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Outlawの秘密のクリプトマイニングをAIがどのように発見したか

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10
Oct 2021
10
Oct 2021
For years, the notorious crypto-jacking group Outlaw have been adapting their botnet to make it past traditional security measures. This blog explains how Darktrace was able to see through their disguises and unpack their methods.

サイバー犯罪者達にとって、悪名は逆説的な望みです。一部の者にとって、自慢する権利はサイバー犯罪の動機ともなりますが、検知を免れたいと願っているものにとって悪名が広まることは分別ある目標とは言えないでしょう。このことは、たとえば大きな利益を生んだEmotetボットネットの背後にいた脅威アクター達が、2021年の初めに8か国の法執行機関による協調した摘発により彼らのオペレーションが壊滅させられたときに学んだことでした。それでもなお、サイバーセキュリティメディアに繰り返し名前が登場し、常に検知を免れているグループもあります。たとえばOutlawなどもその一つです。

Outlawの襲撃計画の立て方

2018年以来活動していながら、ハッキンググループOutlawについてはほとんど知られていません。Outlawは中国国内および国際的に数多くのボットネットおよびクリプトジャッキング攻撃を仕掛けてきました。このグループは、繰り返し使われたファイル名や暗号通貨Moneroのマイニングを行う傾向などからさまざまな特徴で知られていますが、その成功の理由は究極的には攻撃と攻撃の間の数か月の休眠期間に適応し進化することにあると言えます。

Outlawの攻撃の特徴は絶え間ない変更や更新であり、比較的静かに活動することにより、見慣れない脅威に弱いセキュリティシステムを標的としています。

2020年、Outlawはボットネットツールセットを更新し、他の犯罪者のクリプトジャッキングソフトウェアを見つけて潰すことにより、感染したデバイスからの彼らに対する支払いを最大化しようとしたことが注目を集めました。サイバー犯罪者の間に敬意が存在しないことには驚きませんが、この更新にはOutlawのマルウェアが従来のセキュリティ防御をすり抜けられるようさらに厄介な変更が含まれていました。

大きな盗みを働くたびに姿を変え、静かに身を隠しておくことにより、Outlawという名前にどれほどの悪評がついても、過去の攻撃データに依存する従来のセキュリティシステムが彼らに対して備えられないようにしてきたのです。しかし組織がこれらのシステムのルールベースのアプローチを超え、デジタルエステートの保護に自己学習型AIを取り入れるようになると、Outlawのようなグループに対しても形勢を逆転できるようになります。

このブログでは、2021年の夏、世界の遠く離れた2つの場所にあった2台の感染済みゾンビデバイスがOutlawのボットネットによって作動し、Darktraceがこうしたアクティビティをこれらのデバイスが事前に感染していたにも関わらず検知できた事例を紹介します。

賞金稼ぎ:攻撃の最初の兆候

図1:攻撃のタイムライン

7月、中央アメリカの通信会社のネットワークに1台の新しいデバイスが追加されると、Darktraceは2つの疑わしいエンドポイントに対して一連の定期的な接続が行われていることを検知し、ビーコニング動作であることを特定しました。同じ動作がこれとは別に、しかしほとんど同時に、アジア太平洋地域の金融企業で発生していることがわかりました。この会社はDarktraceを初めて導入したところでした。Darktraceの自己学習型AIが既に感染していたデバイスを特定することができたのは、それぞれのデジタルエステート内の同じような動作のデバイスをピアグループとしてクラスタ化することにより、デバイスのさまざまな振る舞いからこれら2台が通常と異なる動きをしていることを認識したためです。

これらのゾンビデバイスがOutlawにより作動させられた最初の兆候は暗号通貨マイニングの開始でした。地理的に遠く離れていたにも関わらずこれらのデバイスは同じクリプトアカウントに接続していることがわかり、無差別かつ急激に拡大するボットネットの性質が再確認されました。

Outlaw は過去にはその活動を中国にあるデバイスに限定しておりこれは警戒していることの現れと考えられていましたが、最近のこのような活動は自信の高まりを示しています。

ボットネットのリクルートプロセス

これに続く443番ポート(HTTPSアクティビティと関係のあることが多いポート)を使ったInternet Relay Chat (IRC) 接続は、以前2020年にOutlawボットネットが示していたアクティビティの特徴と完全に一致していました。IRCはボットマスターとゾンビデバイスの間のコミュニケーションによく使われるツールですが、443番ポートを使うことにより攻撃者は通常のインターネットトラフィックに紛れ込もうとしたのです。

