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Emotetの復活:業界をまたがるキャンペーンの分析

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22
Aug 2022
22
Aug 2022
This blog aims to provide background and technical discoveries from the recent Emotet resurgence detected in early 2022 across multiple Darktrace client environments in multiple regions and industries. Predominantly in March and April 2022, Darktrace DETECT provided visibility over network activities associated with Emotet compromises using initial staged payload downloads involving algorithmically generated DLLs and subsequent outbound command and control, as well as spam activities.

はじめに

昨年は、サイバー脅威の状況が依然として複雑で予測困難であることを示す、さらなる証拠となりました。不確実なアトリビューション、新しいエクスプロイト、急速なマルウェア開発の狭間で、セキュリティの取り組みをどこに集中させるべきかを知ることが難しくなってきています。2021年の最大のサプライズの1つは、悪名高いEmotetボットネットの再来でした。これは、業界の特性や地域を無視し、一見すると無差別に企業をターゲットにしたキャンペーンの例です。1月に法執行機関がEmotetをテイクダウンしてからわずか10か月後、11月に新たなEmotetの活動がセキュリティ研究者によって発見されました。この活動は2022年の第1四半期まで続きましたが、このブログでは、Darktraceの脅威インテリジェンスユニットによる調査結果を通じて、この時期を探っていきます。 

2019年にさかのぼると、EmotetはTrickbotペイロードを配信し、最終的に感染したデバイスにRyukランサムウェア株を展開することが知られています。この相互接続性は、脅威グループのヒドラのような性質を浮き彫りにし、1つを排除しても(たとえ本格的な法執行機関の介入があっても)脅威として排除されることはなく、脅威の状況がより安全になることを示すものでもないことを示しました。 

Emotet が復活したとき、予想通り、最初の感染経路の1つは、既存の Trickbot のインフラを活用することでした。しかし、元の攻撃とは異なり、全く新しいフィッシングキャンペーンが採用されました。

図1:Darktrace の顧客環境で観測されたEmotetの活動分布

当初のEmotet感染と似ていますが、新しい感染の波は2つのカテゴリーに分類されています。エポック4とエポック5です。Darktraceが展開するグローバルな顧客環境では、エポック4 に関連する Emotet 感染が最も多く発生しているようです。影響を受けた顧客環境は、製造業やサプライチェーン、接客業や旅行業、行政、技術や通信、医療など、幅広い国や業種で見られました(図 1)。また、従業員数も250人以下から5,000人以上の企業までさまざまで、企業の属性や規模はターゲティングの要因にはなっていないようです。

エポック1-3とエポック4-5の主な違い

Emotetエクスプロイトの内部構造に関する幅広いセキュリティ調査に基づき、エポック4/5とその前の世代の間にいくつかの重要な違いがあることが確認されました。新しいエポックでは、以下のものが使用されていました:

- Microsoft の文書形式が異なる(OLEとXMLベース)。

- 通信のための異なる暗号化アルゴリズム。新しいエポックでは、C2設定ファイル [2] に含まれる公開暗号鍵で楕円曲線暗号(ECC)[1] を使用しました。これは、これまでのRSA(Rivest-Shamir-Adleman)鍵の暗号化方式とは異なるものでした。

- Control Flow Flatteningは、検知やリバースエンジニアリングを困難にする難読化技術として利用されていました。これは、プログラムの制御フローを隠蔽することによって行われます [3]。

- C2通信は230以上のユニークなIPに向けられており、すべて新しいエポック4と5に関連していることから、新しいC2インフラが観察されました。

エポック4-5の新機能に加え、Darktrace は新たなキャンペーンの影響を受けたそれらの展開に意外な共通点があることを検知しました。これには、Emotetの新しいインフラストラクチャへの自己署名付きSSL接続と、複数の稀な外部エンドポイントへのマルウェアスパム活動が含まれていました。これらのアウトバウンドコミュニケーションに先立ち、複数の展開におけるデバイスが、Emotet に関連するペイロード (アルゴリズムで生成された DLL ファイル) をダウンロードしたことが検知されました。

Emotet 復活キャンペーン

図2: Emotet エポック4-5の侵害に関するDarktraceの検知タイムライン

1. 最初の侵害

復活活動における最初の侵入経路は、ほぼ間違いなく、Trickbotインフラストラクチャまたは成功したフィッシング攻撃です(図2)。最初の侵入後、このマルウェア株は、マクロ化されたファイルを介してペイロードのダウンロードを開始し、その後のマルウェアのダウンロードのためにPowerShellを起動するために使用されます。

ダウンロードの後、Emotet の C2 インフラとの悪意のある通信が、スパムモジュールの活動とともに観測されました。Darktrace 内で、主要なテクニックが観察され、以下に文書化されています。

