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November 20, 2025

ゼロトラストコントロールとAI駆動の検知でOTリモートアクセスを管理

本稿では、現代のOTが可視性だけに頼ることはできない理由、そしてゼロトラストアクセスコントロールとAI駆動のビヘイビア検知と組み合わせることにより、リアルタイムの監視、アカウンタビリティ、安全なリモートアクセスを、オペレーションを混乱させることなく実現する方法について、今後の展望も見据えて解説します。
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.
Written by
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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20
Nov 2025

IT-OT統合へのシフト

近年、産業環境は相互接続が進み外部との連携により依存するようになりました。その結果、真にエアギャップされたOTシステムの現実味は薄れています。特に、OEMが管理するアセットを使用している、レガシー装置に対してリモート診断が必要となる、あるいは第三者のインテグレーターが頻繁に接続するケースなどでは難しいでしょう。

こうした連携は、デジタル変革戦略に基づくもの、あるいは運用効率目標のため、いずれの場合においてもOT環境をより接続された、より自動化された、よりITシステムと絡み合ったものにしつつあります。このような統合により新たな可能性が開かれますが、同時にOT環境は、従来のOTアーキテクチャが耐えるように設計されていないような、さまざまなリスクにさらされることになります。

最新化により生まれるギャップと可視性だけでは不十分な理由

最新化への取り組みにより新たなテクノロジーが産業環境にも導入され、IT環境とOT環境の統合とともに、可視性の欠如も生まれました。しかし、可視性を取り戻すことはスタート地点にすぎません。可視性は何が接続されているかを教えてくれるだけで、アクセスをどのように管理すべきかを教えてはくれません。そしてここがITとOTの分断が避けられなくなるポイントです。

ITではうまく機能するセキュリティ戦略もOTではしばしば不十分なことがあります。OT環境ではわずかな失敗が環境への危険性、安全に関する事故、あるいは多大なコストを伴う稼働の停止などにつながるからです。さらに、安全なアクセス、分割の徹底、説明責任などを求める法規制の高まりからの圧力が加わると、可視性だけではもはや不十分であるということが明確になります。産業環境に今必要なのは、精密性です。そこではコントロールが必要です。そして、オペレーションを中断させることなくその両方を実現する必要があります。それには、アイデンティティベースのアクセス制御、リアルタイムのセッション監視、そして継続的なビヘイビア検知が必要となります。

監視されていないリモートアクセスによるリスク

このリスクは、アセットの故障をトラブルシューティングするためにOEMが緊急にアクセスを必要とする場合など、重大なタイミングで現れます。

限られた時間というプレッシャーのなかで、アクセス権限はしばしば最小限の検証ですばやく付与され、決められたプロセスが省略されることがあります。一旦中に入れば、コマンドの実行、設定の変更、あるいはネットワーク内で水平移動するなど、ユーザーのアクションに対するリアルタイムの監視はないケースがほとんどです。こうしたアクションは多くの場合記録されず、あるいは何かが壊れるまで気づかれません。問題が起こると、チームは断片的なログをつなぎ合わせる作業やインシデント後のフォレンジック作業に追われますが、説明責任の経路は明確ではありません。

アップタイムが決定的に重要であり安全性が譲れない環境においてこのレベルの不透明性では、まったく持続可能ではありません。

可視性のギャップ:誰が何を、いつ行っているか?

私たちが直面している根本的な問題は、誰がアクセス権を持っているかということと、そのアクセス権で何が行われているかという現実がつながっていないことです。  

従来のアクセス管理ツールは認証情報を検証し、入り口を制限するかもしれませんが、セッション中のアクティビティについてリアルタイムの可視性を提供することは稀です。さらに、期待される振る舞いと、侵害、誤使用、設定間違いのかすかな兆候の違いを見分けられるものはさらに少ないでしょう。  

その結果、OTチームとセキュリティチームはしばしば、問題の最も重要なカギとなる、意図と動作が見えない状況に置かれます。

ゼロトラストコントロールとAI駆動の検知でギャップを解消

OTでのリモートアクセスを管理することは、接続権限を付与するだけの問題ではもはやありません。厳密なアクセスパラメーターを徹底すると同時に、異常な振る舞いを継続的に監視することが必要です。これには、精密なアクセスコントロールと、インテリジェントかつリアルタイムの検知という2つの側面からのアプローチが必要です。

