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Ransomware
REvilのRansomware-as-a-Service(RaaS)ビジネスモデルに対する備え







REvil(Sodinokibiとしても知られています)は、過去最大のランサムウェア攻撃の1つを実行したRansomware-as-a-Service (RaaS) ギャングです。2022年1月14日、この犯罪集団の構成員14名を逮捕したとロシアが発表 しました。これは、国際的強力によりREvil撲滅に力を注いだ米国当局の求めに応じたものでした。昨年、大きな話題となった複数の攻撃がREvil グループによるものとされており、これにはJBSランサムウェアおよび Kaseya サプライチェーンインシデントも含まれています。
REvil 構成員の逮捕は西側の法執行機関にとって勝利であることは確かであり、昨年11月には欧州刑事警察機構がREvil関連グループに対して数か月間に7件の逮捕を行ったと発表しています。問題は、これらの逮捕がREvilのオペレーションをどの程度まで、またどのくらいの期間、中断させることができるのか、ということです。
ReversingLabs研究所の 調査が示す早期の兆候によれば、REvilの活動にこれまでのところ影響は出ていません。ロシアによる逮捕から2週間後のREvilインプラントが示す統計情報に変化はなく、むしろ穏やかな増加が見られるほどです。
アクティビティの継続は次のシナリオのいずれかを意味しています:
- 一連の逮捕は犯罪集団のヒエラルキーの中の中間層にしか影響していない
- REvilのRaaSモデルは法執行機関による妨害に耐える十分なレジリエンスを有している
ランサムウェアギャングの襲撃に遭うかもしれない人達にとってはどちらも心配なシナリオであり、現実はこれらに加えてその他の要素が複雑に混じり合ったものとなるでしょう。ランサムウェアの撲滅は長年の懸案ですが、この戦いは長くなることが予想されます。法執行機関に必要なのは、ランサムウェアビジネスを手がけることがもはや利益を生む、あるいは有利なことではなくなるほどに、ビジネスモデルを破壊することです。そしてこれには何か月も、あるいは何年もかかるでしょう。
さて、ランサムウェアの撲滅活動が大きな舞台で展開されつつあるなか、最近の出来事から、セキュリティチームにとって少しでも安心できる材料はないでしょうか?
進化するRaaSモデルにAIで対抗
FBI、CISA、NCSC、ACSC、NSA が最近共同で発表したランサムウェアについての報告書では、昨年の主要な傾向が解説されています:
- RaaSはビジネスモデルおよびプロセスが十分に確立され、ますますプロフェッショナル度が高まっています。
- 開発者、アフィリエイト、フリーランスなどで構成される複雑なネットワークの存在により、このモデルはアトリビューションを複雑にしています。
- ランサムウェアグループは標的についての情報を相互に共有し、被害組織に対する脅威を多様化させています。
まとめると、この報告書では、ランサムウェアギャングが法執行機関の追跡を回避し身代金の支払いを最大化するために、ますます適応性を高めているということが解説されています。複数のグループが消滅し、あるいは解散しましたが、結局は別の名前と若干更新されたプレイブックとともに再出現しています。戦術、テクニック、手順(TTP)は被害を受けた組織によってさまざまですが、それは主にこれらの攻撃が異なるランサムウェアグループやアフィリエイトによって実行されていることによります。
これらの攻撃の背後にいる者たちを摘発しようとしている法執行機関にとって、これはやっかいです。REvil のようなRaaSグループを構成する関係者のネットワークは決まった形を持たず常に変化しているため、個別に逮捕してもいたちごっこの連続となり、グループ全体を壊滅させることにはならないでしょう。
それぞれの攻撃事例においても同様の戦いが展開しています。以前に遭遇した脅威の特徴に的を絞ったセキュリティツールも、いつまで経っても彼らに追いつけません。1つの攻撃が検知され、特徴が記録され、次回に備えて保存されるころには、攻撃者とそのテクニックはその先に進んでいます。
しかし防御側には別の選択肢もあります。ますます多くのセキュリティチームが、攻撃者に対抗するために自己学習型AIを取り入れているのです。自己学習型AIはその周囲環境を学習し、攻撃の兆候であるかすかな変化を識別することにより、新種の攻撃に対しても初回遭遇時に検知し対処することができます。以下に、REvil によって実行された攻撃を、自己学習型AIがルールやシグネチャを使用することなくどのように検知したかを示す実例を紹介します。
REvil脅威検知結果
2021年夏、REvil のアフィリエイトがある医療および介護業界の組織に攻撃を仕掛けました。このセクターは、世界的なパンデミックが発生して以来サイバー攻撃が大幅に増加しています。この攻撃はルールやシグネチャを使うことなくDarktraceのAIによって検知されましたが、セキュリティチームはその当時、Darktraceを監視していませんでした。的を絞ったアクションにより脅威を封じ込めることのできたAutonomous Responseが適用されていなかったため、この攻撃は進行してしまいました。
1人のリモートワーカーのラップトップPCからネットワークにアクセスした攻撃者は、正当なRDP(Remote Desktop)接続を悪用してこの企業のジャンプサーバーに移動し、ブルートフォースで認証情報を抽出しました。
多数の認証情報を入手した攻撃者は、RDPを使って2台目のジャンプサーバーを含む複数の内部デバイスに接続しました。最初に侵入されたサーバーから、RDPポート3389番を使ってデータ抜き出しが開始されました。
2週間後、攻撃者は3台目のサーバーに格納されていたこの組織の重要情報を特定し、C2(コマンド&コントロール)通信の開始を試みました。このサーバーは多数の不審な外部接続を行い、これにはREvilが以前行ったKaseya ランサムウェア攻撃と関係したアクティビティのパターンに似た、未知のドメインへの接続試行が含まれていました。
リモートユーザーのデバイス上で実行されていた Darktrace for Endpointがこれらに対する可視性を提供し、セキュリティチームは最初に侵入されたユーザーのデバイスを特定することができました。もしこのエンドポイント上でAntigenaが適用されていれば、普段と異なる特定の接続をブロックすることでこの不審なアクティビティを中断させ、通常の業務に影響を与えることなく攻撃を封じ込めることができたでしょう。
ローアンドスロー攻撃の点と点を結ぶ
この攻撃者の合計滞留時間は22日間でした。彼らは忍耐強く、多くの場合数日の間隔を空けて、まとまったアクティビティでアクションを実行していました。この行動パターンはランサムウェア攻撃では珍しくなく、特にRaaSモデルを使ったものでは、各ステップを別のメンバーやアフィリエイトが実行する場合もあるためです。
DarktraceのCyber AI Analystは数週間に渡る攻撃ライフサイクルのすべてをリアルタイムに追跡し、攻撃の各フェーズをつなぎ合わせてセキュリティインシデント全体を構成しました。