この通信の直後、これらのデバイスはシェルスクリプトをダウンロードしました。DarktraceのCyber AI Analystはネットワーク内を通過したこのこのシェルスクリプトを傍受し再現することによりその機能のすべてを明らかにしました。興味深いことに、このスクリプトはARMアーキテクチャを使用しているデバイスを識別してボットネットから除外していました。ARMアーキテクチャはその優れた低消費電力性により、主にポータブルなモバイルデバイスで使用されています。

この選別は、Outlawの主たる目的が悪意あるクリプトマイニングであることの証左です。クリプトマイニング性能が低い小型なデバイスを避けることにより、このシェルスクリプトは最も処理性能の高い、したがって利益の大きいデバイス、たとえばデスクトップコンピューターやサーバーにボットネットを集中させています。こうすることにより、クリプトマイニングのスケールにあまり大きな影響を与えることなくボットネット全体の残すIndicators of Compromise (IOCs) を縮小することができます。

問題の2台のデバイスはARMアーキテクチャを使用しておらず、数分後にはdota3[.]tar[.]gzという名前のファイルを含む二次ペイロードを受信しました。これは前世代のOutlawボットネット‘dota2’(人気のビデオゲームの名前をとったもの)の新シリーズともいうべきものです。このファイルの受信に伴って、これらのデバイスは世界に広がるOutlawボットネットの最新バージョンでアップデートされたようでした。

このダウンロードには、攻撃者による ‘Living off the Land’ (環境に寄生する)戦術の使用が一役買っています。これらのデバイスに既に存在している普通のLinuxプログラム(それぞれ‘curl’と‘Wget’)だけを使うことにより、Outlawは従来のセキュリティシステムによりアクティビティをマークされることを回避したのです。たとえばWgetは、表面上はWebサーバーからコンテンツを取得するのに使用する信頼できるプログラムであり、OutlawのTTP(戦術、テクニック、手順)の一部として過去に使用された記録がありません。

アプローチを進化させ適応させることにより、Outlawはルールベースのセキュリティを常に出し抜くことができたのです。しかしDarktraceの自己学習型AIはこれに対応し、このWget接続を即座に疑わしいものと識別してさらなる調査を指示しました。

図2:Cyber AI Analystは7月15日午前のWgetの使用を疑わしいものとして識別し、関係があるかもしれない7月14日の午前に発生したHTTP接続の調査を開始しました。このようにして、攻撃の全体像を構築します。

ボットネットの解明

その後36時間において、DarktraceはSSHと関連することの多いポート、たとえば22番、2222番、2022番などから未知の外部IPアドレスに対する600万回を超えるTCPおよびSSH接続を検知しました。

これらの接続によってボットネットが実際に何をしようとしていたのかは想像するしかありません。これらのデバイスはDDoS(Distributed Denial of Service)や、狙ったSSHアカウントに対するブルートフォース攻撃の一部として使用されていたか、あるいは単にボットネットをさらに拡大するために新しい標的を探し感染させるタスクを担っていたのかもしれません。 Darktraceはどちらのデバイスもこのイベント以前にはSSH接続を行っていなかったことを認識しており、Antigenaがアクティブモードで運用されていれば、これらを中断させる方策を実行していたことでしょう。

図3:2021年7月14日にボットがアクティブ化される前および後のデバイスの動作。モデル違反の大幅な増加は確認済みの「生活パターン」からの明らかな逸脱を示しています。

幸いなことに、どちらのデバイスの所有者もDarktraceの検知アラートに迅速に反応し、それぞれのデジタルエステートに対する深刻な被害が及ぶのを防ぐことができました。これらのデバイスが引き続きボットネットの影響下にあれば、その悪影響ははるかに深刻なものとなっていたはずです。

SSHプロトコルの使用により、Outlawは多数のアクティビティに転回していくことができたはずで、これらのデバイスのネットワークをさらに侵害し、それぞれの組織にデータ損失あるいは金銭的な損失を与えていたはずです。

保安官を呼ぶ:自己学習型AI

ルールベースのセキュリティソリューションは昔の西部劇の「お尋ね者」ポスターのようなもので、先週街にやってきた犯罪者を探す一方で、今日丘の上に現れた犯罪者に対する備えはありません。悪意あるハッカーや犯罪者達が攻撃のたびに新しい見た目を取り入れ新しいテクニックを活用する状況では、脅威に対する新しい対処の方法が必要です。

