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企業安全營運:將 AWS WAF 遙測資料整合到 Graylog 中

在 Graylog 中分析 AWS WAF 應用程式遙測數據

維運藍圖:透過 Illuminate 標準化 Web ACL 承載資料,將原始 JSON 串流轉化為可搜尋的威脅情資

資安維運簡報: 當公開應用程式運行在 Amazon CloudFront、Application Load Balancer (ALB)、API Gateway 或 AWS AppSync 後端時,AWS WAF 扮演著抵禦網頁漏洞利用的第一線關鍵守門員。然而,原始的防火牆日誌經常受困於未經解析的雲端儲存庫中,成了工程維運上的視線盲區。Graylog Illuminate AWS WAF 內容包(Content Pack)自動分解複雜的 JSON 承載資料,將網頁事件對映到規範模式(Canonical schema),並將活躍的偵測事件直接饋送至企業的 SIEM 管線中,藉此橋接此一可視性破口。

深層攝取 WAF 日誌的戰略優勢

亞馬遜網路服務網頁應用程式防火牆(AWS WAF)會針對進入 Application Load Balancer、CloudFront 分布、API Gateway 及 AppSync 的 HTTP 與 HTTPS 請求,對照託管或自訂的存取控制清單(Web ACLs)進行評估。每一次請求都會觸發一個獨立的動作——不論是明確的 ALLOW(允許)、硬性的 BLOCK(阻斷)、用於分析的 COUNT(計數),或是透過 CAPTCHA(驗證碼) 或基於 JavaScript 的瀏覽器 CHALLENGE(挑戰) 模組進行階梯式驗證。

由於 WAF 層直接部署在面向網際網路之服務的最前線,其遙測數據精確記錄了攻擊者正在探測的攻擊模式。深層攝取這些存取日誌,能讓安全維運中心(SOC)洞察零日漏洞探測、憑證填補(Credential-stuffing)趨勢,以及動態應用程式安全規則的實際防禦成效。

 

架構概述:The Illuminate 處理管線

AWS WAF 內容包專為 AWS WAF v2(新版 Web ACL)架構設計,並採用 Amazon Kinesis Data Firehose 進行實時傳輸。一旦啟用,該套件即會執行自動化的提取、富集(Enrichment)與標準化迴圈:

自動化內容包構件

  • 統一攝取串流: 配置 Illuminate: AWS WAF Messages 串流,無需手動設定路由參數,即可立即管理入站的承載資料。
  • 預先建立索引的資料保留: 實施專門的索引集(Index set),並針對預設的每日輪轉與 90 天保留期限進行優化,確保在遭遇高密度掃描事件時,維運效能依舊維持穩定。
  • Graylog 資訊模型 (GIM) 標準化: 將未經解析的 HTTP 請求、來源參數以及結束特徵碼對照 GIM 綱要進行標準化,將原始的雲端變數轉化為標準的企業告警類別。
  • 統一的 Spotlight 主控台: 部署集中式分析介面,實時摘要阻斷率、挑戰結果、地理流量異常以及規則比對狀況。

 

資料攝取組態配置

將您的 AWS 環境與 Graylog 整合,需要一個作用中的 Enterprise 或 Security 授權,並搭配 Illuminate 處理框架。請遵循以下部署順序來建立資料路由:

步驟 1:啟用 Web ACL 日誌記錄

導覽至目標 AWS Web ACL 配置面板。啟用日誌記錄,並指定一個 Amazon Kinesis Data Firehose 傳輸串流 作為您的日誌交付目的地。

步驟 2:將記錄串流至 Graylog

使用原生的 AWS Kinesis/CloudWatch 輸入(Input),配置 Kinesis Data Firehose 串流將記錄轉發至 Graylog 實例。Graylog 的輸入引擎會利用 aws_kinesis_streamaws_kinesis_message_type 屬性自動為流量標記標籤,這意味著您無需配置特定的序列名稱比對規則。

步驟 3:核實 JSON 日誌結構

AWS WAF 會針對其評估的每項請求交付單個詳細的 JSON 物件。Graylog 處理引擎會自動標準化這些變體:

範例 A:觸發規則宣告無效並做出終止性 BLOCK 決策

{
  "timestamp": 1778140491525,
  "formatVersion": 1,
  "webaclId": "arn:aws:wafv2:us-east-1:517803882956:regional/webacl/prod-waf/3b8a9ef3-9057-4538-acdd-bda6c27c2131",
  "terminatingRuleId": "AWS-AWSManagedRulesCommonRuleSet",
  "terminatingRuleType": "MANAGED_RULE_GROUP",
  "action": "BLOCK",
  "httpSourceName": "ALB",
  "httpRequest": {
    "clientIp": "185.142.236.41",
    "country": "SC",
    "uri": "/.well-known/security.txt",
    "httpMethod": "GET",
    "scheme": "http",
    "host": "34.238.104.93"
  },
  "labels": [
    { "name": "awswaf:managed:aws:core-rule-set:NoUserAgent_Header" }
  ]
}