図1:Cyber AI Analystが攻撃キルチェーン全体を明らかに
新しい名前、同じ手口
この攻撃も、攻撃者達がLiving off the Land (環境に寄生する)手法を使っている一例です。これは環境内で既に使われていた正当なプログラムやプロセスを使って悪意あるアクティビティを実行する手法です。これらを、静的なユースケースに基づいた、正当なRDPセッションと悪意あるセッションの区別ができない、従来のツールで検知することは非常に困難でしょう。
REvilのようなサイバー犯罪グループが引き続き法執行機関の摘発を逃れている現在、防御側も環境を学習し、その変化に適応して成長し、攻撃の出現を示すわずかな変化に基づいて脅威に対処できるAIテクノロジーを使って対抗する必要があります。Autonomous Responseは数千を超える組織に採用され、Eメールやクラウドサービスからエンドポイントデバイスに至るまでデジタルエステートのあらゆるエリアをカバーし、ランサムウェア攻撃を早期に、暗号化が行われる前に阻止しています。
この脅威事例についての考察はDarktraceアナリストPetal Beharry が協力しました。
技術的詳細
Darktraceによるモデル検知:
- Device / RDP Scan
- Device / Bruteforce Activity
- Compliance / Outbound Remote Desktop
- Anomalous Connection / Upload via Remote Desktop
- Anomalous Connection / Download and Upload
- Anomalous Connection / Uncommon 1 GiB Outbound
- Anomalous Connection / Active Remote Desktop Tunnel
- Device / New or Uncommon SMB Named Pipe
- Device / Large Number of Connections to New Endpoints
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Inside the SOC
PurpleFox in a Henhouse: How Darktrace Hunted Down a Persistent and Dynamic Rootkit



