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適切なAIサイバーセキュリティ製品を探す

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20
Dec 2022
20
Dec 2022
This blog explores the nuances of AI in cyber security, how to identify true AI, and considerations when integrating AI technology with people, processes, and other technology.

AIは長きにわたりバズワードとなっており、ソーシャルメディアや電子商取引、さらには音楽の好みなど、消費者向けの様々な領域で活用され始めています。ここ数年、AIは企業、特にサイバーセキュリティの分野で活用され始めています。

脅威アクターが攻撃手法にAIを活用するケースは増えています。これは、AI技術の進歩、サイバーセキュリティ業界への参入障壁の低下、脅威アクターとしての継続的な収益性を考慮すると必然的なことです。金融サービスや製造業など異なる業界のセキュリティ意思決定者を調査したところ、回答者の77%が武器化されたAIによって攻撃の規模や速度が増大すると予想しています。 

防御側もサイバーセキュリティにおけるAIの活用を強化しており、回答者の80%以上が、攻撃的なAIに対抗するために組織が高度な防御を必要とすることに同意していることから、敵対者とセキュリティチームが最新の技術的進歩を常に追い求める「サイバー軍拡競争」の様相を呈しています。  

この進化する脅威の状況において、ルールとシグネチャのアプローチはもはや十分ではありません。このようなニーズの高まりから、この分野でもAIによるイノベーションの後押しが続くでしょう。2025年までに、サイバーセキュリティ技術はAIソフトウェア市場の25%を占めるようになると予測しています。

AIを取り巻く興味深さとは裏腹に、多くの人はAIが本当にどのように機能するのか、限定的な理解しか持っていません。AI技術の謎は、多くのサイバーセキュリティ実務家の興味をかきたてるものです。業界として、AIが進歩のために必要であることも分かっていますが、AIや機械学習の周辺にはノイズが多く、理解に苦しむチームもあります。選択のパラドックスにより、セキュリティチームは、提示されたすべての選択肢に対して、よりフラストレーションを感じ、混乱しているのです。

真のAIを見極める

まず、AI技術で解決したいことを定義する必要があります。これは些細なことに思えるかもしれませんが、多くのセキュリティチームは、「どのような問題に取り組んでいるのか」という基本に立ち返ることを忘れてしまいがちです。どのような問題に取り組んでいるのか?何を改善しようとしているのか?ということです。 

すべてのプロセスにAIが必要なわけではありません。いくつかのプロセスは、単に自動化が必要なケースです。より複雑で大規模なシステムには、AIが必要です。重要なのは、ビジネスのこれらの部分を特定し、AIを適用し、これらのAI技術で何を達成しようとしているのかを明確にすることです。 

例えば、工場の現場作業や従業員の休暇日の把握などでは、企業は自動化技術を採用しますが、広報戦略や新規事業の開拓などの経営判断では、AIを活用してトレンドを予測し、経営者の意思決定を支援します。 

同様にサイバーセキュリティにおいても、既知の悪意あるマルウェアやホスティングサイトなどの既知の脅威に対応する場合、それらを把握するのは自動化が最適で、ワークフローやプレイブックも自動化ツールで評価するのがベストです。しかし、ゼロデイ攻撃、インサイダー脅威、IoT脅威、サプライチェーン攻撃などの未知の未知の脅威に関しては、これらの脅威が出現したときに予兆段階で検知し対応するためにAIが必要となります。

自動化はAIとして伝えられることが多く、セキュリティチームにとって区別が難しくなります。自動化は、すでに知っている判断を素早く行うのに役立ちますが、真のAIはより良い判断を下すのに役立ちます。

真のAIと自動化を区別するための重要な方法を挙げます:

