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AIによるEメールセキュリティ:キーボードの前にいる人間を理解する

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30
2020年9月
30
2020年9月
Despite organizations adopting ‘secure’ email gateways and extensive employee training, 94% of cyber-attacks still start in the inbox. Cyber AI understands the human beings behind email communications and autonomously responds to anomalous emails it deems malicious, stopping attacks that other tools miss.

Eメールによる攻撃の中心は、リンクをクリックさせる、フォームに入力させる、添付ファイルを開かせるなど、受信者の関心を引きつけることです。10件のサイバー攻撃のうち9件以上がEメールから始まっており、Eメールゲートウェイの導入やフィッシング詐欺を見分けるための従業員教育など、組織が従業員を守るために最大限の努力を行っても、この攻撃手段は成功し続けているのです。

Eメール攻撃者がこのような成功を収めることができたのは、彼らが犠牲者を理解しているからです。彼らは、人間が習慣の生き物であり、間違いを犯しやすく、感情に流されやすいことを知っています。長年の経験により、攻撃者はEメールを微調整し、より説得力のある、より挑発的なEメールを作成することができるようになりました。現在では、自動化されたツールにより、犯罪者が新しいドメインを購入し、Eメールを大量に送信するスピードと規模が向上しています。これにより、攻撃手法の「A/Bテスト」がより容易になり、成功率の低い手法を捨て、成功率の高い手法を活用することができるようになりました。

フィッシングの手口は大きく5つに分類され、それぞれ異なる感情的な反応を引き起こし、人間の反応を引き出すことを目的としています。

  • 恐怖:お使いのデバイスにウイルスを検知しました、McAfeeのアカウントにログインしてください
  • 好奇心:新しいボイスメールが3件あります、ここをクリックしてください
  • 寛容さ:COVID-19は、あなたの地域のホームレスに大きな影響を与えています。今すぐ寄付してください
  • 欲張り:iPhoneのプレゼントは残り23台!急ぎましょう
  • 懸念事項:お住まいの地域でコロナウイルスが発生しました。詳細を確認する

注目すべきは、ますますダイナミックになる今日のワークフォースは、リモートワークの自宅内でしばしば孤独を感じ、新たな情報に飢えているため、こうした手法の影響を受けやすいということです。

Turning to tech

Eメールによる攻撃が巧妙化するにつれ、多くの企業は、Eメールプロバイダーに内蔵されているセキュリティツールだけでは、今日の攻撃から身を守るには十分でないことに気づいています。Eメールゲートウェイは、スパムやその他の低レベルの攻撃には有効ですが、高度な攻撃、特に新しいマルウェア、新しいドメイン、高度な技術を利用した攻撃を阻止することはできません。このような高度な攻撃は、企業にとって最も大きな損害をもたらすものでもあります。

この失敗は、従来のセキュリティツールのレガシーアプローチに内在する弱点に起因するものです。彼らは、受信メールを「既知の悪質な」IP、ドメイン、ファイルハッシュのリストと比較します。送信者と受信者は単にデータポイントとして扱われ、キーボードの背後にいる人間のニュアンスは無視されます。

これらの指標を単独で見ることは、Eメールのやりとりを行う人々を理解することによってのみ得られる完全なコンテキストを考慮することに失敗します。彼らは通常どこからログインし、誰と通信し、どのように書き、どんな種類の添付ファイルを送受信するのでしょうか。このような豊かな個人的背景があるからこそ、一見良さそうなEメールが紛れもなく悪意あるものであることが明らかになり、特に他のデータではその危険性が明らかにならないことがあるのです。

人間を誤解する

従来のツールが有効でないことに不満を感じている多くの企業は、包括的な従業員トレーニングによって、従業員が悪意のあるEメールに関与する機会を最小限に抑えることが解決策であると考えています。実際、企業は、悪意のあるEメールを発見するためのトレーニングを従業員に施し、テクノロジーによる検知能力の不足を補おうとすることがよくあります。

人間を最後の砦と考えるのは危険であり、このアプローチは、今日の高度な偽物が正規のEメールと区別がつかないように見えるという事実を見落としています。Eメールの本文、個人名、ドメイン、Eメールアドレス(信頼できる送信者の場合)などを分解して初めて、本物と偽物の区別がつくのです。