Versatile Malware: PurpleFox
As organizations and security teams across the world move to bolster their digital defenses against cyber threats, threats actors, in turn, are forced to adopt more sophisticated tactics, techniques and procedures (TTPs) to circumvent them. Rather than being static and predictable, malware strains are becoming increasingly versatile and therefore elusive to traditional security tools.
One such example is PurpleFox. First observed in 2018, PurpleFox is a combined fileless rootkit and backdoor trojan known to target Windows machines. PurpleFox is known for consistently adapting its functionalities over time, utilizing different infection vectors including known vulnerabilities (CVEs), fake Telegram installers, and phishing. It is also leveraged by other campaigns to deliver ransomware tools, spyware, and cryptocurrency mining malware. It is also widely known for using Microsoft Software Installer (MSI) files masquerading as other file types.
The Evolution of PurpleFox
The Original Strain
First reported in March 2018, PurpleFox was identified to be a trojan that drops itself onto Windows machines using an MSI installation package that alters registry values to replace a legitimate Windows system file [1]. The initial stage of infection relied on the third-party toolkit RIG Exploit Kit (EK). RIG EK is hosted on compromised or malicious websites and is dropped onto the unsuspecting system when they visit browse that site. The built-in Windows installer (MSIEXEC) is leveraged to run the installation package retrieved from the website. This, in turn, drops two files into the Windows directory – namely a malicious dynamic-link library (DLL) that acts as a loader, and the payload of the malware. After infection, PurpleFox is often used to retrieve and deploy other types of malware.
Subsequent Variants
Since its initial discovery, PurpleFox has also been observed leveraging PowerShell to enable fileless infection and additional privilege escalation vulnerabilities to increase the likelihood of successful infection [2]. The PowerShell script had also been reported to be masquerading as a .jpg image file. PowerSploit modules are utilized to gain elevated privileges if the current user lacks administrator privileges. Once obtained, the script proceeds to retrieve and execute a malicious MSI package, also masquerading as an image file. As of 2020, PurpleFox no longer relied on the RIG EK for its delivery phase, instead spreading via the exploitation of the SMB protocol [3]. The malware would leverage the compromised systems as hosts for the PurpleFox payloads to facilitate its spread to other systems. This mode of infection can occur without any user action, akin to a worm.
The current iteration of PurpleFox reportedly uses brute-forcing of vulnerable services, such as SMB, to facilitate its spread over the network and escalate privileges. By scanning internet-facing Windows computers, PurpleFox exploits weak passwords for Windows user accounts through SMB, including administrative credentials to facilitate further privilege escalation.
Darktrace detection of PurpleFox
In July 2023, Darktrace observed an example of a PurpleFox infection on the network of a customer in the healthcare sector. This observation was a slightly different method of downloading the PurpleFox payload. An affected device was observed initiating a series of service control requests using DCE-RPC, instructing the device to make connections to a host of servers to download a malicious .PNG file, later confirmed to be the PurpleFox rootkit. The device was then observed carrying out worm-like activity to other external internet-facing servers, as well as scanning related subnets.
Darktrace DETECT™ was able to successfully identify and track this compromise across the cyber kill chain and ensure the customer was able to take swift remedial action to prevent the attack from escalating further.
While the customer in question did have Darktrace RESPOND™, it was configured in human confirmation mode, meaning any mitigative actions had to be manually applied by the customer’s security team. If RESPOND had been enabled in autonomous response mode at the time of the attack, it would have been able to take swift action against the compromise to contain it at the earliest instance.
攻撃の概要

Initial Scanning over SMB
On July 14, 2023, Darktrace detected the affected device scanning other internal devices on the customer’s network via port 445. The numerous connections were consistent with the aforementioned worm-like activity that has been reported from PurpleFox behavior as it appears to be targeting SMB services looking for open or vulnerable channels to exploit.
This initial scanning activity was detected by Darktrace DETECT, specifically through the model breach ‘Device / Suspicious SMB Scanning Activity’. Darktrace’s Cyber AI Analyst™ then launched an autonomous investigation into these internal connections and tied them into one larger-scale network reconnaissance incident, rather than a series of isolated connections.

