3 Ways AI Secures OT & ICS from Cyber Attacks

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09
Jan 2024
09
Jan 2024
Explore the three challenges facing industries that manage OT and ICS Systems, the benefits of adopting AI technology, and Darktrace/OT’s unique role!

What is OT and ICS?

Operational technologies and industrial control systems are the networked technologies used for the automation of physical processes. These are the technologies that allow operators to control processes and retrieve real time process data from a factory, rail system, pipeline, and other industrial processes.  

The role of AI in defending OT/ICS networks  

While largely adopted by industrial organizations, OT is utilized by Critical Infrastructures, these being the industries that directly affect the health, safety, and welfare of the public. As these organizations expand and adopt new networked industrial technologies, they are simultaneously expanding their attack surface.  

With a larger attack surface, more attacks targeting OT/ICS, and focused coordination around cyber security from regulatory authorities, security personnel have increasing workloads that make it difficult to keep pace with threats and vulnerabilities. Defenders are managing growing attack surfaces due to IT and OT convergence. Thus, the adoption of AI technology to protect, detect, respond, and recover from cyber incidents in industrial systems is paramount for keeping critical infrastructure safe.

This blog will explore three challenges facing industries managing OT/ICS, the perceived benefits of adopting AI technology to address these challenges, and Darktrace/OT’s unique role in this process.  

Darktrace also delivers complete AI-powered solutions to defend US federal government customers from cyber disruptions and ensure mission resilience. Learn more about high fidelity detection in Darktrace Federal’s TAC report.

Figure 1: AI statistics from Gartner and Deloitte

Three ways AI helps improves OT/ICS security  

1. Anomaly detection and response

In this heightened security landscape, OT/ICS environments face a spectrum of external cyber threats that demand vigilant defense. From the looming risk of industrial ransomware to the threat of insiders, yet another dimension is added to security challenge, meaning security professionals must be equipped to detect and respond to internal and external threats.  

While threats are eminent from both inside and outside the organization, many organizations rely on Indicator of Compromises (IOCs) for threat detection. By definition, these solutions can only detect network activity they recognize as an indicator of compromise; therefore, often miss insider threats and novel (zero-day) attacks because the tactics, techniques, and procedures (TTPs) and attack toolkits have never been seen in practice.  

Anomaly-based detection is best suited to combat never-before-seen threats and signatureless threats from the inside. However, not all detection methods are equal. Most anomaly-based detection solutions that leverage AI rely on a combination of supervised machine learning, deep learning, and transformers to train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. This data set gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.  

While this method reduces the workload for security teams who would have to input attack data otherwise manually, it runs the same risk of only detecting known threats and has potential privacy concerns when shipping this data externally.  

To improve the quality and speed of anomaly detection, Darktrace/OT uses Self-Learning AI that leverages Bayesian Probabilistic Methodologies, Graph Theory, and Deep Neural Networks to learn your organization from the ground up in real time. By learning your unique organization, Darktrace/OT develops a sophisticated baseline knowledge of your network and assets, identifying abnormal activity that indicates a threat based on your unique network data at machine speed. Because the AI engine is local to the organization and/or assets, concerns of data residency and privacy are reduced, and the result is faster time to detect and triage incidents.  

Leveraging Self-Learning AI, Darktrace/OT uses autonomous response that severs only the anomalous or risky behaviors allowing the assets to continue to operate as normal. Organizations work with Darktrace to customize how they want Darktrace’s autonomous response to be applied. These options vary from on a device- by-device basis, device type by device type, or subnet by subnet basis and can be done completely autonomously or in human confirmation mode. This gives security teams more time to respond to an incident and reduces operational downtime when facing a threat.  

Darktrace leverages a combination of AI methods:

  • 自己学習型AI
  • Bayesian classification probabilistic models  
  • Deep neural networks
  • Transformers
  • Graph theory models
  • Clustering models  
  • Anomaly detection models
  • Generative and applied AI  
  • 自然言語処理  
  • Supervised machine learning for investigation process of alerts

2. Vulnerability & Asset Management

At present, managing OT cyber risk is labor and resource intensive. Many organizations use third-party auditors to identify assets and vulnerabilities, grade compliance, and recommend improvements.  