ゼロトラストアクセスコントロールが基盤となります。アイデンティティベースの、ジャストインタイム型のアクセス権を適用することにより、OT環境において、外部ベンダーやリモートユーザーが明示的に操作を承認されたシステムに対してのみ、そして必要な時間のみアクセスできるよう徹底できます。これらのコントロールのは、特定のデバイス、コマンド、あるいは機能へのアクセスに制限できるだけの細かさが必要です。これらの原則をPurdueモデル全体に一貫して適用することにより、OT環境を過剰なリスクにさらしてしまうキャッチオール式のVPNトンネル、ジャンプサーバー、そして脆いファイアウォール例外などへの依存を解消することができます。

アクセスコントロールは方程式の1部にすぎない

Darktrace / OT は継続的なAI駆動のビヘイビア検知でゼロトラストコントロールを補強します。静的なルールや事前定義済みのシグネチャに依存する代わりに、Darktraceは自己学習型AIを使用して、あらゆるデバイス、プロトコル、ユーザーに渡る環境全体で何が"正常”かについての、リアルタイムの、変化し続ける理解を構築します。これにより、微細な設定ミス、認証情報の間違った使用、あるいは水平移動を、後から知るのではなく発生と同時にリアルタイムに検知することができます。

ユーザーのアイデンティティとセッション内のアクティビティを、ビヘイビア分析と相関付けることによりDarktraceは全体像を明らかにし、誰がどのシステムにアクセスしたか、どのようなアクションを実行したか、それらのアクションはこれまでの通常状態と比較してどうか、そして逸脱が発生したかどうかを知ることができます。リモートアクセスセッションに関連する当て推量を取り除き、明確な、コンテキストを含めた情報を提供します。

重要な点は、Darktraceがオペレーション内のノイズと本物のサイバー脅威に関連した異常を区別することです。CVEアラートから日常的なアクティビティまですべてを1つのストリームにまとめてしまう他のツールとは異なり、Darktraceは正しいリモートアクセス動作とミスや乱用の可能性を区別します。つまり、組織はコンプライアンスの観点からアクセスを監査できるとともに、セッションがもしエクスプロイトされていれば、その不正な使用は、高確度なサイバー脅威に関連したアラートとして確認できることを意味します。このアプローチはコントロールを補完するものとして利用することができ、もしアクセス権が過剰に拡大されている、あるいは間違って利用されている場合にも、その挙動を可視化し、それに対するアクションが可能です。

たとえば、セッションにおいて、普段とは異なるコマンドシーケンス、新たな水平移動経路、あるいはスケジュールされた時間帯以外のアクティビティが発生するなど、学習したベースラインを逸脱した場合、Darktraceは即座にフラグを立てることができます。これらの情報を基に、人手による調査を開始する、あるいはアクセス権のはく奪やセッション隔離などポリシーに応じて自動的にアクションをトリガーするなどが可能です。

この多層的なアプローチにより、リアルタイムの意思決定が可能になり、中断のないオペレーションが確保され、重要な作業を遅らせたりワークフローを中断したりすることなくあらゆるリモートアクティビティに対して完全な説明責任を担保することができます。

ゼロトラストアクセスとAI駆動の監視の組み合わせ:

  • きめ細かいアクセス適用: ゼロトラスト原則に従いコンプライアンスの要件を満たす、ロールベースの、ジャストインタイムのアクセス。 
  • コンテキストを加えた脅威検知: 自己学習型AIが異常なOT動作をリアルタイムに検知し、脅威をアクセスイベントとユーザーアクティビティに結びつける。 
  • 自動化されたセッション管理: 動作の異常によってアラートや自動制御をトリガーすることができ、アップタイムを維持しつつ封じ込めまでの時間を短縮。
  • Purdueレイヤー全体に渡る完全な可視性: 相関付けされたデータにより、IT、OTレイヤー全体にわたりリモートアクセスイベントをデバイスレベルの動作と結びつけることが可能。
  • スケーラブルかつ受動的な監視: 動作を受動的に学習することによりレガシーシステムやエアギャップされた環境全体をカバーすることが可能、シグネチャやエージェント、侵入型スキャンは必要なし。