As Darktrace RESPOND was configured in human confirmation mode, it was unable to autonomously block these internal connections. However, it did suggest blocking connections on port 445, which could have been manually applied by the customer’s security team.

特権昇格
The device successfully logged in via NTLM with the credential, ‘administrator’. Darktrace recognized that the endpoint was external to the customer’s environment, indicating that the affected device was now being used to propagate the malware to other networks. Considering the lack of observed brute-force activity up to this point, the credentials for ‘administrator’ had likely been compromised prior to Darktrace’s deployment on the network, or outside of Darktrace’s purview via a phishing attack.
Exploitation
Darktrace then detected a series of service control requests over DCE-RPC using the credential ‘admin’ to make SVCCTL Create Service W Requests. A script was then observed where the controlled device is instructed to launch mshta.exe, a Windows-native binary designed to execute Microsoft HTML Application (HTA) files. This enables the execution of arbitrary script code, VBScript in this case.


There are a few MSIEXEC flags to note:
- /i : installs or configures a product
- /Q : sets the user interface level. In this case, it is set to ‘No UI’, which is used for “quiet” execution, so no user interaction is required
Evidently, this was an attempt to evade detection by endpoint users as it is surreptitiously installed onto the system. This corresponds to the download of the rootkit that has previously been associated with PurpleFox. At this stage, the infected device continues to be leveraged as an attack device and scans SMB services over external endpoints. The device also appeared to attempt brute-forcing over NTLM using the same ‘administrator’ credential to these endpoints. This activity was identified by Darktrace DETECT which, if enabled in autonomous response mode would have instantly blocked similar outbound connections, thus preventing the spread of PurpleFox.

Installation
On August 9, Darktrace observed the device making initial attempts to download a malicious .PNG file. This was a notable change in tactics from previously reported PurpleFox campaigns which had been observed utilizing .MOE files for their payloads [3]. The .MOE payloads are binary files that are more easily detected and blocked by traditional signatured-based security measures as they are not associated with known software. The ubiquity of .PNG files, especially on the web, make identifying and blacklisting the files significantly more difficult.
The first connection was made with the URI ‘/test.png’. It was noted that the HTTP method here was HEAD, a method similar to GET requests except the server must not return a message-body in the response.
The metainformation contained in the HTTP headers in response to a HEAD request should be identical to the information sent in response to a GET request. This method is often used to test hypertext links for validity and recent modification. This is likely a way of checking if the server hosting the payload is still active. Avoiding connections that could possibly be detected by antivirus solutions can help keep this activity under-the-radar.


The server responds with a status code of 200 before the download begins. The HEAD request could be part of the attacker’s verification that the server is still running, and that the payload is available for download. The ‘/test.png’ HEAD request was sent twice, likely for double confirmation to begin the file transfer.

Subsequent analysis using a Packet Capture (PCAP) tool revealed that this connection used the Windows Installer user agent that has previously been associated with PurpleFox. The device then began to download a payload that was masquerading as a Microsoft Word document. The device was thus able to download the payload twice, from two separate endpoints.
By masquerading as a Microsoft Word file, the threat actor was likely attempting to evade the detection of the endpoint user and traditional security tools by passing off as an innocuous text document. Likewise, using a Windows Installer user agent would enable threat actors to bypass antivirus measures and disguise the malicious installation as legitimate download activity.
Darktrace DETECT identified that these were masqueraded file downloads by correctly identifying the mismatch between the file extension and the true file type. Subsequently, AI Analyst was able to correctly identify the file type and deduced that this download was indicative of the device having been compromised.
In this case, the device attempted to download the payload from several different endpoints, many of which had low antivirus detection rates or open-source intelligence (OSINT) flags, highlighting the need to move beyond traditional signature-base detections.