  • データセット:自動化では、探しているものは非常によく計画されています。何を探しているかはすでに分かっており、ルールや署名によってプロセスを加速しているだけなのです。真のAIはより動的でダイナミックなものです。AIは、あなたの注意を引くべき活動を定義する必要はなく、あなたのためにハイライトし、優先順位をつけます。
  • バイアス:何を求めているかを定義するとき、人間である私たちは本来、これらの判断にバイアスをかけてしまうものです。また、その時点の知識にも限界があります。これでは、肝心の未知の部分が抜け落ちてしまいます。
  • リアルタイム:どの組織も常に変化しており、AIがそのデータをすべて考慮することが重要です。リアルタイムで、しかも組織の成長に合わせて変化する真のAIは、なかなかありません。 

ダークトレースのAI研究センターは、サイバーセキュリティにおける真のAIの応用について数多くの論文を発表しています。当センターは150名以上のメンバーで構成され、100件以上の特許と出願中の特許を保有しています。ホワイトペーパーの中には、攻撃経路モデリングに関する研究や、組織における予防的アプローチとしてのAIの活用などが含まれています。 

AIのアウトプットを人、プロセス、テクノロジーと統合する


AIと人の融合

私たちは信頼不足の時代に生きていますが、それはAIにも当てはまります。人間である私たちはAIに対して懐疑的になることがありますが、では、私たちのために働くようなAIに対する信頼をどのように構築すればよいのでしょうか。これは、当該技術を利用するユーザーだけでなく、組織全体にも当てはまることです。これは「人」の柱なので、AIに対する信頼を得るための重要な要因は、教育、文化、そしてエクスポージャーです。新しいAI技術を学び、試すことにオープンである文化があれば、時間の経過とともにAIに対する信頼が自然に構築されていくことでしょう。

AIとプロセスの統合

その上で、AIとそのアウトプットをワークフローやプレイブックに統合することを検討すべきです。そのあたりの意思決定をするために、セキュリティ管理者は、自分たちのセキュリティの優先順位は何か、あるいは特定のテクノロジーがどのセキュリティギャップを埋めるためのものかを明確にする必要があります。アウトソーシングしたMSSP/SOCチーム、50人規模の社内SOCチーム、あるいは2人だけのチームであろうと、優先順位を理解し、適切なリソースを割り当てることが重要なのです。

AIとテクノロジーの融合 

最後に、既存のテクノロジースタックとAIの統合があります。ほとんどのセキュリティチームは、SIEM、ファイアウォール、エンドポイントなどのツールや、ペンテスト、脆弱性評価演習などのサービスなど、さまざまな目的を達成するために、さまざまなツールやサービスを導入しています。最大の課題の 1 つは、これらの情報をすべてまとめ、そこから実用的な洞察を引き出すことです。複雑な技術では、脅威の評価や解釈が異なるため、複数のレベルで統合することは常に困難です。

セキュリティチームは、さまざまなツールやサービスの出力結果を理解することに最も時間を費やしていることがよくあります。例えば、ペンテストレポートの結果を基にSOARの設定を強化したり、SOCのアラートを基にファイアウォールの設定をアドバイスしたり、脆弱性評価レポートを基に第三者のインシデント対応チームの範囲を決めたりします。

これらのツールは大量のデータを強力に操ることができますが、最終的に知識の所有権は人間のチームにあるべきで、それを実現する方法が継続的なフィードバックと統合なのです。このようなことを大規模かつ迅速に行うには、もはや人間のチームを利用するのは効率的ではありません。 

Cyber AI Loopは、Darktraceのサイバーセキュリティに対するアプローチです。4つの製品ファミリーは、組織のサイバーセキュリティ態勢の重要な側面を構成しています。Darktrace PREVENT, DETECT, RESPOND, HEALの各製品は、それぞれが継続的な好循環にフィードバックし、常に互いの能力を強化し合っています。 