妥協のない完全なセキュリティ

オペレーションの敏捷性かそれともセキュリティコントロールか、あるいは可視性かそれとも簡潔性か、これらのどちらかを選ぶ必要はもうありません。ゼロトラストアプローチをリアルタイムのAI検知で強化することにより、権限と動作の両方を認識し、産業オペレーションの現実に即した、多様な環境にスケール可能な、安全なリモートアクセスを実現することができます。

重要インフラの保護において、検知を伴わないアクセスはリスクであり、アクセスコントロールを伴わない検知は不完全だからです。

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.
Written by
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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April 1, 2026

AI-powered security for a rapidly growing grocery enterprise

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Protecting a complex, fast-growing retail organization

For this multi-banner grocery holding organization, cybersecurity is considered an essential business enabler, protecting operations, growth, and customer trust. The organization’s lean IT team manages a highly distributed environment spanning corporate offices, 100+ stores, distribution centers and  thousands of endpoints, users, and third-party connections.

Mergers and acquisitions fueled rapid growth, but they also introduced escalating complexity that constrained visibility into users, endpoints, and security risks inherited across acquired environments.

Closing critical visibility gaps with limited resources

Enterprise-wide visibility is a top priority for the organization, says the  Vice President of Information Technology. “We needed insights beyond the perimeter into how users and devices were behaving across the organization.”

A security breach that occurred before the current IT leadership joined the company reinforced the urgency and elevated cybersecurity to an executive-level priority with a focus on protecting customer trust. The goal was to build a multi-layered security model that could deliver autonomous, enterprise-wide protection without adding headcount.

Managing cyber risk in M&A

Mergers and acquisitions are central to the grocery holding company’s growth strategy. But each transaction introduces new cyber risk, including inherited network architectures, inconsistent tooling, excessive privileges, and remnants of prior security incidents that were never fully remediated.

“Our M&A targets range from small chains with a single IT person and limited cyber tools to large chains with more developed IT teams, toolsets and instrumentation,” explains the VP of IT. “We needed a fast, repeatable, and reliable way to assess cyber risk before transactions closed.”

AI-driven security built for scale, speed, and resilience

Rather than layering additional point tools onto an already complex environment, the retailer adopted the Darktrace ActiveAI Security Platform™ in 2020 as part of a broader modernization effort to improve resilience, close visibility gaps, and establish a security foundation that could scale with growth.

“Darktrace’s AI-driven approach provided the ideal solution to these challenges,” shares the VP of IT. “It has empowered our organization to maintain a robust security strategy, ensuring the protection of our network and the smooth operation of our business.”

Enterprise-wide visibility into traffic  

By monitoring both north-south and east-west traffic and applying Self-Learning AI, Darktrace develops a dynamic understanding of how users and devices normally behave across locations, roles, and systems.

“Modeling normal behavior across the environment enables us to quickly spot behavior that doesn’t fit. Even subtle changes that could signal a threat but appear legitimate at first glance,” explains the VP of IT.

Real-time threat containment, 24/7

Adopting autonomous response has created operational breathing room for the security team, says the company’s Cybersecurity  Engineer.

“Early on, we enabled full Darktrace autonomous mode and we continue to do so today,” shares the IT Security Architect. “Allowing the technology to act first gives us the time we need to investigate incidents during business hours without putting the business at risk.”

Unified, actionable view of security ecosystem

The grocery retailer integrated Darktrace with its existing security ecosystem of firewalls, vulnerability management tools, and endpoint detection and response, and the VP of IT described the adoption process as “exceptionally smooth.”

The team can correlate enterprise-wide security data for a unified and actionable picture of all activity and risk. Using this “single pane of glass” approach, the retailer trains Level 1 and Level 2 operations staff to assist with investigations and user follow-ups, effectively extending the reach of the security function without expanding headcount.

From reactive defense to security at scale

With Darktrace delivering continuous visibility, autonomous containment, and integrated security workflows, the organization has strengthened its cybersecurity posture while improving operational efficiency. The result is a security model that not only reduces risk, but also supports growth, resilience, and informed decision-making at the business level.

Faster detection, faster resolution

With autonomous detection and response, the retailer can immediately contain risk while analysts investigate and validate activity. With this approach, the company can maintain continuous protection even outside business hours and reduce the chance of lateral spread across systems or locations.