If Darktrace RESPOND was enabled in autonomous response mode at the time of the attack it would have acted by blocking connections to these suspicious endpoints, thus preventing the download of malicious files. However, as RESPOND was in human confirmation mode, RESPOND actions required manual application by the customer’s security team which unfortunately did not happen, as such the device was able to download the payloads.
結論
The PurpleFox malware is a particularly dynamic strain known to continually evolve over time, utilizing a blend of old and new approaches to achieve its goals which is likely to muddy expectations on its behavior. By frequently employing new methods of attack, malicious actors are able to bypass traditional security tools that rely on signature-based detections and static lists of indictors of compromise (IoCs), necessitating a more sophisticated approach to threat detection.
Darktrace DETECT’s Self-Learning AI enables it to confront adaptable and elusive threats like PurpleFox. By learning and understanding customer networks, it is able to discern normal network behavior and patterns of life, distinguishing expected activity from potential deviations. This anomaly-based approach to threat detection allows Darktrace to detect cyber threats as soon as they emerge.
By combining DETECT with the autonomous response capabilities of RESPOND, Darktrace customers are able to effectively safeguard their digital environments and ensure that emerging threats can be identified and shut down at the earliest stage of the kill chain, regardless of the tactics employed by would-be attackers.
Credit to Piramol Krishnan, Cyber Analyst, Qing Hong Kwa, Senior Cyber Analyst & Deputy Team Lead, Singapore
付録
Darktraceによるモデル検知
- Device / Increased External Connectivity
- Device / Large Number of Connections to New Endpoints
- Device / SMB Session Brute Force (Admin)
- Compliance / External Windows Communications
- Anomalous Connection / New or Uncommon Service Control
- Compromise / Unusual SVCCTL Activity
- Compromise / Rare Domain Pointing to Internal IP
- Anomalous File / Masqueraded File Transfer
RESPOND Models
- Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
- Antigena / Network / External Threat / Antigena Suspicious Activity Block
- Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
- Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block
- Antigena / Network / External Threat / Antigena Suspicious File Block
- Antigena / Network / External Threat / Antigena File then New Outbound Block
IoC一覧
IoC - Type - Description
/C558B828.Png - URI - URI for Purple Fox Rootkit [4]
5b1de649f2bc4eb08f1d83f7ea052de5b8fe141f - File Hash - SHA1 hash of C558B828.Png file (Malware payload)
190.4.210[.]242 - IP - Purple Fox C2 Servers
218.4.170[.]236 - IP - IP for download of .PNG file (Malware payload)
180.169.1[.]220 - IP - IP for download of .PNG file (Malware payload)
103.94.108[.]114:10837 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
221.199.171[.]174:16543 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
61.222.155[.]49:14098 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
178.128.103[.]246:17880 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
222.134.99[.]132:12539 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
164.90.152[.]252:18075 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
198.199.80[.]121:11490 - IP - IP from Service Control MSIEXEC script to download PNG file (Malware payload)
MITRE ATT&CK マッピング
Tactic - Technique
Reconnaissance - Active Scanning T1595, Active Scanning: Scanning IP Blocks T1595.001, Active Scanning: Vulnerability Scanning T1595.002
Resource Development - Obtain Capabilities: Malware T1588.001
Initial Access, Defense Evasion, Persistence, Privilege Escalation - Valid Accounts: Default Accounts T1078.001
Initial Access - Drive-by Compromise T1189
Defense Evasion - Masquerading T1036
Credential Access - Brute Force T1110
Discovery - Network Service Discovery T1046
Command and Control - Proxy: External Proxy T1090.002
参考文献
- https://blog.360totalsecurity.com/en/purple-fox-trojan-burst-out-globally-and-infected-more-than-30000-users/
- https://www.trendmicro.com/en_us/research/19/i/purple-fox-fileless-malware-with-rookit-component-delivered-by-rig-exploit-kit-now-abuses-powershell.html
- https://www.akamai.com/blog/security/purple-fox-rootkit-now-propagates-as-a-worm
- https://www.foregenix.com/blog/an-overview-on-purple-fox
- https://www.trendmicro.com/en_sg/research/21/j/purplefox-adds-new-backdoor-that-uses-websockets.html
Blog
OT
$70 Million in Cyber Security Funding for Electric Cooperatives & Utilities