At best, these exercises become tick-box exercises for companies to stay in compliance with little measurable reduction in cyber risk. At worst, asset owners can be left with a mountain of vulnerability information to work through, much of it irrelevant to the security risks Engineering and Operations teams deal with day to day, and increasingly out of date each passing day after the annual or biannual audit has been completed.  

In both cases, organizations are left using a patchwork of point products to address different aspects of preventative OT cyber security, most of which lack wider business context and lead to costly inefficiencies with no real impact to vulnerability or risk exposure.  

Darktrace’s technology helps in three unique ways:

  1. AI populates asset inventories: Self-Learning AI technology listens and learns from network traffic to populate or update asset inventories. It does this not just by identifying simple IPs, mac addresses, and hostnames, it learns from what it sees and automatically classifies or tags specific types of assets with the function that they perform. For example, if a specific device is performing functions like a PLC, sending commands to and from an HMI, it can appropriately tag and label these systems.
  2. AI prioritizes risk: Leveraging Bayesian Probabilistic Methodologies, Graph Theory, and Deep Neural Networks, Darktrace/OT assesses the strategic risks facing your organization in real time. Using knowledge of data points on all your networked assets, data flow topology, your assets vulnerabilities and OSINT, Darktrace identifies and prioritizes high-value assets, potential attack pathways based on an existing vulnerabilities targetability and impact.
  3. AI explains remediation tactics: Many OT devices run 24/7 operations and cannot be taken offline to apply a patch, assuming a patch is even available. Darktrace/OT uses natural language processing to provide and explain prioritized remediation and mitigation associated with a given cyber risk across all MITRE ATT&CK techniques. Thus, where a CVE exists but a patch cannot be applied, a different technical mitigation can be recommended to remove a potential attack path before it can be exploited, preemptively securing vital internal systems and assets.
Figure 2: A critical attack path which starts with the compromise of a PC in the internal IT network, and ends with a PLC in the OT network. Each step is mapped out to the real world TTPs including abuse of SSH sessions and the modifications of ICS programs

3. Simplify compliance and reporting

Organizations, regardless of size or resources, have compliance regulations they need to adhere to. What this creates is an increased workload for security professionals. For smaller organizations, security teams might lack the manpower or resources to report in the short time frame that is required. For large organizations, keeping track of a massive amount of assets proves to be a challenge. Both cases emanate the risk of reporting fatigue where organizations might be hesitant to report incidents due to the complexity and time requirements they demand.  

An AI engine within the Darktrace/OT platform, Cyber AI analyst autonomously investigates incidents, summarize findings in natural language, and provides comprehensive insights into the nature and scope of cyber threats to improve the time it takes to triage and report on incidents. The ability to stitch together and present related security events provides a holistic understanding of the incident, enabling security analysts to identify patterns, assess the scope of potential threats, and prioritize responses effectively.  

Darktrace's detection capabilities identify every stage of an intrusion, from a compromised domain controller to network reconnaissance and privilege escalation. The AI technology is capable of detecting infections across several devices and generating incident reports that piece together disparate events to give a clear security narrative containing details of the attack, bridging the communication gap between IT and OT specialists.  

Post-incident, the technology assists in outlining timelines, discerning compromised data, pinpointing unusual activities, and aiding security teams in proactive threat mitigation.  

With its capabilities, organizations can swiftly understand the attack timeline, affected assets, unauthorized accesses, compromised data points, and malicious interactions, facilitating appropriate communication and action. For example, when Cyber AI Analyst shows an attack path, the security team gains insight on the segmentation or lack thereof between two subnets allowing the security team to appropriately segment the subnets.  

Cyber AI improves critical infrastructure operators’ ability to report major cyber-attacks to regulatory authorities. Considering that 72 hours is the reporting period for most significant incidents — and 24 hours for ransomware payments — Cyber AI Analyst is no longer a nice-to-have but a must-have for critical infrastructure.

