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多様な言語によるフィッシング攻撃を阻止する

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16
Jun 2021
16
Jun 2021
With globalized companies and supply chains, organizations need one solution which works for all emails no matter the time zone, no matter the language. This blog analyses how Antigena Email stopped a series of multi-language phishing attacks, including an Emotet campaign in Japanese.

Click here! Clique aqui! ここをクリック! Klikk here! !اینجا کلیک کنید naDev yIbej! Hic tange!

言葉は人を惑わせます。Eメールセキュリティの領域では、言葉により受信者にリンクをクリックさせたり取引を完了させたりできます。また、セキュリティツールを騙して、Eメールが正当なものであると思わせることもできます。

このような理由から、Darktrace/Emailは言語に依存せず、数学を用いて、組織内のすべてのEメールユーザーの「通常の状態」への理解を構築します。これにより、書式や言語にかかわらず、脅威を示す世界中のEメールを無力化することができます。

自然言語処理

侵害されたアカウントやなりすましEメールを捕捉する際に、人間による普通の対応と比較した、意図やトーンの違いをどのようにコンピューターに教えることができるでしょうか。

Eメールセキュリティにおいて最も一般的なアプローチの1つが、自然言語処理(NLP)です。NLPでは、自然言語(人間の言葉)を分析する方法についてコンピューターをどうプログラミングするかが焦点となります。一般的には、大量のデータに晒すことによって行います。

その結果、文書の内容を、言葉のニュアンスを含めて「理解」できるコンピューターができあがります。その後、このテクノロジーを用いて、文書内の情報を抽出するとともに、文書自体を分類整理することができます。

現代における制約

しかし、Eメールセキュリティ分野におけるNLPの利用は限定されています。これは、専門用語や口語、さらにはプログラミング当時にはなかった用語などについてもさらに教育をしない限り、理解を誤ってしまうためです。また、言語を追加するたびに、毎回ゼロからの学習が必要になります。NLPは、学習した言語に対してのみ機能するため、すべての小規模市場で機能するように教育するには採算が合いません。

したがって、米国に本社を置くEメールセキュリティベンダーの製品を採用した場合、そのベンダーは英語ベースのフィッシングの脅威を検知するために大半の時間を割いている可能性が高くなります。英語でのみやりとりしているなら問題ありませんが、そうではない場合も少なくありません。グローバル化された21世紀の世界では、言語に依存しないセキュリティテクノロジーのニーズはこれまでになく高まっています。

AIはどれでも同じではない: 教師なし機械学習

Darktrace Emailは教師なし機械学習を使用しています。この技術は「オンザジョブ」で学習することができ、大量のデータセットを入力する必要がありません。NLPからの考察を追加情報として得ることはできますが、検知や理解はNLPに依存しません。

AIを使用する際には、オンザジョブで学習しているのか、ラベル付きデータセットで学習しているのか、AIの学習方法に対する理解が不可欠です。これは、なりすまし、フィッシング、サプライヤー偽装、その他あらゆる形式のメール攻撃によってユーザーをそそのかす試みを暴くために、Eメールに潜む意図を理解しようとする場合には特に重要です。

Darktrace Cyber AIは、Eメールの中の言語を理解するようにコンピューターを教育する代わりに、送受信されるEメールについての送信者、受信者、リンク、IPアドレス、添付アドレスの種類などのアクティビティを動的に評価します。次にAIは、これらすべてのオブジェクトの動きを利用して、すべてのやりとりにわたってすべてのユーザーの「生活パターン」を作成します。これには、ビジネスでの外部ユーザーとの頻繁なやりとりも含まれます。

数学的なアプローチを採ることで、Darktrace Emailはやりとりに使用する専門用語や方言にかかわらず「通常の状態」を理解し、ノルウェー語からラテン語、ペルシャ語に至るまであらゆる言語を独自に解釈し、その結果フィッシング攻撃やアカウント乗っ取りを示すわずかな異常を識別します。