近年の大規模なデータ流出インシデントにより、攻撃者は企業のEメールや盗まれたパスワードにこれまで以上にアクセスできるようになり、サプライチェーンへの攻撃はますます一般的になっています。攻撃者が信頼できるアカウントや既存のEメールのスレッドを乗っ取った場合、従業員が微妙な文言の変化や添付文書の種類の違いに気づくことは期待できないでしょう。どんなに厳密な社内教育プログラムを実施しても、また社員がどんなに警戒していても、人間はこうした非常に微妙な指標を見抜くことができない段階にまで来ています。しかも、ワンクリックでこうしたインシデントは発生するのです。

人間を理解する

Eメールセキュリティは、長い間、複雑なサイバーセキュリティのパズルの未解決の部分として残っています。従来のツールと従業員のトレーニングの両方がうまくいかなかったため、組織は根本的に異なるアプローチを取るようになりました。官民を問わず、世界中の何千もの企業が、キーボードの裏にいる人間を理解する人工知能を使用し、ビジネス全体におけるEメールのやり取りについて、ニュアンスの異なる継続的に進化する理解を形成しています。

AIは、人間の行動、相手、文章の書き方、2人以上の人間の典型的な会話の内容を学習することによって、従業員の習慣を理解し始め、時間とともに従業員の通常の行動パターンを包括的に把握することができるようになります。最も重要なことは、AIが自己学習し、「普通」の理解を継続的に修正することです。そのため、従業員の習慣が変われば、AIの理解も変わります。

これにより、従業員の「生活パターン」、あるいは組織全体の「生活パターン」から逸脱した行動の異常を検知することができるようになりました。

このようなEメールセキュリティの根本的な新しいアプローチにより、今まで見たこともないような脅威の微妙な指標を認識し、Eメールを停止するか通過させるかを正確に判断することが可能になりました。

Eメールゲートウェイの背後に位置するこの自己学習型テクノロジーは、極めて高い捕捉率を誇っています。金融機関の幹部を装ったものから、パンデミック時の従業員の不安を煽る「フィアウェア」まで、他のツールが見落とした悪質なEメールを数え切れないほどキャッチしてきたのです。

攻撃者は革新を続けており、自動化によってEメールの脅威の新潮流が起きています。現在、セキュリティリーダーの88%が、攻撃的なAIを搭載したサイバー攻撃は避けられないと考えています。Eメールの脅威の状況は急速に変化しており、より説得力のあるデマメールが届くようになると予想されます。組織は今こそ、Eメール防御にAIを採用し、この事態に備えるべき重要な時期です。

INSIDE THE SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
AUTHOR
ABOUT ThE AUTHOR
Dan Fein
VP, Product

Based in New York, Dan joined Darktrace’s technical team in 2015, helping customers quickly achieve a complete and granular understanding of Darktrace’s product suite. Dan has a particular focus on Darktrace/Email, ensuring that it is effectively deployed in complex digital environments, and works closely with the development, marketing, sales, and technical teams. Dan holds a Bachelor’s degree in Computer Science from New York University.

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クラウド

Securing the cloud: Using business context to improve visibility and prioritize cyber risk

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26
Mar 2024

Why are businesses shifting to the cloud?

Businesses are increasingly migrating to cloud, due to its potential to streamline operations, reduce costs, and enhance scalability and flexibility. By shifting their infrastructure to the cloud, either as a whole or, more commonly in a hybrid model, organizations can access a wide array of services, such as storage, compute and software applications, without the need for extensive on-premises hardware. However, this transition isn't without challenges.  

Security challenges of cloud migration

Data security, compliance, integration with existing systems, and ensuring consistent performance are critical concerns that need to be addressed. Therefore, companies must develop robust oversight, implement comprehensive security measures, and invest in staff training to successfully navigate the transition to the cloud all while minimizing potential disruptions.

Implementing security measures within a company, however, is a complex endeavour that involves coordination among numerous internal stakeholders two of the most pivotal players involved in cloud security investment, are the security team, entrusted with crafting a business's defensive strategy, and the DevOps engineering team, architects of the infrastructure underpinning the organization's business operations.

Key questions to ask when securing the cloud

Which team is responsible for maintaining the application?  

What do they consider normal?  

How are potential misconfigurations increasing the potential risk of an incident?

Best practices of cloud security

Contextual awareness of the business is a crucial facet for securing a company's cloud infrastructure, as it enables organizations to align security measures with specific business objectives, risks, and regulatory requirements. Understanding the context of the business operations, its goals, critical assets, and compliance obligations, allows security teams to tailor their strategies and controls accordingly.