Figure 3: The tabs labeled 1-4 denote model breaches, each with a specific action and severity indicated by color dots. Darktrace integrates these breaches, offering the security team a unified view of interconnected security events.  

The right AI for the right challenge

Incident Phase:

Protect

Role of AI:

サイバーリスクの優先順位付け

Attack path modelling

Compliance reporting

Darktrace Product:

PREVENT/OT

Incident Phase:

Detect

Role of AI:

Anomaly detection

Triaging and investigating

Darktrace Product:

Cyber AI analyst

DETECT/OT

Incident Phase:

応答する

Role of AI: 

Autonomous response  

Incident reporting

Darktrace Product:

RESPOND/OT

Incident Phase:

Recover

Role of AI:

Incident preparedness

Incident simulations

Darktrace Product:

HEAL

Credit to: Nicole Carignan, VP of Strategic Cyber AI - Kendra Gonzalez Duran, Director of Technology Innovation - & Daniel Simonds, Director of Operational Technology for their contribution to this blog.

INSIDE THE SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
AUTHOR
ABOUT ThE AUTHOR
Oakley Cox
Analyst Technical Director, APAC

Oakley is a technical expert with 5 years’ experience as a Cyber Analyst. After leading a team of Cyber Analysts at the Cambridge headquarters, he relocated to New Zealand and now oversees the defense of critical infrastructure and industrial control systems across the APAC region. His research into cyber-physical security has been published by Cyber Security journals and CISA. Oakley is GIAC certified in Response and Industrial Defense (GRID), and has a Doctorate (PhD) from the University of Oxford.

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Stemming the Citrix Bleed Vulnerability with Darktrace’s ActiveAI Platform

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28
May 2024

What is Citrix Bleed?

Since August 2023, cyber threat actors have been actively exploiting one of the most significant critical vulnerabilities disclosed in recent years: Citrix Bleed. Citrix Bleed, also known as CVE-2023-4966, remained undiscovered and even unpatched for several months, resulting in a wide range of security incidents across business and government sectors [1].

How does Citrix Bleed vulnerability work?

The vulnerability, which impacts the Citrix Netscaler Gateway and Netscaler ADC products, allows for outside parties to hijack legitimate user sessions, thereby bypassing password and multifactor authentication (MFA) requirements.

When used as a means of initial network access, the vulnerability has resulted in the exfiltration of sensitive data, as in the case of Xfinity, and even the deployment of ransomware variants including Lockbit [2]. Although Citrix has released a patch to address the vulnerability, slow patching procedures and the widespread use of these products has resulted in the continuing exploitation of Citrix Bleed into 2024 [3].

How Does Darktrace Handle Citrix Bleed?

Darktrace has demonstrated its proficiency in handling the exploitation of Citrix Bleed since it was disclosed back in 2023; its anomaly-based approach allows it to efficiently identify and inhibit post-exploitation activity as soon as it surfaces.  Rather than relying upon traditional rules and signatures, Darktrace’s Self-Learning AI enables it to understand the subtle deviations in a device’s behavior that would indicate an emerging compromise, thus allowing it to detect anomalous activity related to the exploitation of Citrix Bleed.

In late 2023, Darktrace identified an instance of Citrix Bleed exploitation on a customer network. As this customer had subscribed to the Proactive Threat Notification (PTN) service, the suspicious network activity surrounding the compromise was escalated to Darktrace’s Security Operation Center (SOC) for triage and investigation by Darktrace Analysts, who then alerted the customer’s security team to the incident.

Darktrace’s Coverage

Initial Access and Beaconing of Citrix Bleed

Darktrace’s initial detection of indicators of compromise (IoCs) associated with the exploitation of Citrix Bleed actually came a few days prior to the SOC alert, with unusual external connectivity observed from a critical server. The suspicious connection in question, a SSH connection to the rare external IP 168.100.9[.]137, lasted several hours and utilized the Windows PuTTY client. Darktrace also identified an additional suspicious IP, namely 45.134.26[.]2, attempting to contact the server. Both rare endpoints had been linked with the exploitation of the Citrix Bleed vulnerability by multiple open-source intelligence (OSINT) vendors [4] [5].