Enterprise-grade protection with a lean team

From cloud environments to clients to SaaS collaboration tools, Darktrace provides holistic autonomous AI defense, processing petabytes of the organization’s network traffic and investigating millions of individual events that could be indicative of a wider incident.

Today, Darktrace autonomously conducts the majority of all investigations on behalf of the IT team, escalating only a tiny fraction for analyst review. The impact has been profound, freeing analysts from endless alerts and hours of triage so they can focus on more valuable, proactive, and gratifying work.

“From an operational perspective, Darktrace gives us time back,” says the Cybersecurity Engineer. More importantly, says the VP of IT, “it gives us peace of mind that we’re protected even if we’re not actively monitoring every alert.”

A strategic input for M&A decision-making

One of the most strategic outcomes has been the role of cybersecurity on M&A. 90 days prior to closing a transaction, the security team uses Darktrace alongside other tools to perform a cyber risk assessment of the potential acquisition. “Our approach with Darktrace has consistently identified gaps and exposed risks,” says the VP of IT, including:

  • Remnants of previous incidents that were never fully remediated
  • Network configurations with direct internet exposure
  • Excessive administrative privileges in Active Directory or on critical hosts

While security findings may not alter deal timelines, the VP of IT says they can have enormous business implications. “With early visibility into these risks, we can reduce exposure to inherited cyber threats, strengthen our position during negotiations, and establish clear remediation requirements.”

A security strategy built to evolve with the business

As the holding group expands its cloud footprint, it will extend Darktrace protections into Azure, applying the same AI-driven visibility and autonomous response to cloud workloads. The VP of IT says Darktrace's evolving capabilities will be instrumental in addressing the organization’s future cybersecurity needs and ability to adapt to the dynamic nature of cloud security.

“With Darktrace’s AI-driven approach, we have moved beyond reactive defense, establishing a resilient security foundation for confident expansion and modernization.”

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March 31, 2026

Phantom Footprints: Tracking GhostSocks Malware

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Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work? 

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.
Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.
Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

 Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.
Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.
Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.
Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.
Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.
Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices

References

1.    https://bloo.io/research/malware/ghostsocks

2.    https://www.virustotal.com/gui/domain/retreaw.click/community

3.    https://synthient.com/blog/ghostsocks-from-initial-access-to-residential-proxy

4.    https://www.joesandbox.com/analysis/1810568/0/html

5. https://www.virustotal.com/gui/url/fab6525bf6e77249b74736cb74501a9491109dc7950688b3ae898354eb920413

Darktrace Model Detections

Real-time Detection Models

Anomalous Connection / Suspicious Self-Signed SSL

Anomalous Connection / Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Possible Fast Flux C2 Activity

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Compromise / Sustained SSL or HTTP Increase

Autonomous Response Models

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique

Resource Development – T1588 - Malware

Initial Access - T1189 - Drive-by Compromise

Persistence – T1112 – Modify Registry

Command and Control – T1071 – Application Layer Protocol

Command and Control – T1095 – Non-application Layer Protocol

Command and Control – T1071 – Web Protocols

Command and Control – T1571 – Non-Standard Port

Command and Control – T1102 – One-Way Communication

List of Indicators of Compromise (IoCs)

86.54.24[.]29 - IP - Likely GhostSocks C2

http[://]86.54.24[.]29/Renewable[.]exe - Hostname - GhostSocks Distribution Endpoint

http[://]d2ihv8ymzp14lr.cloudfront[.]net/2021-08-19/udppump[.]exe - CDN - Payload Distribution Endpoint

www.lbfs[.]site - Hostname - Likely C2 Endpoint

retreaw[.]click - Hostname - Lumma C2 Endpoint

alltipi[.]com - Hostname - Possible C2 Endpoint

w2.bruggebogeyed[.]site - Hostname - Possible C2 Endpoint

9b90c62299d4bed2e0752e2e1fc777ac50308534 - SHA1 file hash – Likely GhostSocks payload

3d9d7a7905e46a3e39a45405cb010c1baa735f9e - SHA1 file hash - Likely follow-up payload

10f928e00a1ed0181992a1e4771673566a02f4e3 - SHA1 file hash - Likely follow-up payload

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About the author
Isabel Evans
Cyber Analyst
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