How does Darktrace help secure the cloud?

In response to the difficulties outlined above, Darktrace has adopted a holistic approach to security with an ActiveAI security platform that is context-aware. This platform enables stakeholders to effectively detect and respond to threats that may arise within their cloud or on premises environments.  

By monitoring your network and identity activity, Darktrace can identify what is considered “normal” within your organization. This however doesn’t tell the whole story. It is also important to understand where these actions are occurring within the context of the business.  

Visibility in the cloud

Without visibility into the individual assets that make up the cloud environment, how these are configured, and how they operate at run time, security is incredibly difficult to maintain. Visibility allows security teams to identify potential vulnerabilities, misconfigurations, or unauthorized access points that could be exploited by malicious actors. It enables proactive monitoring and rapid response to security incidents, ensuring that any threats are promptly identified and mitigated before they can cause significant damage.  

Building architecture diagrams

The cornerstone of our strategy lies in the architecture diagrams, which serve as a framework for organizing resources within our cloud environment. An architecture comprises of interconnected resources governed by access controls and network routing mechanisms. Its purpose is to logically group these resources into the applications they support.  

Achieving this involves compiling a comprehensive inventory of the cloud environment, analyzing resource permissions—including both outbound and inbound access—and considering any overarching organizational policies. For networked devices, we delve into route tables, firewalls, and subnet access control policies. This information is then utilized to build a graph of interconnected assets, wherein each resource constitutes a node, and the possible connections between resources are represented as edges.

Once we have built up an inventory of all the resources within your environments, we can then start building architectures based on the graph. We do this by selecting distinct starting points for graph traversal, which we infer from our deep understanding of the cloud, an example would be a Virtual Private Cloud (VPC) - A VPC is a virtual network that closely resembles a traditional network that you'd operate in your own data center.  

All networked devices are usually housed within a VPC, with applications typically grouped into one or more VPCs. If multiple VPCs are detected with peering connections between them, we consider them as distinct parts of the same system. This approach enables us to comprehend applications across regions and accounts, rather than solely from the isolated viewpoint of a single VPC.

However, the cloud isn’t all about compute instances, serverless is a popular architecture. In fact, for many developers serverless architectures offer greater scalability and flexibility. Reviewing prevalent serverless architecture patterns, we've chosen some common fundamental resources as our starting point, Lambda functions and Elastic Container Service (ECS) clusters are prime examples, serving as crucial components in various serverless systems with distinct yet similar characteristics.

Prioritize risk in the cloud

Once we have built up an inventory of all the cloud asset, Darktrace/Cloud utilizes an ‘outlier’ detection machine learning model. This looks to categorize all the assets and identifies the ones that look different or ‘odd’ when compared with the assets around it, this is based on a wide range of characteristics some of which will include, Name, VPC ID, Host Region etc, whilst also incorporating contextual knowledge of where these assets are found, and how they fit into the architecture they are in.  

Once outliers are identified, we can use this information to assess the potential risk posed by the asset. Context plays a crucial role in this stage, as incorporating observations about the asset enables effective scoring. For instance, detecting a misconfiguration, anomalous network connections, or unusual user activity can significantly raise the asset's score. Consequently, the architecture it belongs to can be flagged for further investigation.

Adapting to a dynamic cloud environment

The cloud is incredibly dynamic. Therefore, Darktrace does not see architectures as fixed entities. Instead, we're always on the lookout for changes, driven by user and service activity. This prompts us to dive back in, update our architectural view, and keep a living record of the cloud's ever-changing landscape, providing near real-time insights into what's happening within it.  

Darktrace/Cloud doesn’t just consider isolated detections, it identifies assets that have misconfigurations and anomalous activity across the network and management plane and adjusts the priority of the alerting to match the potential risk that these assets could be leveraged to enable an attack.  

While in isolation misconfigurations don’t have much meaningful impact, when they are combined with real time updates and anomaly detection within the context of the architecture you see a very important and impactful perspective.  

Combining all of this into one view where security and dev ops teams can collaborate ensures continuity across teams, playing a vital role in providing effective security.

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著者について
Adam Stevens
Analyst Technical Director

Blog

Inside the SOC

Socks5Systemz: How Darktrace’s Anomaly Detection Unraveled a Stealthy Botnet

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22
Mar 2024

What are botnets?