日本語のEmotetを捕捉する

昨年、Darktraceは、悪名高いバンキングマルウェアであるEmotetを利用した高度なスパムウェア攻撃を発見しました。この攻撃はさまざまな業界を標的として、きわめて高度なフィッシングメールを使用していました。

日本のある食品企業で、Darktraceは7月の2日間に6件のフィッシングメールを検知しました。

図1:EmotetによるEメール

上のメールでは、件名行とファイル名の両方に「請求書の件」と書かれ、その後に数字と日付が続いています。ここで攻撃者は明らかに、正当な業務メールを模倣しようと試み、有名な日本企業(三菱食品(株))と、一般的な日本語の氏名を(藤沢 昭彦)を用いています。

Darktrace/Emailはこのメールの背後にある主な特徴を明らかにしました。真の送信者はGMO(安価なウェブメールサービスを提供する日本企業)のドメイン名を使用し、実は日本ではなくポルトガルに所在していました。

図2:Antigena Emailが勧誘の試みを検知

Darktrace/Emailのモデルは、Eメールが書かれている言語にかかわらず、トピックの異常性と、Eメールにおけるそそのかしの試みを認識し、85点という高い異常スコアを与えました。さらに、DarktraceのAIは、添付ファイルの拡張子とMIMEタイプも、このユーザーが通常Eメールでやりとりする場合と比較して異常であると判定しました。

ポルトガル語による脅威の発見

別のインスタンスでは、欧州にある企業に、悪意のある一連のEメールが送信されました。これらのEメールは、件名行のパーソナライズや悪意あるURLの隠蔽など、企業のセキュリティツールを回避するために複数の戦術を用いていました。

図3:Darktrace/Emailの対話型ユーザーインターフェイス。件名には「送金通知」と書かれています。

このEメールはCaixaBankドメインにつながっているように見えるリンクが含まれていました。しかし、Darktrace Emailはこれを、受信者を誤解させる故意の試みと認識しました。実際に、リンク先はあるWordPressドメインで、このドメインはCyber AIによって、同社にとって100%稀であると識別されました。

さらに詳しく調べたところ、これらのEメールはベトナムから送信されたことがわかりました。この送信者は同社と過去にやりとりしたことがなく、Eメール内の孤立したリンクも、100%稀なドメインとしてマークされていました。Darktrace Emailはこれらの悪意あるEメールを保留し、企業を被害から未然に保護しました。

世界共通の防衛

この2つの例は、教師なし機械学習アプローチのメリットを示しています。既存のデータに頼らず数百種類におよぶ指標を分析するAIセキュリティソリューションは、幅広い言語を利用するようになっているグローバルなフィッシング脅威に対して圧倒的な優位性があります。

Eメールベースの攻撃は、日増しに標的を絞り、説得力を高めています。高度な翻訳ツールを用いた、標的を絞ったソーシャルエンジニアリングとスピアフィッシングは、毎日のように企業にあらゆる言語で集中攻撃を浴びせています。

韓国の現地事務所を対象にしたフィッシング攻撃でも、アラビア語によるそそのかしの試みでも、あるいは『スタートレック』コンベンションから送信されたクリンゴン語(同シリーズに登場する架空の言語)の悪意あるメールでさえも、さらには無数の方言やトーンで行われるどんなメールのやりとりでも、Darktrace Emailは世界中、そしてさらに広い範囲で、顧客の企業を保護します。

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.
AUTHOR
ABOUT ThE AUTHOR
Mariana Pereira
VP, Cyber Innovation

Mariana is the VP of Cyber Innovation at Darktrace, and works closely with the development, analyst, and marketing teams to advise technical and non-technical audiences on how best to augment cyber resilience, and how to implement AI technology as a means of defense. She speaks regularly at international events, with a specialism in presenting on sophisticated, AI-powered email attacks. She holds an MBA from the University of Chicago, and speaks several languages including French, Italian, and Portuguese.