2. 足場を固めるバイナリ ダイナミック リンク ライブラリ(.dll)とアルゴリズムによるファイル名の生成 

Emotet のペイロードはポリモーフィック型であり、アルゴリズムで生成されたファイル名を含んでいます。また、導入した環境では、www[.]arkpp[.]com という疑わしいホスト名を含む HTTP GET リクエストや、以下のような Emotet 関連のサンプルが確認されています。

- hpixQfCoJb0fS1.dll (SHA256ハッシュ:859a41b911688b00e104e9c474fc7aaf7b1f2d6e885e8d7fbf11347bc2e21eaa)

- M0uZ6kd8hnzVUt2BNbRzRFjRoz08WFYfPj2.dll (SHA256 ハッシュ:9fbd590cf65cbfb2b842d46d82e886e3acb5bfecfdb82afc22a5f95bda7dd804)

- TpipJHHy7P.dll (SHA256 ハッシュ:40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b)

これらのDLLファイルは、rundll32[.]exeやregsvr32[.]exe などのWindowsプロセスに依存して実行されるEmotetローダーの配布を表している可能性が高いです。 

3. 足場を確立するEmotet C2 サーバーへのアウトバウンド SSL 接続 

EmotetのC2通信のための明確なネットワークIoCは、CN=example[.]com,OU=IT Department,O=Global Security,L=London,ST=London,C=GB と一致する証明書発行者と件名を使用し、共通のJA3クライアントフィンガープリント(72a589da586844d7f0818cce684948ea)を使用して自己署名SSLを含みました。主なC2通信は、エポック5ではなくエポック4に分類されるインフラに関与していることが確認されました。通信内容は暗号化されていましたが、ネットワーク接続の詳細はC2活動の検知に十分なものでした(図3)。

図3:ダウンロードと送信SSLアクティビティに関するUIモデルブリーチログ

TCPポート25、465、587でのアウトバウンドSSLおよびSMTP接続 

Microsoft Outlook 15.0 のような異常なユーザーエージェントがSMTP接続に使用されており、送信メールの件名の一部にBase64エンコードされた文字列が含まれていることが確認されました。さらに、このJA3クライアントのフィンガープリント(37cdab6ff1bd1c195bacb776c5213bf2)は、SSL接続からよく見受けられました。少なくとも10件の展開で観測されたマルウェアスパムのホスト名一式に基づくと、TLDは.jp、.com、.net、.mxが大半で、日本のTLDが最も多くなっています(図4)。

図4: アウトバウンドSSLおよびSMTPで観測されたマルウェアスパムのTLD

 スパムモジュールから生成された平文のスパムコンテンツがPCAPで確認されました(図5)。フィッシングやスパムの明らかな指標となる例としては、1) 個人ヘッダーとEメールヘッダーの不一致、2) 件名における異常な返信チェーンと受信者の参照、3) 疑わしい圧縮ファイルの添付(例:Electronic form[.]zip.)があります。

図5:SPAMモジュールに関連するPCAPの例

4. ミッションの達成

 Emotetの復活は、CobaltStrikeに関連する異常なSMBドライブ書き込みを伴う二次的な侵害でも示されました。これには、SSLアクティビティに見られる以下のJA3ハッシュ(72a589da586844d7f0818ce684948eea)や、svchost.exeファイルを含むSMB書き込みなどが一貫して含まれています。

Darktrace による検知

 Emotetの復活に伴う活動を特定するために使用された主要なDETECT モデルは、可能性のあるC2を決定することに重点を置いていました。これらは以下の通りです:

·       Suspicious SSL Activity

·       Suspicious Self-Signed SSL

·       Rare External SSL Self-Signed

·       Possible Outbound Spam

ファイルにフォーカスする有益なモデルも含まれていました:

·       Zip or Gzip from Rare External Location

·       EXE from Rare External Location

Darktraceの検知能力は、脅威研究チームの調査で判明したケーススタディのサンプルでも示すことができます。

ケーススタディ 

DarktraceのEmotet活動の検知は、業種や企業規模に制限されることなく行われました。新しいエポックでは、多くの類似した特徴が見られましたが、各インシデントではキャンペーンとは異なる手法が見られました。これは、以下の2つのクライアント環境において示されています。

行政機関の大規模な顧客環境を調査したところ、16種類のデバイスがexample[.]comという発行元と52,536回のSSL接続を行ったことが検知されました。この発行者に関連するデバイスは、主に同じ自己署名およびスパムに関するDETECT モデルを破っていることが確認されました。このSSLの前に異常な受信オクテットストリームが観察されましたが、ダウンロードとEmotet C2接続の間には明確な関係はありませんでした。影響を受けたデバイスはネットワーク全体のごく一部に過ぎないにもかかわらず、Darktrace アナリストは、はるかに大きなネットワーク「ノイズ」に対してフィルターをかけ、侵害の詳細な証拠を突き止めて、顧客に通知することができました。

Darktrace はまた、より小規模な顧客環境における新たなEmotetの活動も確認しました。ヘルスケアおよび製薬分野のある企業を見てみると、2022年3月中旬から、単一の内部デバイスがホストarkpp[.]comに対してHTTP GETリクエストを行い、SHA256ハッシュを持つアルゴリズム生成DLL、TpipJHy7P.dllを含むことが検知されています。40060259d583b8cf83336bc50cc7a7d9e0a4de22b9a04e62ddc6ca5dedd6754b (図6). 