範例 B:標準預設動作之 ALLOW 決策

{
  "timestamp": 1778154180000,
  "formatVersion": 1,
  "webaclId": "arn:aws:wafv2:us-east-1:517803882956:regional/webacl/prod-waf/3b8a9ef3-9057-4538-acdd-bda6c27c2131",
  "terminatingRuleId": "Default_Action",
  "terminatingRuleType": "REGULAR",
  "action": "ALLOW",
  "httpSourceName": "ALB",
  "httpRequest": {
    "clientIp": "192.0.2.10",
    "country": "US",
    "uri": "/",
    "httpMethod": "GET",
    "scheme": "https",
    "host": "app.example.com"
  },
  "labels": []
}

步驟 4:初始化處理引擎

登入 Graylog 主控台,導覽至 Enterprise → Illuminate,找到 AWS WAF Processing Pack,然後點擊 Activate(啟用)。索引架構與解析邏輯隨即會在後端即時初始化。

 

Graylog 資訊模型 (GIM) 分類綱要

該內容包將 AWS WAF 的動作映射至標準化的 GIM 欄位中,將流量歸類至網路與偵測類別,以實現一致性的威脅獵捕:

日誌類型廠商動作描述GIM 類別GIM 子類別GIM 事件代碼
aws_wafWAF BLOCK – 請求因比對到終止規則而被阻斷detectiondetection.network detection300001
aws_wafWAF CAPTCHA – 向用戶端調用階梯式互動驗證碼挑戰detectiondetection.network detection300001
aws_wafWAF CHALLENGE – 調用靜默 JavaScript 瀏覽器驗證挑戰detectiondetection.network detection300001
aws_wafWAF ALLOW – 請求通過驗證並放行至後端源伺服器networknetwork.default129999
aws_wafWAF COUNT – 規則比對僅作為遙測數據記錄networknetwork.default129999

 

建構 AWS WAF 可視性的戰略驅動因素

將 WAF 基礎設施指標集中於 Graylog,能為防禦團隊在四個關鍵資安領域中提供核心的技術證據:

1. 實時攻擊監控

  • 在惡意網頁載荷——包括 SQL 注入(SQLi)與跨網站指令碼(XSS)模式——到達您的應用程式伺服器之前,揭露被 AWS 託管規則攔截的軌跡。
  • 追蹤與單一用戶端 IP 設定檔關聯的高頻率 BLOCK 觸發器,以逮獲自動化網頁搜刮(Scraping)、目錄暴力破解及漏洞掃描。
  • 監控 COUNT 規則,在將新起草的自訂規則切換至實際阻斷模式之前,先評估其在真實環境下的影響。
  • 識別高機率的 CAPTCHACHALLENGE 失敗率,以追蹤企圖規避應用程式存取控制的殭屍網路(Botnets)。

2. 多層次威脅獵捕

  • 允許分析師跨越整個應用程式設備艦隊,搜尋與活躍的 CVE 漏洞利用或已知網路間諜活動相關聯的特定終止規則 ID(Terminating Rule ID)。
  • ALLOWBLOCK 事件之間交叉比對來源 IP 參數,以追蹤攻擊者如何企圖對映您的應用程式結構。
  • 標記非預期的地理位置異常,這些異常正對正式環境的 Web ACL 端點發送大量的阻斷流量。

3. 事件應變與數位取證調查

  • 為數位取證團隊提供不可篡改的請求歷史時間軸,以便在活躍事件期間迅速重構攻擊者的行動軌跡。
  • 精確定位攔截到攻擊的特定規則或受管群組,並輕鬆識別規則漏失繞過企圖的任何防禦破口。
  • 審查圍繞在 BLOCK 行為周遭的 ALLOW 流量,以確切釐清在此之前有哪些請求已成功到達後端源儲存庫。

4. 合規性與維運審計

  • 維護面向公眾之 WAF 政策所做出的執行決策之不可篡改、長期的日誌記錄,以證實符合 PCI DSS、HIPAA 和 SOC 2 下嚴格的存取控制授權。
  • 追蹤 Web ACL 與規則群組的組態配置調整,為變更管理團隊提供可驗證的合規證據。

關於 Graylog
Graylog 通過完整的 SIEM、企業日誌管理和 API 安全解決方案,提升公司企業網絡安全能力。Graylog 集中監控攻擊面並進行深入調查,提供卓越的威脅檢測和事件回應。公司獨特結合 AI / ML 技術、先進的分析和直觀的設計,簡化了網絡安全操作。與競爭對手複雜且昂貴的設置不同,Graylog 提供強大且經濟實惠的解決方案,幫助公司企業輕鬆應對安全挑戰。Graylog 成立於德國漢堡,目前總部位於美國休斯頓,服務覆蓋超過 180 個國家。

