snort_ids_to_threat_mapping lookup: ArcaneDoor, Static Tundra, AgentTesla, Amadey, AsyncRAT, Chafer, DCRAT, Lumma Stealer, Nobelium, Quasar, Remcos, Snake, Xworm. To add or update threat actors, update the cisco_snort_ids_to_threat_mapping.csv lookup file with new or modified threat names and associated Snort signature IDs.
Detects network traffic to a known Command and Control (C2) server (136.243.146.46:8898) associated with the YiBackdoor malware.
Detects DNS queries to domains within known low reputation Effective Top-Level Domains (eTLDs). These eTLDs are often associated with malicious activities such as command and control, phishing, or malware distribution. The rule leverages a curated list of suspicious eTLDs, which is updated periodically.
Detects network connections to the 'webhook.site' domain, which is used as an exfiltration endpoint by the 'Shai-Halud' supply chain attack campaign. The risk score is elevated if the specific malicious URL path is observed.
The following analytic identifies attempts to access the Remote ShellServlet on a web server, specifically targeting Confluence servers vulnerable to CVE-2023-22518 and CVE-2023-22515. It leverages web data to detect URLs containing "*plugins/servlet/com.jsos.shell/*" with a status code of 200. This activity is significant as it is commonly associated with web shells and other malicious behaviors, potentially leading to unauthorized command execution. If confirmed malicious, attackers could gain remote code execution capabilities, compromising the server and potentially the entire network.
This analytic detects unexpected outbound network connections initiated by known webserver processes such as `httpd.exe`, `nginx.exe`, or `tomcat.exe` to common file sharing or public content hosting services like GitHub, Discord CDN, Transfer.sh, or Pastebin. Webservers are rarely expected to perform outbound downloads, especially to dynamic or anonymous file hosting domains. This behavior is often associated with server compromise, where an attacker uses a reverse shell, webshell, or injected task to fetch malware or tools post-exploitation. The detection leverages Cisco Network Visibility Module flow data, enriched with process context, to identify this highly suspicious behavior.
The analytic detects long URLs containing multiple SQL commands, indicating a potential SQL injection attack. This detection leverages web traffic data, specifically targeting web server destinations with URLs longer than 1024 characters or HTTP user agents longer than 200 characters. SQL injection is significant as it allows attackers to manipulate a web application's database, potentially leading to unauthorized data access or modification. If confirmed malicious, this activity could result in data breaches, unauthorized access, and complete system compromise. Immediate investigation and validation of alerts are crucial to mitigate these risks.
Detects network traffic consistent with XWorm malware using the Telegram API for Command and Control (C2) or data exfiltration. This rule specifically looks for HTTP requests to 'api.telegram.org' with URL patterns indicative of sending messages or files via the Telegram Bot API (e.g., /bot<token>/sendMessage, /bot<token>/sendDocument, /bot<token>/sendPhoto).
The analytic detects the presence of the Supernova webshell, used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection leverages Splunk to search for URLs containing "*logoimagehandler.ashx*codes*", "*logoimagehandler.ashx*clazz*", "*logoimagehandler.ashx*method*", and "*logoimagehandler.ashx*args*". This activity is significant as it indicates potential unauthorized access and arbitrary code execution on a compromised system. If confirmed malicious, this could lead to data theft, ransomware deployment, or other severe outcomes. Immediate steps include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent processes and network connections.
This detection identifies a single MAC address rapidly appearing on multiple switch ports within a short time frame. This behavior, known as MAC flapping, is a strong indicator of a network loop, which can cause broadcast storms and degrade or disable network services.
Detects network traffic potentially associated with a scraper botnet variant that uses the "Hello-World/1.0" user-agent string.
Time to live (TTL) values in DNS responses provide valuable threat-hunting insights, including:
- Fast-flux botnets (rotating IPs with low TTLs).
- Malware C2 detection (extremely low TTLs). ( | where TTLs < 10 )
- DNS tunneling (high TTLs or changing TTLs). ( | where TTLs > 86400 )
- Fake domains mimicking real services (TTL anomalies).
- Evasive infrastructure constantly changing TTL values.
You can use the following KQL Query to search for the mentioned cases.
- Fast-flux botnets (rotating IPs with low TTLs).
- Malware C2 detection (extremely low TTLs). ( | where TTLs < 10 )
- DNS tunneling (high TTLs or changing TTLs). ( | where TTLs > 86400 )
- Fake domains mimicking real services (TTL anomalies).
- Evasive infrastructure constantly changing TTL values.
You can use the following KQL Query to search for the mentioned cases.
This KQL Query is oriented to extract the different bits of TCP Flags which helps to review the different state of the connections and identify possible threats associated.
In addition, it allows to filter by specific TCP Flag to detect specific threats based on every case.
In addition, it allows to filter by specific TCP Flag to detect specific threats based on every case.
Detects potential Adversary-in-the-Middle (AiTM) phishing activity, commonly associated with frameworks like Evilginx. The rule identifies suspicious domain patterns where legitimate identity provider (IdP) domains are used as subdomains of another domain, or where IdP-related keywords are used in hyphenated subdomains.
The following query is oriented to extend information about the potential threats associated with distinct HTTP status code. This allows me to quickly work with other KQL queries to identify potential threats. Consider this KQL query as a "Pivot Table" for multiple queries that can be triggered based on your criteria.
This rule detects attempts to download known malicious NPM packages from the public npm registry (registry.npmjs.org) by monitoring network connection logs. It specifically looks for tarball (.tgz) downloads of packages and versions that have been identified as compromised or malicious. The rule extracts the package name and version from the download URL and compares it against a predefined list of malicious packages for high-fidelity detection.
This rule detects network connections to known dynamic DNS (DDNS) subdomains. Adversaries often use dynamic DNS services to maintain persistent command and control (C2) communication channels, as these services allow them to frequently change the IP address associated with a static hostname, making it harder for defenders to block or track their infrastructure. These providers also have established domain reputation, and provide legitimate services making blanket IOC rules impossible. The rule identifies connections to specific DDNS providers such as DuckDNS, No-IP, PublicVM, and others.
The analytic detects outbound network connections from an endpoint process to suspicious or non-standard ports. It uses Cisco Network Visibility Module flow data to identify processes communicating over ports like 4444, 2222, or 51820, which are commonly associated with tools such as Metasploit, SliverC2, other penetration testing tools, red team activities, or malware. These connections warrant further investigation, especially when initiated by unexpected or non-network-native binaries.
This analytic detects changes to SNMP community strings on Cisco devices, which could indicate an attacker establishing persistence or attempting to extract credentials. After gaining initial access to network devices, threat actors like Static Tundra often modify SNMP configurations to enable unauthorized monitoring and data collection. This detection specifically looks for the configuration of SNMP community strings with read-write (rw) or read-only (ro) permissions, as well as the configuration of SNMP hosts that may be used to exfiltrate data. These activities are particularly concerning as they may represent attempts to establish persistent access or extract sensitive information from compromised devices.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
This analytic detects the creation or modification of network interfaces on Cisco devices, which could indicate an attacker establishing persistence or preparing for lateral movement. After gaining initial access to network devices, threat actors like Static Tundra often create new interfaces (particularly loopback interfaces) to establish covert communication channels or maintain persistence. This detection specifically looks for the configuration of new interfaces, interface state changes, and the assignment of IP addresses to interfaces. These activities are particularly concerning when they involve unusual interface names or descriptions containing suspicious terms.
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