Although not a recent addition to the threat landscape, botnets persist as a significant concern for organizations, with many threat actors utilizing them for political, strategic, or financial gain. Botnets pose a particularly persistent threat to security teams; even if one compromised device is detected, attackers will likely have infected multiple devices and can continue to operate. Moreover, threat actors are able to easily replace the malware communication channels between infected devices and their command-and-control (C2) servers, making it incredibly difficult to remove the infection.

Botnet example: Socks5Systemz

One example of a botnet recently investigated by the Darktrace Threat Research team is Socks5Systemz. Socks5Systemz is a proxy-for-rent botnet, whereby actors can rent blocks of infected devices to perform proxying services.  Between August and November 2023, Darktrace detected indicators of Socks5Systemz botnet compromise within a cross-industry section of the customer base. Although open-source intelligence (OSINT) research of the botnet only appeared in November 2023, the anomaly-based approach of Darktrace DETECT™ allowed it to identify multiple stages of the network-based activity on affected customer systems well before traditional rules and signatures would have been implemented.

Darktrace’s Cyber AI Analyst™ complemented DETECT’s successful identification of Socks5Systemz activity on customer networks, playing a pivotal role in piecing together the seemingly separate events that comprised the wider compromise. This allowed Darktrace to build a clearer picture of the attack, empowering its customers with full visibility over emerging incidents.

In the customer environments highlighted in this blog, Darktrace RESPOND™ was not configured to operate autonomously. As a result, Socks5Systemz attacks were able to advance through their kill chains until customer security teams acted upon Darktrace’s detections and began their remediation procedures.

What is Socks5Systemz?

The Socks5Systemz botnet is a proxy service where individuals can use infected devices as proxy servers.

These devices act as ‘middlemen’, forwarding connections from malicious actors on to their intended destination. As this additional connectivity conceals the true origin of the connections, threat actors often use botnets to increase their anonymity. Although unauthorized proxy servers on a corporate network may not appear at first glance to be a priority for organizations and their security teams, complicity in proxy botnets could result in reputational damage and significant financial losses.

Since it was first observed in the wild in 2016, the Socks5Systemz botnet has grown steadily, seemingly unnoticed by cyber security professionals, and has infected a reported 10,000 devices worldwide [1]. Cyber security researchers noted a high concentration of compromised devices in India, with lower concentrations of devices infected in the United States, Latin America, Australia and multiple European and African countries [2]. Renting sections of the Socks5Systemz botnet costs between 1 USD and 4,000 USD, with options to increase the threading and time-range of the rentals [2]. Due to the lack of affected devices in Russia, some threat researchers have concluded that the botnet’s operators are likely Russian [2].

Darktrace’s Coverage of Socks5Systemz

The Darktrace Threat Research team conducted investigations into campaign-like activity across the customer base between August and November 2023, where multiple indicators of compromise (IoCs) relating to the Socks5Systemz proxy botnet were observed. Darktrace identified several stages of the attack chain described in static malware analysis by external researchers. Darktrace was also able to uncover additional IoCs and stages of the Socks5Systemz attack chain that had not featured in external threat research.

Delivery and Execution

Prior research on Socks5Systemz notes how the malware is typically delivered via user input, with delivery methods including phishing emails, exploit kits, malicious ads, and trojanized executables downloaded from peer-to-peer (P2P) networks [1].

Threat actors have also used separate malware loaders such as PrivateLoader and Amadey deliver the Socks5Systemz payload. These loaders will drop executable files that are responsible for setting up persistence and injecting the proxy bot into the infected device’s memory [2]. Although evidence of initial payload delivery did not appear during its investigations, Darktrace did discover IoCs relating to PrivateLoader and Amadey on multiple customer networks. Such activity included HTTP POST requests using PHP to rare external IPs and HTTP connections with a referrer header field, indicative of a redirected connection.

However, additional adjacent activity that may suggest initial user execution and was observed during Darktrace’s investigations. For example, an infected device on one deployment made a HTTP GET request to a rare external domain with a “.fun” top-level domain (TLD) for a PDF file. The URI also appears to have contained a client ID. While this download and HTTP request likely corresponded to the gathering and transmission of further telemetry data and infection verification [2], the downloaded PDF file may have represented a malicious payload.

Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.
Figure 1: Advanced Search log details highlighting a device infected by Socks5Systemz downloading a suspicious PDF file.