關於 Version 2 Digital
Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products. Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

AI驅動的軟體品質:將Perforce靜態分析與模型上下文協議(MCP)集成

AI 輔助程式碼修復:將任何 MCP 主機連接至 Perforce 靜態分析

透過模型內容協定(MCP)建構解耦、合規且無特定模型限制的重構工作流

戰略性工程簡報: 傳統的靜態應用程式安全測試(SAST)極其擅長識別邏輯缺陷、安全漏洞與合規性漂移。然而,修復這些程式碼瑕疵在歷史上往往需要人工進行程式碼檢查與驗證。透過將底層的大型語言模型(LLM)與專有的開發者環境解耦,Perforce 靜態分析 MCP 伺服器(Perforce Static Analysis MCP Server)允許工程師直接在他們偏好的 IDE 和編排工具中,協調自動化、具備合規性且感知情境的程式碼重構,從而維持如 MISRA 和 CERT 等嚴格的合規標準。

超越診斷門禁的演進

歷史上,靜態分析扮演著診斷門禁的角色——標記語法違規或競態條件(Race conditions),同時將根本原因分析、程式碼重構和迴歸測試的人工作業完全留給開發者。這種維運上的斷層在高速度的開發管線中引發了摩擦。

原生 AI 輔助程式碼修復的導入改變了這一動態。Perforce 靜態分析套件不再只是向團隊發出架構缺陷告警,而是槓桿結構化的情境後設資料(Metadata)來提出精確、符合標準的修復修補程式。開發者只需審查並核准建議的變更,即可讓 AI 引擎自動將修復套用到在地程式碼庫中。

在您的在地建置管線(Build pipeline)中評估我們的自動化程式碼修復框架。

 

Perforce MCP 伺服器的架構機制

模型內容協定(Model Context Protocol, MCP)作為一種開放標準介面,安全地橋接了大型語言模型(LLM)與外部遙測工具、資料儲存庫及建置執行階段。Perforce 靜態分析 MCP 伺服器充當一個抽象層,直接部署在您的核心引擎編譯器前,將分析指標暴露給任何相容的用戶端執行階段。

在標準的生產環境中,開發者在其在地情境中執行一個在地化的 MCP 伺服器實例。當一個支援開放標準的用戶端——例如啟用 MCP 的 IDE——註冊了該伺服器端點,在地模型便能透過一個結構化的五階段執行迴圈,直接、實時地存取底層的靜態分析引擎:

  1. 漸進式編譯與掃描: 開發者在編輯器內部在地編寫並測試程式碼,按需觸發漸進式掃描,以儘早擷取漏洞與編碼標準偏離。
  2. 情境脈絡攝取: 當工程師鎖定特定缺陷以進行自動化修復時,主機 LLM 會查詢 MCP 端點以攝取所有相關情境,包括語法路徑與語意規則。
  3. 修復方案合成: LLM 處理結構化承載資料以生成精確的程式碼修正,並將提出的修補程式顯示在在地對話介面或統一的 side-by-side 差異(Diff)視窗中。
  4. 自動化迴歸測試: 修復方案一經提出,在地建置引擎便會對修改後的區塊執行自動重新分析,驗證該變更在解決問題的同時,不會引入新的漏洞。
  5. 人員在環(Human-in-the-Loop)核實: 開發者審查最終的診斷輸出,核准經驗證的程式碼修正,以確保品質與合規標準維持完好。

 

為什麼靈活性主導了現代 AI 治理

現代企業工程團隊很少使用單一、統一的工具組。強迫不同的開發群體——例如終端優先(Terminal-first)的系統工程師與依賴 IDE 的應用程式開發者——整合到單一專有的工作空間介面會引入摩擦並降低採用率。擁抱開放的 MCP 模型交付了清晰的優勢:

  • 保留受信任的工作空間: 該協定原生整合到您現有的開發環境中,無需放棄偏好的 IDE 或手動重構工具。
  • 與模型無關的自由選擇: 對安全敏感的團隊可以將程式碼情境路由至在地離線模型以維護資料隱私,而優化複雜任務的團隊則能利用高效能的雲端 LLM。
  • 減輕廠商鎖定風險: 隨著 AI 地景的快速演進,組織可以更換底層的語言模型或編輯器環境,而無需重新建構其靜態分析管線。
  • 強制執行持續性合規: 無論連接何種主機或模型,每一次建議的修補程式都會對照配置的規則設定檔(包括 MISRA、CERT 及內部企業標準)進行驗證。

 