Darktrace model alert highlighting an affected device making an unusual SSH connection to 168.100.9[.]137 via port 22.
Figure 1: Darktrace model alert highlighting an affected device making an unusual SSH connection to 168.100.9[.]137 via port 22.

As Darktrace is designed to identify network-level anomalies, rather than monitor edge infrastructure, the initial exploitation via the typical HTTP buffer overflow associated with this vulnerability fell outside the scope of Darktrace’s visibility. However, the aforementioned suspicious connectivity likely constituted initial access and beaconing activity following the successful exploitation of Citrix Bleed.

Command and Control (C2) and Payload Download

Around the same time, Darktrace also detected other devices on the customer’s network conducting external connectivity to various endpoints associated with remote management and IT services, including Action1, ScreenConnect and Fixme IT. Additionally, Darktrace observed devices downloading suspicious executable files, including “tniwinagent.exe”, which is associated with the tool Total Network Inventory. While this tool is typically used for auditing and inventory management purposes, it could also be leveraged by attackers for the purpose of lateral movement.

防衛回避

In the days surrounding this compromise, Darktrace observed multiple devices engaging in potential defense evasion tactics using the ScreenConnect and Fixme IT services. Although ScreenConnect is a legitimate remote management tool, it has also been used by threat actors to carry out C2 communication [6]. ScreenConnect itself was the subject of a separate critical vulnerability which Darktrace investigated in early 2024. Meanwhile, CISA observed that domains associated with Fixme It (“fixme[.]it”) have been used by threat actors attempting to exploit the Citrix Bleed vulnerability [7].

Reconnaissance and Lateral Movement

A few days after the detection of the initial beaconing communication, Darktrace identified several devices on the customer’s network carrying out reconnaissance and lateral movement activity. This included SMB writes of “PSEXESVC.exe”, network scanning, DCE-RPC binds of numerous internal devices to IPC$ shares and the transfer of compromise-related tools. It was at this point that Darktrace’s Self-Learning AI deemed the activity to be likely indicative of an ongoing compromise and several Enhanced Monitoring models alerted, triggering the aforementioned PTNs and investigation by Darktrace’s SOC.

Darktrace observed a server on the network initiating a wide range of connections to more than 600 internal IPs across several critical ports, suggesting port scanning, as well as conducting unexpected DCE-RPC service control (svcctl) activity on multiple internal devices, amongst them domain controllers. Additionally, several binds to server service (srvsvc) and security account manager (samr) endpoints via IPC$ shares on destination devices were detected, indicating further reconnaissance activity. The querying of these endpoints was also observed through RPC commands to enumerate services running on the device, as well as Security Account Manager (SAM) accounts.  

Darktrace also identified devices performing SMB writes of the WinRAR data compression tool, in what likely represented preparation for the compression of data prior to data exfiltration. Further SMB file writes were observed around this time including PSEXESVC.exe, which was ultimately used by attackers to conduct remote code execution, and one device was observed making widespread failed NTLM authentication attempts on the network, indicating NTLM brute-forcing. Darktrace observed several devices using administrative credentials to carry out the above activity.

In addition to the transfer of tools and executables via SMB, Darktrace also identified numerous devices deleting files through SMB around this time. In one example, an MSI file associated with the patch management and remediation service, Action1, was deleted by an attacker. This legitimate security tool, if leveraged by attackers, could be used to uncover additional vulnerabilities on target networks.

A server on the customer’s network was also observed writing the file “m.exe” to multiple internal devices. OSINT investigation into the executable indicated that it could be a malicious tool used to prevent antivirus programs from launching or running on a network [8].