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Gootloader Malware: Detecting and Containing Multi-Functional Threats with Darktrace

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15
Feb 2024

What is multi-functional malware?

While traditional malware variants were designed with one specific objective in mind, the emergence of multi-functional malware, such as loader malware, means that organizations are likely to be confronted with multiple malicious tools and strains of malware at once. These threats often have non-linear attack patterns and kill chains that can quickly adapt and progress quicker than human security teams are able to react. Therefore, it is more important than ever for organizations to adopt an anomaly approach to combat increasingly versatile and fast-moving threats.

Example of Multi-functional malware

One example of a multi-functional malware recently observed by Darktrace can be seen in Gootloader, a multi-payload loader variant that has been observed in the wild since 2020. It is known to primarily target Windows-based systems across multiple industries in the US, Canada, France, Germany, and South Korea [1].  

How does Gootloader malware work?

Once installed on a target network, Gootloader can download additional malicious payloads that allow threat actors to carry out a range of harmful activities, such as stealing sensitive information or encrypting files for ransom.

The Gootloader malware is known to infect networks via search engine optimization (SEO) poisoning, directing users searching for legitimate documents to compromised websites hosting a malicious payload masquerading as the desired file.

If the malware remains undetected, it paves the way for a second stage payload known as Gootkit, which functions as a banking trojan and information-stealer, or other malware tools including Cobalt Strike and Osiris [2].

Darktrace detection of Gootloader malware

In late 2023, Darktrace observed one instance of Gootloader affecting a customer in the US. Thanks to its anomaly-focused approach, Darktrace DETECT™ quickly identified the anomalous activity surrounding this emerging attack and brought it to the immediate attention of the customer’s security team. All the while, Darktrace RESPOND™ was in place and able to autonomously intervene, containing the suspicious activity and ensuring the Gootloader compromise could not progress any further.

In September 2023, Darktrace identified an instance of the Gootloader malware attempting to propagate within the network of a customer in the US. Darktrace identified the first indications of the compromise when it detected a device beaconing to an unusual external location and performing network scanning. Following this, the device was observed making additional command-and-control (C2) connections, before finally downloading an executable (.exe) file which likely represented the download of a further malicious payload.

As this customer had subscribed to the Proactive Notification Service (PTN), the suspicious activity was escalated to the Darktrace Security Operations Center (SOC) for further investigation by Darktrace’s expert analysts. The SOC team were able to promptly triage the incident and advise urgent follow-up actions.

Gootloader Attack Overview

Figure 1: Timeline of Anomalous Activities seen on the breach device.

Initial Beaconing and Scanning Activity

On September 21, 2023, Darktrace observed the first indications of compromise on the network when a device began to make regular connections to an external endpoint that was considered extremely rare for the network, namely ‘analyzetest[.]ir’.

Although the endpoint did not overtly seem malicious in nature (it appeared to be related to laboratory testing), Darktrace recognized that it had never previously been seen on the customer’s network and therefore should be treated with caution.  This initial beaconing activity was just the beginning of the malicious C2 communications, with several additional instances of beaconing detected to numerous suspicious endpoints, including funadhoo.gov[.]mv, tdgroup[.]ru’ and ‘army.mil[.]ng.

Figure 2: Initial beaconing activity detected on the breach device.

Soon thereafter, Darktrace detected the device performing internal reconnaissance, with an unusually large number of connections to other internal locations observed. This scanning activity appeared to primarily be targeting the SMB protocol by scanning port 445.

Within seconds of DETECT’s detection of this suspicious SMB scanning activity, Darktrace RESPOND moved to contain the compromise by blocking the device from connecting to port 445 and enforcing its ‘pattern of life’. Darktrace’s Self-Learning AI enables it to learn a device’s normal behavior and recognize if it deviates from this; by enforcing a pattern of life on an affected device, malicious activity is inhibited but the device is allowed to continue its expected activity, minimizing disruption to business operations.