Establishing C2 Communication  

Once the proxy bot has been injected into the device’s memory, the malware attempts to contact servers owned by the botnet’s operators. Across several customer environments, Darktrace identified infected devices attempting to establish connections with such C2 servers. First, affected devices would make repeated HTTP GET requests over port 80 to rare external domains; these endpoints typically had “.ua” and “.ru” TLDs. The majority of these connection attempts were not preceded by a DNS host lookup, suggesting that the domains were already loaded in the device’s cache memory or hardcoded into the code of running processes.

Figure 2: Breach log data connections identifying repeated unusual HTTP connections over port 80 for domains without prior DNS host lookup.

While most initial HTTP GET requests across investigated incidents did not feature DNS host lookups, Darktrace did identify affected devices on a small number of customer environments performing a series of DNS host lookups for seemingly algorithmically generated domains (DGA). These domains feature the same TLDs as those seen in connections without prior DNS host lookups.  

Figure 3: Cyber AI Analyst data indicating a subset of DGAs queried via DNS by infected devices.

These DNS requests follow the activity reported by researchers, where infected devices query a hardcoded DNS server controlled by the threat actor for an DGA domain [2]. However, as the bulk of Darktrace’s investigations presented HTTP requests without a prior DNS host lookup, this activity indicates a significant deviation from the behavior reported by OSINT sources. This could indicate that multiple variations of the Socks5Systemz botnet were circulating at the time of investigation.

Most hostnames observed during this time of investigation follow a specific regular expression format: /[a-z]{7}\.(ua|net|info|com|ru)/ or /[a-z0-9]{15}\.(ua)/. Darktrace also noticed the HTTP GET requests for DGA domains followed a consistent URI pattern: /single.php?c=<STRING>. The requests were also commonly made using the “Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)” user agent over port 80.

This URI pattern observed during Darktrace’s investigations appears to reflect infected devices contacting Socks5Systemz C2 servers to register the system and details of the host, and signal it is ready to receive further instructions [2]. These URIs are encrypted with a RC4 stream cipher and contain information relating to the device’s operating system and architecture, as well as details of the infection.

The HTTP GET requests during this time, which involved devices made to a variety a variety of similar DGA domains, appeared alongside IP addresses that were later identified as Socks5Systemz C2 servers.

Figure 4: Cyber AI Analyst investigation details highlighting HTTP GET activity whereby RC4 encrypted data is sent to proxy C2 domains.

However, not all affected devices observed by Darktrace used DGA domains to transmit RC4 encoded data. Some investigated systems were observed making similar HTTP GET requests over port 80, albeit to the external domain: “bddns[.]cc”, using the aforementioned Mozilla user agent. During these requests, Darktrace identified a consistent URI pattern, similar to that seen in the DGA domain GET requests: /sign/<RC4 cipher text>.  

Darktrace DETECT recognized the rarity of the domains and IPs that were connected to by affected devices, as well as the usage of the new Mozilla user agent.  The HTTP connections, and the corresponding Darktrace DETECT model breaches, parallel the analysis made by external researchers: if the initial DGA DNS requests do not return a valid C2 server, infected devices connect to, and request the IP address of a server from, the above-mentioned domain [2].

Connection to Proxy

After sending host and infection details via HTTP and receiving commands from the C2 server, affected devices were frequently observed initiating activity to join the Sock5Systemz botnet. Infected hosts would first make HTTP GET requests to an IP identified as Socks5Systemz’s proxy checker application, usually sending the URI “proxy-activity.txt” to the domain over the HTTP protocol. This likely represents an additional validation check to confirm that the infected device is ready to join the botnet.

Figure 5: Cyber AI Analyst investigation detailing HTTP GET requests over port 80 to the Socks5Systemz Proxy Checker Application.

Following the final validation checks, devices would then attempt TCP connections to a range of IPs, which have been associated with BackConnect proxy servers, over port 1074. At this point, the device is able to receive commands from actors who login to and operate the corresponding BackConnect server. This BackConnect server will transmit traffic from the user renting the segment of the botnet [2].

Darktrace observed a range of activity associated with this stage of the attack, including the use of new or unusual user agents, connections to suspicious IPs, and other anomalous external connectivity which represented a deviation from affected devices’ expected behavior.

Additional Activities Following Proxy Addition

The Darktrace Threat Research team found evidence of the possible deployment of additional malware strains during their investigation into devices affected by Socks5Systemz. IoCs associated with both the Amadey and PrivateLoader loader malware strains, both of which are known to distribute Socks5Systemz, were also observed on affected devices. Additionally, Darktrace observed multiple infected systems performing cryptocurrency mining operations around the time of the Sock5Systemz compromise, utilizing the MinerGate protocol to conduct login and job functions, as well as making DNS requests for mining pools.