Impact and Data Exfiltration

Following the initial steps of the breach chain, Darktrace observed numerous devices on the customer’s network engaging in data exfiltration and impact events, resulting in additional PTN alerts and a SOC investigation into data egress. Specifically, two servers on the network proceeded to read and download large volumes of data via SMB from multiple internal devices over the course of a few hours. These hosts sent large outbound volumes of data to MEGA file storage sites using TLS/SSL over port 443. Darktrace also identified the use of additional file storage services during this exfiltration event, including 4sync, file[.]io, and easyupload[.]io. In total the threat actor exfiltrated over 8.5 GB of data from the customer’s network.

Darktrace Cyber AI Analyst investigation highlighting the details of a data exfiltration attempt.
Figure 2: Darktrace Cyber AI Analyst investigation highlighting the details of a data exfiltration attempt.

Finally, Darktrace detected a user account within the customer’s Software-as-a-Service (SaaS) environment conducting several suspicious Office365 and AzureAD actions from a rare IP for the network, including uncommon file reads, creations and the deletion of a large number of files.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network and the post-exploitation activity was able to progress until the customer was made aware of the attack by Darktrace’s SOC team. Had RESPOND been active and configured in autonomous response mode at the time of the attack, it would have been able to promptly contain the post-exploitation activity by blocking external connections, shutting down any C2 activity and preventing the download of suspicious files, blocking incoming traffic, and enforcing a learned ‘pattern of life’ on offending devices.

結論

Given the widespread use of Netscaler Gateway and Netscaler ADC, Citrix Bleed remains an impactful and potentially disruptive vulnerability that will likely continue to affect organizations who fail to address affected assets. In this instance, Darktrace demonstrated its ability to track and inhibit malicious activity stemming from Citrix Bleed exploitation, enabling the customer to identify affected devices and enact their own remediation.

Darktrace’s anomaly-based approach to threat detection allows it to identify such post-exploitation activity resulting from the exploitation of a vulnerability, regardless of whether it is a known CVE or a zero-day threat. Unlike traditional security tools that rely on existing threat intelligence and rules and signatures, Darktrace’s ability to identify the subtle deviations in a compromised device’s behavior gives it a unique advantage when it comes to identifying emerging threats.

Credit to Vivek Rajan, Cyber Analyst, Adam Potter, Cyber Analyst

付録

Darktrace モデルカバレッジ

Device / Suspicious SMB Scanning Activity

Device / ICMP Address Scan

Device / Possible SMB/NTLM Reconnaissance

Device / Network Scan

Device / SMB Lateral Movement

Device / Possible SMB/NTLM Brute Force

Device / Suspicious Network Scan Activity

User / New Admin Credentials on Server

Anomalous File / Internal::Unusual Internal EXE File Transfer

Compliance / SMB Drive Write

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Rare WinRM Incoming

Anomalous Connection / Unusual Admin SMB Session

Device / Unauthorised Device

User / New Admin Credentials on Server

Anomalous Server Activity / Outgoing from Server

Device / Long Agent Connection to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Device / New or Uncommon SMB Named Pipe

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compliance / Remote Management Tool On Server

Device / Anomalous RDP Followed By Multiple Model Breaches

Device / SMB Session Brute Force (Admin)

Device / New User Agent

Compromise / Large Number of Suspicious Failed Connections

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Enhanced Unusual External Data Transfer

Device / Increased External Connectivity

Unusual Activity / Unusual External Data to New Endpoints

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Compliance / Possible Unencrypted Password File On Server

Anomalous Connection / Suspicious Read Write Ratio and Rare External

Device / Reverse DNS Sweep]

Unusual Activity / Possible RPC Recon Activity

Anomalous File / Internal::Executable Uploaded to DC

Compliance / SMB Version 1 Usage

Darktrace AI Analyst Incidents

Scanning of Multiple Devices

Suspicious Remote Service Control Activity

SMB Writes of Suspicious Files to Multiple Devices

Possible SSL Command and Control to Multiple Devices

Extensive Suspicious DCE-RPC Activity

Suspicious DCE-RPC Activity

Internal Downloads and External Uploads

Unusual External Data Transfer

Unusual External Data Transfer to Multiple Related Endpoints

MITRE ATT&CK マッピング

Technique – Tactic – ID – Sub technique of

Network Scanning – Reconnaissance - T1595 - T1595.002

Valid Accounts – Defense Evasion, Persistence, Privilege Escalation, Initial Access – T1078 – N/A