Figure 3: The breach device Model Breach Event Log showing Darktrace DETECT identifying suspicious SMB scanning activity and the corresponding RESPOND actions.

Following the initial detection of this anomalous activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the beaconing and scanning activity and was able to connect these seemingly separate events into one incident. AI Analyst analyzes thousands of connections to hundreds of different endpoints at machine speed and then summarizes its findings in a single pane of glass, giving customers the necessary information to assess the threat and begin remediation if necessary. This significantly lessens the burden for human security teams, saving them previous time and resources, while ensuring they maintain full visibility over any suspicious activity on their network.

Figure 4: Cyber AI Analyst incident log summarizing the technical details of the device’s beaconing and scanning behavior.

Beaconing Continues

Darktrace continued to observe the device carrying out beaconing activity over the next few days, likely representing threat actors attempting to establish communication with their malicious infrastructure and setting up a foothold within the customer’s environment. In one such example, the device was seen connecting to the suspicious endpoint ‘fysiotherapie-panken[.]nl’. Multiple open-source intelligence (OSINT) vendors reported this endpoint to be a known malware delivery host [3].

Once again, Darktrace RESPOND was in place to quickly intervene in response to these suspicious external connection attempts. Over the course of several days, RESPOND blocked the offending device from connecting to suspicious endpoints via port 443 and enforced its pattern of life. These autonomous actions by RESPOND effectively mitigated and contained the attack, preventing it from escalating further along the kill chain and providing the customer’s security team crucial time to take act and employ their own remediation.

Figure 5: A sample of the autonomous RESPOND actions that was applied on the affected device.

Possible Payload Retrieval

A few days later, on September 26, 2023, Darktrace observed the affected device attempting to download a Windows Portable Executable via file transfer protocol (FTP) from the external location ‘ftp2[.]sim-networks[.]com’, which had never previously been seen on the network. This download likely represented the next step in the Gootloader infection, wherein additional malicious tooling is downloaded to further cement the malicious actors’ control over the device. In response, Darktrace RESPOND immediately blocked the device from making any external connections, ensuring it could not download any suspicious files that may have rapidly escalated the attackers’ efforts.

Figure 6: DETECT’s identification of the offending device downloading a suspicious executable file via FTP.

The observed combination of beaconing activity and a suspicious file download triggered an Enhanced Monitoring breach, a high-fidelity DETECT model designed to detect activities that are more likely to be indicative of compromise. These models are monitored by the Darktrace SOC round the clock and investigated by Darktrace’s expert team of analysts as soon as suspicious activity emerges.

In this case, Darktrace’s SOC triaged the emerging activity and sent an additional notice directly to the customer’s security team, informing them of the compromise and advising on next steps. As this customer had subscribed to Darktrace’s Ask the Expert (ATE) service, they also had a team of expert analysts available to them at any time to aid their investigations.

Figure 7: Enhanced Monitoring Model investigated by the Darktrace SOC.

結論

Loader malware variants such as Gootloader often lay the groundwork for further, potentially more severe threats to be deployed within compromised networks. As such, it is crucial for organizations and their security teams to identify these threats as soon as they emerge and ensure they are effectively contained before additional payloads, like information-stealing malware or ransomware, can be downloaded.

In this instance, Darktrace demonstrated its value when faced with a multi-payload threat by detecting Gootloader at the earliest stage and responding to it with swift targeted actions, halting any suspicious connections and preventing the download of any additional malicious tooling.

Darktrace DETECT recognized that the beaconing and scanning activity performed by the affected device represented a deviation from its expected behavior and was indicative of a potential network compromise. Meanwhile, Darktrace RESPOND ensured that any suspicious activity was promptly shut down, buying crucial time for the customer’s security team to work with Darktrace’s SOC to investigate the threat and quarantine the compromised device.