While such behavior would fall outside of the expected activity for Socks5Systemz and cannot be definitively attributed to it, Darktrace did observe devices affected by the botnet performing additional malicious downloads and operations during its investigations.

結論

Ultimately, Darktrace’s anomaly-based approach to threat detection enabled it to effectively identify and alert for malicious Socks5Systemz botnet activity long before external researchers had documented its IoCs and tactics, techniques, and procedures (TTPs).  

In fact, Darktrace not only identified multiple distinct attack phases later outlined in external research but also uncovered deviations from these expected patterns of behavior. By proactively detecting emerging threats through anomaly detection rather than relying on existing threat intelligence, Darktrace is well positioned to detect evolving threats like Socks5Systemz, regardless of what their future iterations might look like.

Faced with the threat of persistent botnets, it is crucial for organizations to detect malicious activity in its early stages before additional devices are compromised, making it increasingly difficult to remediate. Darktrace’s suite of products enables the swift and effective detection of such threats. Moreover, when enabled in autonomous response mode, Darktrace RESPOND is uniquely positioned to take immediate, targeted actions to contain these attacks from the onset.

Credit to Adam Potter, Cyber Security Analyst, Anna Gilbertson, Cyber Security Analyst

付録

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Beaconing Activity To External Rare
  • Compromise / DGA Beacon
  • Compromise / Beacon to Young Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Long Period)
  • Device / New User Agent
  • Device / New User Agent and New IP

Cyber AI Analyst Incidents

  • HTTP コマンド&コントロールの可能性
  • Possible HTTP Command and Control to Multiple Endpoints
  • Unusual Repeated Connections
  • Unusual Repeated Connections to Multiple Endpoints
  • Multiple DNS Requests for Algorithmically Generated Domains

侵害インジケータ

IoC - Type - Description

185.141.63[.]172 - IP Address - Socks5Systemz C2 Endpoint

193.242.211[.]141 - IP Address - Socks5Systemz C2 Endpoint

109.230.199[.]181 - IP Address - Socks5Systemz C2 Endpoint

109.236.88[.]134 - IP Address - Socks5Systemz C2 Endpoint

217.23.5[.]14 - IP Address - Socks5Systemz Proxy Checker App

88.80.148[.]8 - IP Address - Socks5Systemz Backconnect Endpoint

88.80.148[.]219 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]4 - IP Address - Socks5Systemz Backconnect Endpoint

185.141.63[.]2 - IP Address - Socks5Systemz Backconnect Endpoint

195.154.188[.]211 - IP Address - Socks5Systemz Backconnect Endpoint

91.92.111[.]132 - IP Address - Socks5Systemz Backconnect Endpoint

91.121.30[.]185 - IP Address - Socks5Systemz Backconnect Endpoint

94.23.58[.]173 - IP Address - Socks5Systemz Backconnect Endpoint

37.187.148[.]204 - IP Address - Socks5Systemz Backconnect Endpoint

188.165.192[.]18 - IP Address - Socks5Systemz Backconnect Endpoint

/single.php?c=<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/sign/<RC4 data hex encoded> - URI - Socks5Systemz HTTP GET Request

/proxy-activity.txt - URI - Socks5Systemz HTTP GET Request

datasheet[.]fun - Hostname - Socks5Systemz C2 Endpoint

bddns[.]cc - Hostname - Socks5Systemz C2 Endpoint

send-monitoring[.]bit - Hostname - Socks5Systemz C2 Endpoint

MITRE ATT&CK マッピング

コマンド&コントロール

T1071 - アプリケーションレイヤープロトコル

T1071.001 – Web protocols

T1568 – Dynamic Resolution

T1568.002 – Domain Generation Algorithms

T1132 – Data Encoding

T1132 – Non-Standard Encoding

T1090 – Proxy

T1090.002 – External Proxy

持ち出し

T1041 – Exfiltration over C2 channel

影響

T1496 – Resource Hijacking

参考文献

1. https://www.bleepingcomputer.com/news/security/socks5systemz-proxy-service-infects-10-000-systems-worldwide/

2. https://www.bitsight.com/blog/unveiling-socks5systemz-rise-new-proxy-service-privateloader-and-amadey

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著者について
Adam Potter
Cyber Analyst
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