Remote Access Software – Command and Control – T1219 – N/A

Lateral Tool Transfer – Lateral Movement – T1570 – N/A

Data Transfers – Exfiltration – T1567 – T1567.002

Compressed Data – Exfiltration – T1030 – N/A

NTLM Brute Force – Brute Force – T1110 - T1110.001

AntiVirus Deflection – T1553 - NA

Ingress Tool Transfer   - COMMAND AND CONTROL - T1105 - NA

Indicators of Compromise (IoCs)

204.155.149[.]37 – IP – Possible Malicious Endpoint

199.80.53[.]177 – IP – Possible Malicious Endpoint

168.100.9[.]137 – IP – Malicious Endpoint

45.134.26[.]2 – IP – Malicious Endpoint

13.35.147[.]18 – IP – Likely Malicious Endpoint

13.248.193[.]251 – IP – Possible Malicious Endpoint

76.223.1[.]166 – IP – Possible Malicious Endpoint

179.60.147[.]10 – IP – Likely Malicious Endpoint

185.220.101[.]25 – IP – Likely Malicious Endpoint

141.255.167[.]250 – IP – Malicious Endpoint

106.71.177[.]68 – IP – Possible Malicious Endpoint

cat2.hbwrapper[.]com – Hostname – Likely Malicious Endpoint

aj1090[.]online – Hostname – Likely Malicious Endpoint

dc535[.]4sync[.]com – Hostname – Likely Malicious Endpoint

204.155.149[.]140 – IP - Likely Malicious Endpoint

204.155.149[.]132 – IP - Likely Malicious Endpoint

204.155.145[.]52 – IP - Likely Malicious Endpoint

204.155.145[.]49 – IP - Likely Malicious Endpoint

参考文献

  1. https://www.axios.com/2024/01/02/citrix-bleed-security-hacks-impact
  2. https://www.csoonline.com/article/1267774/hackers-steal-data-from-millions-of-xfinity-customers-via-citrix-bleed-vulnerability.html
  3. https://www.cybersecuritydive.com/news/citrixbleed-security-critical-vulnerability/702505/
  4. https://www.virustotal.com/gui/ip-address/168.100.9.137
  5. https://www.virustotal.com/gui/ip-address/45.134.26.2
  6. https://www.trendmicro.com/en_us/research/24/b/threat-actor-groups-including-black-basta-are-exploiting-recent-.html
  7. https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-325a
  8. https://www.file.net/process/m.exe.html
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著者について
Vivek Rajan
Cyber Analyst

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How to Protect your Organization Against Microsoft Teams Phishing Attacks

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21
May 2024

The problem: Microsoft Teams phishing attacks are on the rise

Around 83% of Fortune 500 companies rely on Microsoft Office products and services1, with Microsoft Teams and Microsoft SharePoint in particular emerging as critical platforms to the business operations of the everyday workplace. Researchers across the threat landscape have begun to observe these legitimate services being leveraged more and more by malicious actors as an initial access method.

As Teams becomes a more prominent feature of the workplace many employees rely on it for daily internal and external communication, even surpassing email usage in some organizations. As Microsoft2 states, "Teams changes your relationship with email. When your whole group is working in Teams, it means you'll all get fewer emails. And you'll spend less time in your inbox, because you'll use Teams for more of your conversations."

However, Teams can be exploited to send targeted phishing messages to individuals either internally or externally, while appearing legitimate and safe. Users might receive an external message request from a Teams account claiming to be an IT support service or otherwise affiliated with the organization. Once a user has accepted, the threat actor can launch a social engineering campaign or deliver a malicious payload. As a primarily internal tool there is naturally less training and security awareness around Teams – due to the nature of the channel it is assumed to be a trusted source, meaning that social engineering is already one step ahead.