Credit to: Ashiq Shafee, Cyber Security Analyst, Qing Hong Kwa, Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore

付録

Darktrace DETECT によるモデル検知

Anomalous Connection / Rare External SSL Self-Signed

Device / Suspicious SMB Scanning Activity

Anomalous Connection / Young or Invalid Certificate SSL Connections to Rare

Compromise / High Volume of Connections with Beacon Score

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / Slow Beaconing Activity To External Rare

Compromise / Beacon for 4 Days

Anomalous Connection / Suspicious Expired SSL

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / Sustained SSL or HTTP Increase

Compromise / Large Number of Suspicious Successful Connections

Compromise / Large Number of Suspicious Failed Connections

Device / Large Number of Model Breaches

Anomalous File / FTP Executable from Rare External Location

Device / Initial Breach Chain Compromise

RESPOND Models

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

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

Antigena / Network/Insider Threat/Antigena Network Scan 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

Antigena / Network / External Threat / Antigena Suspicious Activity Block

侵害指標(IoC)一覧

Type

Hostname

IoCs + Description

explorer[.]ee - C2 Endpoint

fysiotherapie-panken[.]nl- C2 Endpoint

devcxp2019.theclearingexperience[.]com- C2 Endpoint

campsite.bplaced[.]net- C2 Endpoint

coup2pompes[.]fr- C2 Endpoint

analyzetest[.]ir- Possible C2 Endpoint

tdgroup[.]ru- C2 Endpoint

ciedespuys[.]com- C2 Endpoint

fi.sexydate[.]world- C2 Endpoint

funadhoo.gov[.]mv- C2 Endpoint

geying.qiwufeng[.]com- C2 Endpoint

goodcomix[.]fun- C2 Endpoint

ftp2[.]sim-networks[.]com- Possible Payload Download Host

MITRE ATT&CK マッピング

Tactic – Technique

Reconnaissance - Scanning IP blocks (T1595.001, T1595)

Command and Control - Web Protocols , Application Layer Protocol, One-Way Communication, External Proxy, Non-Application Layer Protocol, Non-Standard Port (T1071.001/T1071, T1071, T1102.003/T1102, T1090.002/T1090, T1095, T1571)

Collection – Man in the Browser (T1185)

Resource Development - Web Services, Malware (T1583.006/T1583, T1588.001/T1588)

Persistence - Browser Extensions (T1176)

参考文献

1.     https://www.blackberry.com/us/en/solutions/endpoint-security/ransomware-protection/gootloader

2.     https://redcanary.com/threat-detection-report/threats/gootloader/

3.     https://www.virustotal.com/gui/domain/fysiotherapie-panken.nl

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Ashiq Shafee
Cyber Security Analyst

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Seven Cyber Security Predictions for 2024

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13
Feb 2024

2024 Cyber Threat Predictions

After analyzing the observed threats and trends that have affected customers across the Darktrace fleet in the second half of 2023, the Darktrace Threat Research team have made a series of predictions. These assessments highlight the threats that are expected to impact Darktrace customers and the wider threat landscape in 2024.  

1. Initial access broker malware, especially loader malware, is likely to be a prominent threat.  

Initial access malware such as loaders, information stealers, remote access trojans (RATs), and downloaders, will probably remain some of the most relevant threats to most organizations, especially when noted in the context that many are interoperable, tailorable Malware-as-a-Service (MaaS) tools.  

These types of malware often serve as a gateway for threat actors to compromise a target network before launching subsequent, and often more severe, attacks. Would-be cyber criminals are now able to purchase and deploy these malware without the need for technical expertise.  

2. Infrastructure complexity will increase SaaS attacks and leave cloud environments vulnerable.

The increasing reliance on SaaS solutions and platforms for business operations, coupled with larger attack surfaces than ever before, make it likely that attackers will continue targeting organizations’ cloud environments with account takeovers granting unauthorized access to privileged accounts. These account hijacks can be further exploited to perform a variety of nefarious activities, such as data exfiltration or launching phishing campaigns.  

