Splunk Security
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The following analytic detects suspicious registry modifications indicative of NjRat's fileless storage technique. It leverages the Endpoint.Registry data model to identify specific registry paths and values commonly used by NjRat for keylogging and executing DLL plugins. This activity is significant as it helps evade traditional file-based detection systems, making it crucial for SOC analysts to monitor. If confirmed malicious, this behavior could allow attackers to persist on the host, execute arbitrary code, and capture sensitive keystrokes, leading to potential data breaches and further system compromise.
The following analytic detects the execution of ngrok.exe on a Windows operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly used by adversaries to bypass network defenses and establish reverse proxies. If confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence, or facilitate further attacks by tunneling traffic through the compromised system.
This analytic detects potential credential dumping by identifying specific GrantedAccess permission requests and CallTrace DLLs targeting the LSASS process. It leverages Sysmon EventCode 10 logs, focusing on access requests to lsass.exe and call traces involving debug and native API DLLs like dbgcore.dll, dbghelp.dll, and ntdll.dll. This activity is significant as credential dumping can lead to unauthorized access to sensitive credentials. If confirmed malicious, attackers could gain elevated privileges and persist within the environment, posing a severe security risk.
The analytic detects when an unusual number of NTLM authentications is attempted by the same source against multiple destinations. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to multiple domain-joined Windows devices using an NTLM-based process/attack. This same activity may also generate a large number of EventID 4776 events as well.
The analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages network resolution data to analyze domain names and detect unusual character sequences indicative of DGA activity. Adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions.
The following analytic identifies unauthorized software downloads to network devices via TFTP, FTP, or SSH/SCP. It detects this activity by analyzing network traffic events on specific ports (69, 21, 22) from devices categorized as network, router, or switch. This activity is significant because adversaries may exploit netbooting to load unauthorized operating systems, potentially compromising network integrity. If confirmed malicious, this could lead to unauthorized control over network devices, enabling further attacks, data exfiltration, or persistent access within the network.
The analytic identifies potential exploitation of CVE-2023-20198 in the Web User Interface of Cisco IOS XE software. It detects suspicious activity involving specific URL patterns and HTTP methods, indicative of unauthorized administrative access and potential implant deployment. This activity can lead to full control of the device, compromising its integrity and security.
The analytic identifies emails claiming to be sent from a domain similar to one you are monitoring for potential abuse. It leverages email header data, specifically the sender's address, and cross-references it with a lookup table of known domain permutations generated by the "ESCU - DNSTwist Domain Names" search. This activity is significant as it can indicate phishing attempts or brand impersonation, which are common tactics used in social engineering attacks. If confirmed malicious, this could lead to unauthorized access, data theft, or reputational damage.
The following analytic detects `StopLogging` events within AWS CloudTrail logs, a critical action that adversaries may use to evade detection. By halting the logging of their malicious activities, attackers aim to operate undetected within a compromised AWS environment. This detection is achieved by monitoring for specific CloudTrail log entries that indicate the cessation of logging activities. Identifying such behavior is crucial for a Security Operations Center (SOC), as it signals an attempt to undermine the integrity of logging mechanisms, potentially allowing malicious activities to proceed without observation. The impact of this evasion tactic is significant, as it can severely hamper incident response and forensic investigations by obscuring the attacker's actions.
The analytic detects `PutBucketLifecycle` events in AWS CloudTrail logs where a user sets a lifecycle rule for an S3 bucket with an expiration period of fewer than three days. This activity is significant because attackers may use it to delete CloudTrail logs quickly, thereby evading detection and impairing forensic investigations. If confirmed malicious, this could allow attackers to cover their tracks, making it difficult to trace their actions and respond to the breach effectively.
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