What is the Bipartisan Infrastructure Deal?
The Bipartisan Infrastructure Law passed by congress in 2021 aimed to upgrade power and infrastructure to deliver clean, reliable energy across the US to achieve zero-emissions. To date, the largest investment in clean energy, the deal will fund new programs to support the development and deployment of clean energy technology.
Why is it relevant to electric municipalities?
Section 40124 of the Bipartisan Infrastructure Law allocates $250 million over a 5-year period to create the Rural and Municipal Utility Cybersecurity (RMUC) Program to help electric cooperative, municipal, and small investor-owned utilities protect against, detect, respond to, and recover from cybersecurity threats.1 This act illuminates the value behind a full life-cycle approach to cyber security. Thus, finding a cyber security solution that can provide all aspects of security in one integrated platform would enhance the overall security posture and ease many of the challenges that arise with adopting multiple point solutions.
On November 16, 2023 the Office of Cybersecurity, Energy Security, and Emergency Response (CESER) released the Advanced Cybersecurity Technology (ACT) for electric utilities offering a $70 million funding opportunity that aims to enhance the cybersecurity posture of electric cooperative, municipal, and small investor-owned utilities.
Funding Details
10 projects will be funded with application submissions due November 29, 2023, 5:00 pm ET with $200,000 each in cash prizes in the following areas:
- Direct support for eligible utilities to make investments in cybersecurity technologies, tools, training, and improvements in utility processes and procedures;
- Funding to strengthen the peer-to-peer and not-for-profit cybersecurity technical assistance ecosystem currently serving eligible electric utilities; and
- Increasing access to cybersecurity technical assistance and training for eligible utilities with limited cybersecurity resources. 2
To submit for this award visit: https://www.herox.com/ACT1Prize
How can electric municipalities utilize the funding?
While the adoption of hybrid working patterns increase cloud and SaaS usage, the number of industrial IoT devices also continues to rise. The result is decrease in visibility for security teams and new entry points for attackers. Particularly for energy and utility organizations.
Electric cooperatives seeking to enhance their cyber security posture can aim to invest in cyber security tools that provide the following:
Compliance support: Consider finding an OT security solution that maps out how its solutions and features help your organization comply with relevant compliance mandates such as NIST, ISA, FERC, TSA, HIPAA, CIS Controls, and more.
Anomaly based detection: Siloed security solutions also fail to detect attacks that span
the entire organization. Anomaly-based detection enhances an organization’s cyber security posture by proactively defending against potential attacks and maintaining a comprehensive view of their attack surface.
Integration capabilities: Implementation of several point solutions that complete individual tasks runs the risk of increasing workloads for operators and creates additional challenges with compliance, budgeting, and technical support. Look for cyber security tools that integrate with your existing technologies.
Passive and active asset tracking: Active Identification offers accurate enumeration, real time updates, vulnerability assessment, asset validation while Passive Identification eliminates the risk of operational disruption, minimizes risk, does not generate additional network traffic. It would be ideal to find a security solution that can do both.
Can secure both IT and OT in unison: Given that most OT cyber-attacks actually start in IT networks before pivoting into OT, a mature security posture for critical infrastructure would include a single solution for both IT and OT. Separate solutions for IT and OT present challenges when defending network boundaries and detecting incidents when an attacker pivots from IT to OT. These independent solutions also significantly increase operator workload and materially diminish risk mitigation efforts.
Darktrace/OT for Electric Cooperatives and Utilities
For smaller teams with just one or two dedicated employees, Darktrace’s Cyber AI Analyst and Investigation features allow end users to spend less time in the platform as it compiles critical incidents into comprehensive actionable event reports. AI Analyst brings all the information into a centralized view with incident reporting in natural language summaries and can be generated for compliance reports specific to regulatory requirements.
For larger teams, Darktrace alerts can be forwarded to 3rd party platforms such as a SIEM, where security team decision making is augmented. Additionally, executive reports and autonomous response reduce the alert fatigue generally associated with legacy tools. Most importantly, Darktrace’s unique understanding of normal allows security teams to detect zero-days and signatureless attacks regardless of the size of the organization and how alerts are consumed.
Key Benefits of Darktrace/OT
- Anomaly-based detection and real-time response
- Secures IT, OT, and IoT in unison
- Active and Passive Asset Identification
- Automated security reporting
- Attack surface management and vulnerability assessment
- Covers all levels of the Purdue Model
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参考文献