It is paramount for organizations to not only fortify their SaaS environments with security strategies including multifactor authentication (MFA), regular monitoring of credential usage, and strict access control, but moreover augment SaaS security using anomaly detection.  

3. The prevalence and evolution of ransomware will surge.

The Darktrace Threat Research team anticipates a surge in Ransomware-as-a-Service (RaaS) attacks, marking a shift away from conventional ransomware. The uptick in RaaS observed in 2023 evidences that ransomware itself is becoming increasingly accessible, lowering the barrier to entry for threat actors. This surge also demonstrates how lucrative RaaS is for ransomware operators in the current threat landscape, further reinforcing a rise in RaaS.  

This development is likely to coincide with a pivot away from traditional encryption-centric ransomware tactics towards more sophisticated and advanced extortion methods. Rather than relying solely on encrypting a target’s data for ransom, malicious actors are expected to employ double or even triple extortion strategies, encrypting sensitive data but also threatening to leak or sell stolen data unless their ransom demands are met.  

4. Threat actors will continue to rely on living-off-the-land techniques.

With evolving sophistication of security tools and greater industry adoption of AI techniques, threat actors have focused more and more on living-off-the-land. The extremely high volume of vulnerabilities discovered in 2023 highlights threat actors’ persistent need to compromise trusted organizational mechanisms and infrastructure to gain a foothold in networks. Although inbox intrusions remain prevalent, the exploitation of edge infrastructure has demonstrably expanded compared to previously endpoint-focused attacks.

Given the prevalence of endpoint evasion techniques and the high proportion of tactics utilizing native programs, threat actors will likely progressively live off the land, even utilizing new techniques or vulnerabilities to do so, rather than relying on unidentified malicious programs which evade traditional detection.

5. The “as-a-Service” marketplace will contribute to an increase in multi-phase compromises.

With the increasing “as-a-Service” marketplaces, it is likely that organizations will face more multi-phase compromises, where one strain of malware is observed stealing information and that data is sold to additional threat actors or utilized for second and/or third-stage malware or ransomware.  

This trend builds on the concept of initial access brokers but utilizes basic browser scraping and data harvesting to make as much profit throughout the compromise process as possible. This will likely result in security teams observing multiple malicious tools and strains of malware during incident response and/or multi-functional malware, with attack cycles and kill chains morphing into less linear and more abstract chains of activity. This makes it more essential than ever for security teams to apply an anomaly approach to stay ahead of asymmetric threats.  

6. Generative AI will let attackers phish across language barriers.

Classic phishing scams play a numbers game, targeting as many inboxes as possible and hoping that some users take the bait, even if there are spelling and grammar errors in the email. Now, Generative AI has reduced the barrier for entry, so malicious actors do not have to speak English to produce a convincing phishing email.  

In 2024, we anticipate this to extend to other languages and regions. For example, many countries in Asia have not yet been greatly impacted by phishing. Yet Generative AI continues to develop, with improved data input yielding improved output. More phishing emails will start to be generated in various languages with increasing sophistication.    

7. AI regulation and data privacy rules will stifle AI adoption.

AI regulation, like the European Union’s AI Act and NIS2, is starting to be implemented around the world. As policies continue to come out about AI and data privacy, practical and pragmatic AI adoption becomes more complex.  

Businesses will likely have to take a second look at AI they are adopting into their tech stacks to consider what may happen if a tool is suddenly deprecated because it is no longer fit for purpose or loses the approvals in place. Many will also have to use completely different supply chain evaluations from their usual ones based on developing compliance registrars. This increased complication may make businesses reticent to adopt innovative AI solutions as legislation scrambles to keep up.  

Learn more about observed threat trends and future predictions in the 2023 End of Year Threat Report

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購入義務なし
Darktrace Threat Visualizerと組織毎にカスタマイズされた3回の脅威レポートへのフルアクセスを提供しますが、購入の義務はありません。
ありがとうございます!あなたの投稿を受け取りました。
フォームを送信する際に何らかの問題が発生しました。