Screenshot of a Microsoft Teams message request from a Midnight Blizzard-controlled account (courtesy of Microsoft)
Figure 1: Screenshot of a Microsoft Teams message request from a Midnight Blizzard-controlled account (courtesy of Microsoft)

Microsoft Teams Phishing Examples

Microsoft has identified several major phishing attacks using Teams within the past year.

In July 2023, Microsoft announced that the threat actor known as Midnight Blizzard – identified by the United States as a Russian state-sponsored group – had launched a series of phishing campaigns via Teams with the aim of stealing user credentials. These attacks used previously compromised Microsoft 365 accounts and set up new domain names that impersonated legitimate IT support organizations. The threat actors then used social engineering tactics to trick targeted users into sharing their credentials via Teams, enabling them to access sensitive data.  

At a similar time, threat actor Storm-0324 was observed sending phishing lures via Teams containing links to malicious SharePoint-hosted files. The group targeted organizations that allow Teams users to interact and share files externally. Storm-0324’s goal is to gain initial access to hand over to other threat actors to pursue more dangerous follow-on attacks like ransomware.

Darktrace がMicrosoft Teamsのフィッシングを阻止する方法について、さらに詳しく知りたい方は、ブログをお読みください: 餌に喰いつくな:Darktrace Microsoft Teamsのフィッシング攻撃を阻止する方法

The market: Existing Microsoft Teams security solutions are insufficient

Microsoft’s native Teams security focuses on payloads, namely links and attachments, as the principal malicious component of any phishing. These payloads are relatively straightforward to detect with their experience in anti-virus, sandboxing, and IOCs. However, this approach is unable to intervene before the stage at which payloads are delivered, before the user even gets the chance to accept or deny an external message request. At the same time, it risks missing more subtle threats that don’t include attachments or links – like early stage phishing, which is pure social engineering – or completely new payloads.

Equally, the market offering for Teams security is limited. Security solutions available on the market are always payload-focused, rather than taking into account the content and context in which a link or attachment is sent. Answering questions like:

  • Does it make sense for these two accounts to speak to each other?
  • Are there any linguistic indicators of inducement?

Furthermore, they do not correlate with email to track threats across multiple communication environments which could signal a wider campaign. Effectively, other market solutions aren’t adding extra value – they are protecting against the same types of threats that Microsoft is already covering by default.

The other aspect of Teams security that native and market solutions fail to address is the account itself. As well as focusing on Teams threats, it’s important to analyze messages to understand the normal mode of communication for a user, and spot when a user’s Teams activity might signal account takeover.

The solution: How Darktrace protects Microsoft Teams against sophisticated threats

With its biggest update to Darktrace/Email ever, Darktrace now offers support for Microsoft Teams. With that, we are bringing the same AI philosophy that protects your email and accounts to your messaging environment.  

Our Self-Learning AI looks at content and context for every communication, whether that’s sent in an email or Teams message. It looks at actual user behavior, including language patterns, relationship history of sender and recipient, tone and payloads, to understand if a message poses a threat. This approach allows Darktrace to detect threats such as social engineering and payloadless attacks using visibility and forensic capabilities that Microsoft security doesn’t currently offer, as well as early symptoms of account compromise.  

Unlike market solutions, Darktrace doesn’t offer a siloed approach to Teams security. Data and signals from Teams are shared across email to inform detection, and also with the wider Darktrace ActiveAI security platform. By correlating information from email and Teams with network and apps security, Darktrace is able to better identify suspicious Teams activity and vice versa.  

Interested in the other ways Darktrace/Email augments threat detection? Read our latest blog on how improving the quality of end-user reporting can decrease the burden on the SOC. To find our more about Darktrace's enduring partnership with Microsoft, click here.

参考文献

[1] Essential Microsoft Office Statistics in 2024

[2] Microsoft blog, Microsoft Teams and email, living in harmony, 2024

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Carlos Gray
Product Manager
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