このサイクルは、インシデントのライフサイクルのあらゆる段階で人間を補強します。例えば、PREVENT は、組織にとって特にリスクが高い可能性がある脆弱性を警告することができます。この場合、明確な緩和策が提示され、その間に攻撃が発生すると、PREVENT が DETECT と RESPOND に転送され、直ちに攻撃が開始されるように準備されます。逆に、RESPOND が攻撃を食い止めると、PREVENT に情報がフィードバックされ、攻撃者の次の行動を予測します。サイバー AI ループ は、毎月、毎年、組織が継続的に防御態勢を改善できるように、全体的な方法でセキュリティを強化することを支援します。 

説明可能なAI

AIがその複雑さにもかかわらず有用であるためには、明確で理解しやすいアウトプットを生成する必要があります。サイバーインシデントが発生した瞬間、人間のチームは素早く理解する必要があります。ここで何が起こったのか?いつ発生したのか?どのデバイスが影響を受けたのか?私のビジネスにとって、それは何を意味するのか?何を優先して対処すべきか?

このため、Darktrace は、最初の調査結果の上にもう一段階のAIを適用し、バックグラウンドで自律的に調査を行い、大量の個々のセキュリティ事象を、人間がレビューする価値のあるわずかなサイバーインシデント全体にまで絞り込みます。また、関連するすべての情報を含む自然言語によるインシデントレポートを生成し、お客様のチームが瞬時に判断できるようにします。 

図 1: Darktrace が個々のモデルブリーチをインシデントに、そして人間がレビューするためのクリティカルインシデントにフィルタリングする例 

Cyber AI Analystは、ネットワークだけでなく、エンドポイント、クラウド環境、IoTデバイス、OTデバイスの検知も考慮します。また、Cyber AI Analystは、攻撃対象や関連するリスクに着目してトリアージを行い、予期せぬ事態が発生した場合に組織に最大の損害をもたらすような最も優先順位の高いアラートを表示します。これらのインサイトは、リアルタイムで提供されるだけでなく、お客様の環境に固有のものです。

これは、AIをめぐる議論で頻繁に出てくるもう一つのトピック、「誤検知」への対処にも役立ちます。もちろん、これは正当な懸念です。もし、小さなチームが何千ものアラートを見なければならないのであれば、AIの価値を引き出すことに何の意味があるのでしょうか。しかし、AIによって膨大な量のログをより多く関連付けることができるようになる一方で、その目的はセキュリティチームの仕事を増やすことではなく、むしろ補強することにあることを忘れてはなりません。

ビジネスがこれらのAIのアウトプット、そしてより重要な知識を所有し続けることができるようにするには、Darktraceの Cyber AI Analystで使われているような説明可能なAIが、AIの発見を解釈し、人間のチームが何が起こったのか、もしあればAIがどんな行動をとったのか、そしてなぜそうなったのかを確実に知るために必要なのです。 

結論

組織はそれぞれ異なり、そのセキュリティもそれを反映したものであるべきです。しかし、サイバーセキュリティにおけるAIの基本的な共通課題は、規模、リソース、業種、文化に関係なく、すべてのセキュリティチームに共通しています。そのサイバー戦略と成熟度こそが、各チームを際立たせているのです。成熟度は、専門的な資格の数やチームの経験年数で定義されるものではありません。成熟したチームは、問題を解決するために協力し合います。AIは銀の弾丸ではありませんが、正しく使用すれば、デジタルエコシステム全体のセキュリティを自律的に強化し、その防御に当たる人間を補強する強力な弾丸であることを理解しています。 

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
Germaine Tan
VP of Cyber Risk Management

Germaine is the Director of Analysis, APAC at Darktrace. Based in Singapore, she works with CISOs, managers and security teams all over APAC on model optimization and operationalization of Darktrace in their digital environments. She also manages the team of 17 analysts in the APAC region that threat hunts and monitors networks from all over the world. Germaine holds a Bachelor of Science in Engineering and a Masters of Science in Technology Management from Nanyang Technological University. She is CISSP, CRISC and CEH certified.

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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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Adam Stevens
Analyst Technical Director

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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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