Darktraceは‘Outlaw’という名前も、彼らの攻撃の変化の歴史も、彼らを阻止する上で知る必要がありません。根本的な自己学習型アプローチにより、Darktraceは周囲の環境をゼロから学習し、サイバー脅威の兆候かもしれないかすかな変化を識別します。さらに、独自の自律遮断技術により、人間の介入を必要とすることなく、的を絞ったアクションを実行してマシンスピードで脅威を無害化することも可能です。

この脅威事例についての考察はDarktraceアナリストJun Qi Wong が協力しました。

Cyber AI Analyst が複雑な攻撃を自動調査する仕組みについて知る

技術的詳細

Darktraceによるモデル検知

  • Compliance / Crypto Currency Mining Activity
  • Compromise / High Priority Crypto Currency Mining [Enhanced Monitoring]
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Device / Increased External Connectivity
  • Unusual Activity / Unusual External Activity
  • Compromise / SSH Beacon
  • Compromise / High Frequency SSH Beacon
  • Anomalous Connection / Multiple Connections to New External TCP Port

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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Inside the SOC

Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework

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17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

結論

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

付録

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

侵害指標(IoC)一覧

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

参考文献

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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著者について
Natalia Sánchez Rocafort
Cyber Security Analyst

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Eメール

Looking Beyond Secure Email Gateways with the Latest Innovations to Darktrace/Email

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09
Apr 2024

Organizations Should Demand More from their Email Security

In response to a more intricate threat landscape, organizations should view email security as a critical component of their defense-in-depth strategy, rather than defending the inbox alone with a traditional Secure Email Gateway (SEG). Organizations need more than a traditional gateway – that doubles, instead of replaces, the capabilities provided by native security vendor – and require an equally granular degree of analysis across all messaging, including inbound, outbound, and lateral mail, plus Teams messages.  

Darktrace/Email is the industry’s most advanced cloud email security, powered by Self-Learning AI. It combines AI techniques to exceed the accuracy and efficiency of leading security solutions, and is the only security built to elevate, not duplicate, native email security.  

With its largest update ever, Darktrace/Email introduces the following innovations, finally allowing security teams to look beyond secure email gateways with autonomous AI:

  • AI-augmented data loss prevention to stop the entire spectrum of outbound mail threats
  • an easy way to deploy DMARC quickly with AI
  • major enhancements to streamline SOC workflows and increase the detection of sophisticated phishing links
  • expansion of Darktrace’s leading AI prevention to lateral mail, account compromise and Microsoft Teams

What’s New with Darktrace/Email  

Data Loss Prevention  

Block the entire spectrum of outbound mail threats with advanced data loss prevention that builds on tags in native email to stop unknown, accidental, and malicious data loss

Darktrace understands normal at individual user, group and organization level with a proven AI that detects abnormal user behavior and dynamic content changes. Using this understanding, Darktrace/Email actions outbound emails to stop unknown, accidental and malicious data loss.  

Traditional DLP solutions only take into account classified data, which relies on the manual input of labelling each data piece, or creating rules to catch pattern matches that try to stop data of certain types leaving the organization. But in today’s world of constantly changing data, regular expression and fingerprinting detection are no longer enough.

  • Human error – Because it understands normal for every user, Darktrace/Email can recognize cases of misdirected emails. Even if the data is correctly labelled or insensitive, Darktrace recognizes when the context in which it is being sent could be a case of data loss and warns the user.  
  • Unclassified data – Whereas traditional DLP solutions can only take action on classified data, Darktrace analyzes the range of data that is either pending labels or can’t be labeled with typical capabilities due to its understanding of the content and context of every email.  
  • Insider threat – If a malicious actor has compromised an account, data exfiltration may still be attempted on encrypted, intellectual property, or other forms of unlabelled data to avoid detection. Darktrace analyses user behaviour to catch cases of unusual data exfiltration from individual accounts.

And classification efforts already in place aren’t wasted – Darktrace/Email extends Microsoft Purview policies and sensitivity labels to avoid duplicate workflows for the security team, combining the best of both approaches to ensure organizations maintain control and visibility over their data.

End User and Security Workflows

Achieve more than 60% improvement in the quality of end-user phishing reports and detection of sophisticated malicious weblinks1

Darktrace/Email improves end-user reporting from the ground up to save security team resource. Employees will always be on the front line of email security – while other solutions assume that end-user reporting is automatically of poor quality, Darktrace prioritizes improving users’ security awareness to increase the quality of end-user reporting from day one.  

Users are empowered to assess and report suspicious activity with contextual banners and Cyber AI Analyst generated narratives for potentially suspicious emails, resulting in 60% fewer benign emails reported.  