図6:VirusTotalのスクリーンショットで、SHA256ハッシュが他のセキュリティベンダーから悪意のあるものとしてフラグが立っていることが判明

サンプルがダウンロードされた後、デバイスは、ネットワーク上のデバイスから一度も接触したことのない多数のエンドポイントに接触しました。エンドポイントは、Emotet関連のIOCと前述の同じSSL証明書を含むポート443、8080、および7080を介して接触していました。また、同様の時間帯にマルウェアスパムの活動も観察されました。

 上記の Emotet のケーススタディは、従来のルールやシグネチャに依存することなく、一連の異常な活動を 自律的に検知することで、いかに重要な脅威の活動を明らかにできるかを示しています。ステージングされたペイロードの可能性は、影響を受けた環境の一部でしか見られませんでしたが、多くのエンドポイントやポートを含む以下のアウトバウンドC2およびマルウェアスパム活動は、Emotetの検知に十分なものでした。

 このような場合、Darktraceの自律遮断技術(RESPOND)は、段階的なペイロード、外部へのC2通信、マルウェアスパム活動に関連する活動を正確にターゲットとするピンポイントな処置を推奨または実施することになります。さらに、デバイスの通常の生活パターンを制限することで、同時に発生する悪意ある活動を防ぐと同時に、通常のビジネスオペレーションの継続を可能にします。

 結論 

- 過去と現在のEmotetの系統の技術的な違いは、悪意のある脅威アクターの多様性を強調し、シグネチャに依存しないセキュリティソリューションの必要性を示しています。

-Darktraceの可視化能力と独自の振る舞い検知により、ルールやシグネチャに依存することなく、新たなEmotet 株に関連するネットワーク活動の可視化を継続的に実現しています。主な例として、新しいEmotetのインフラへのC2接続があります。

- 今後は、疑わしいDLLを使用したC2確立を検知することで、Emotet株のネットワーク上でのさらなる伝播を防ぐことができます。

- DarktraceのAIによる検知と遮断能力は、静的・動的コード解析によるEmotet株の分析、その後のルールとシグネチャの実装を含む従来の侵害後の研究を凌駕するものです。

Paul JenningsとHanah Darleyの本ブログへの寄稿に感謝します。

付録

モデルブリーチ

·       Anomalous Connection / Anomalous SSL without SNI to New External 

·       Anomalous Connection / Application Protocol on Uncommon Port 

·       Anomalous Connection / Multiple Connections to New External TCP Port 

·       Anomalous Connection / Multiple Failed Connections to Rare Endpoint 

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·       Anomalous Connection / Possible Outbound Spam 

·       Anomalous Connection / Rare External SSL Self-Signed 

·       Anomalous Connection / Repeated Rare External SSL Self-Signed      

·       Anomalous Connection / Suspicious Expired SSL 

·       Anomalous Connection / Suspicious Self-Signed SSL

·       Anomalous File / Anomalous Octet Stream (No User Agent) 

·       Anomalous File / Zip or Gzip from Rare External Location 

·       Anomalous File / EXE from Rare External Location

·       Compromise / Agent Beacon to New Endpoint 

·       Compromise / Beacon to Young Endpoint 

·       Compromise / Beaconing Activity To External Rare 

·       Compromise / New or Repeated to Unusual SSL Port 

·       Compromise / Repeating Connections Over 4 Days 

·       Compromise / Slow Beaconing Activity To External Rare 

·       Compromise / SSL Beaconing to Rare Destination 

·       Compromise / Suspicious Beaconing Behaviour 

·       Compromise / Suspicious Spam Activity 

·       Compromise / Suspicious SSL Activity 

·       Compromise / Sustained SSL or HTTP Increase 

·       Device / Initial Breach Chain Compromise 

·       Device / Large Number of Connections to New Endpoints 

·       Device / Long Agent Connection to New Endpoint 

·       Device / New User Agent 

·       Device / New User Agent and New IP 

·       Device / SMB Session Bruteforce 

·       Device / Suspicious Domain 

·       Device / Suspicious SMB Scanning Activity 

Darktrace をお使いのお客様で、Darktrace を使って Emotet をトリアージする方法についてもっと知りたい方は、こちらを参照してください。 

参考文献

[1] https://blog.lumen.com/emotet-redux/

[2] https://blogs.vmware.com/security/2022/03/emotet-c2-configuration-extraction-and-analysis.html

[3] https://news.sophos.com/en-us/2022/05/04/attacking-emotets-control-flow-flattening/

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.
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Eugene Chua
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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

Blog

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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