Out of the higher-quality emails that end up being reported, the next step is to reduce the amount of emails that reach the SOC. Darktrace/Email’s Mailbox Security Assistant automates their triage with secondary analysis combining additional behavioral signals – using x20 more metrics than previously – with advanced link analysis to detect 70% more sophisticated malicious phishing links.2 This directly alleviates the burden of manual triage for security analysts.

For the emails that are received by the SOC, Darktrace/Email uses automation to reduce time spent investigating per incident. With live inbox view, security teams gain access to a centralized platform that combines intuitive search capabilities, Cyber AI Analyst reports, and mobile application access. Analysts can take remediation actions from within Darktrace/Email, eliminating console hopping and accelerating incident response.

Darktrace takes a user-focused and business-centric approach to email security, in contrast to the attack-centric rules and signatures approach of secure email gateways

Microsoft Teams

Detect threats within your Teams environment such as account compromise, phishing, malware and data loss

Around 83% of Fortune 500 companies rely on Microsoft Office products and services, particularly Teams and SharePoint.3

Darktrace now leverages the same behavioral AI techniques for Microsoft customers across 365 and Teams, allowing organizations to detect threats and signals of account compromise within their Teams environment including social engineering, malware and data loss.  

The primary use case for Microsoft Teams protection is as a potential entry vector. While messaging has traditionally been internal only, as organizations open up it is becoming an entry vector which needs to be treated with the same level of caution as email. That’s why we’re bringing our proven AI approach to Microsoft Teams, that understands the user behind the message.  

Anomalous messaging behavior is also a highly relevant indicator of whether a user has been compromised. Unlike other solutions that analyze Microsoft Teams content which focus on payloads, Darktrace goes beyond basic link and sandbox analysis and looks at actual user behavior from both a content and context perspective. This linguistic understanding isn’t bound by the requirement to match a signature to a malicious payload, rather it looks at the context in which the message has been delivered. From this analysis, Darktrace can spot the early symptoms of account compromise such as early-stage social engineering before a payload is delivered.

Lateral Mail Analysis

Detect and respond to internal mailflow with multi-layered AI to prevent account takeover, lateral phishing and data leaks

The industry’s most robust account takeover protection now prevents lateral mail account compromise. Darktrace has always looked at internal mail to inform inbound and outbound decisions, but will now elevate suspicious lateral mail behaviour using the same AI techniques for inbound, outbound and Teams analysis.

Darktrace integrates signals from across the entire mailflow and communication patterns to determine symptoms of account compromise, now including lateral mailflow

Unlike other solutions which only analyze payloads, Darktrace analyzes a whole range of signals to catch lateral movement before a payload is delivered. Contributing yet another layer to the AI behavioral profile for each user, security teams can now use signals from lateral mail to spot the early symptoms of account takeover and take autonomous actions to prevent further compromise.

DMARC

Gain in-depth visibility and control of 3rd parties using your domain with an industry-first AI-assisted DMARC

Darktrace has created the easiest path to brand protection and compliance with the new Darktrace/DMARC. This new capability continuously stops spoofing and phishing from the enterprise domain, while automatically enhancing email security and reducing the attack surface.

Darktrace/DMARC helps to upskill businesses by providing step by step guidance and automated record suggestions provide a clear, efficient road to enforcement. It allows organizations to quickly achieve compliance with requirements from Google, Yahoo, and others, to ensure that their emails are reaching mailboxes.  

Meanwhile, Darktrace/DMARC helps to reduce the overall attack surface by providing visibility over shadow-IT and third-party vendors sending on behalf of an organization’s brand, while informing recipients when emails from their domains are sent from un-authenticated DMARC source.

Darktrace/DMARC integrates with the wider Darktrace product platform, sharing insights to help further secure your business across Email Attack Path and Attack Surface management.

結論

To learn more about the new innovations to Darktrace/Email download the solution brief here.

All of the new updates to Darktrace/Email sit within the new Darktrace ActiveAI Security Platform, creating a feedback loop between email security and the rest of the digital estate for better protection. Click to read more about the Darktrace ActiveAI Security Platform or to hear about the latest innovations to Darktrace/OT, the most comprehensive prevention, detection, and response solution purpose built for critical infrastructures.  

Learn about the intersection of cyber and AI by downloading the State of AI Cyber Security 2024 report to discover global findings that may surprise you, insights from security leaders, and recommendations for addressing today’s top challenges that you may face, too.

参考文献

[1] Internal Darktrace Research

[2] Internal Darktrace Research

[3] Essential Microsoft Office Statistics in 2024

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著者について
Carlos Gray
Product Manager
Our ai. Your data.

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