三大相容 MCP 的主機類別

Perforce 靜態分析 MCP 伺服器可輕鬆連接至多個開發用戶端,這些用戶端在實務上大致歸類為三個不同的架構類別:

1. 對話式 AI 介面

獨立的桌面應用程式與網頁助理(如 Claude Desktop 或 Claude.ai)連結至 MCP 伺服器,以獲取合成精確程式碼建議所需的詳細資料與文件。這允許開發者在自然語言對話中審計發現、探索複雜的 MISRA 違規並生成經修正的程式碼片段。

2. 整合式開發環境 (IDEs)

次世代的環境與編輯器——包括執行 GitHub Copilot 的 Visual Studio Code——為修復工作流提供了直接的內嵌式(Inline)整合。此連線允許開發者在活躍的檔案分頁中直接接收違規告警、通俗易懂的根本原因解釋以及預先驗證的程式碼修復,使其專注於程式碼本身。

3. 代理人(Agentic)與自動化框架

高階編排框架(如 LangChain、AutoGen 及自訂的代理人執行階段)代表了自動化工具光譜的極致。這些系統不再等待手動的使用者查詢,而是自主協調跨越獨立 MCP 平台的複雜多步驟工作流以擷取情境並採取行動。代理人可以從 Perforce 伺服器攝取發現、生成候選修復方案、執行自動化迴歸測試,並開啟完全經驗證的拉取請求(Pull request)供人工審查。

 

受支援的 AI 工具與部署組態配置

Perforce 靜態分析 MCP 伺服器的開放標準特性,實現了與多種公開和私有 AI 工具的即插即用整合:

  • Claude Code: 專為快速終端工作流優化的命令列優先環境。由於其缺乏圖形化差異視窗,開發者可以明確提示 Claude Code 來顯示程式碼變更,或使用官方的 Claude Code VS Code 插件將終端體驗帶入圖形化檢視中。
  • Cursor: 圍繞模型輔助開發設計的 AI 優先程式碼編輯器。Cursor 原生連接至 Perforce MCP 伺服器,利用其配置的語言模型生成精確的內嵌式程式碼修復。
  • Ollama 與在地化部署: 對於具備嚴格資料主權要求、無法將程式碼傳送至網路邊界之外的組織,Ollama 允許在專屬的在地硬體上執行私有模型。Perforce MCP 伺服器連接到在地模型與連接到雲端 LLM 一樣輕鬆,提供了一個安全、完全離線的修復管線。

 

為合規程式碼庫提供的無縫整合

Perforce 靜態分析 AI 輔輔助修復的真正威力在於它獨立於任何單一的模型供應商或編輯器介面。藉由插入您團隊已信任的開發環境,您可以連接最符合預算與合規需求的工具,同時確保所有生成的程式碼依舊緊緊綁定於受信任引擎(如 Perforce Helix QAC 和 Perforce Klocwork)的嚴格標準之上。

這種方法允許您按照自己的步調導入 AI——儘早擷取瑕疵、更快速地解決它們,並在整個過程中讓開發者維持主導控制。

準備好自動化您的修復管線了嗎?

獲取 Perforce 靜態分析的免費試用版,探索如何加速開發安全、可靠且符合標準的程式碼庫。

About Perforce
The best run DevOps teams in the world choose Perforce. Perforce products are purpose-built to develop, build and maintain high-stakes applications. Companies can finally manage complexity, achieve speed without compromise, improve security and compliance, and run their DevOps toolchains with full integrity. With a global footprint spanning more than 80 countries and including over 75% of the Fortune 100, Perforce is trusted by the world’s leading brands to deliver solutions to even the toughest challenges. Accelerate technology delivery, with no shortcuts.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

Detecting and Investigating Lateral Movement: A Network Traffic Analysis Framework

Detecting and Investigating Lateral Movement: A Network Traffic Analysis Framework

A Technical Guide to Identifying Malicious Pivots, Abused Administrative Protocols, and Incident Triaging Workflows via Passive Network Detection and Response (NDR)

Strategic Threat Briefing: Lateral movement represents one of the most critical execution phases of an internal breach. Unlike external exploitation vectors, an adversary navigating your internal network rarely introduces custom malicious tooling; instead, they hijack legitimate, built-in administrative services to blend into normal baseline traffic. This framework details how to leverage passive, agentless network-level telemetry—such as GREYCORTEX Mendel—to expose unauthorized pivots, differentiate malicious commands from routine IT administration, and map structural attack chains in real time.
 

The Illusion of Administrative Normalcy

The core challenge in isolating lateral movement lies in the nature of the protocols involved. Services like SMB, RDP, and PSExec form the operational foundation of daily Windows enterprise infrastructure. Because these channels are ubiquitous, threat actors deliberately weaponize them to map internal subnets, locate high-value active directories, and exfiltrate staging assets without triggering traditional perimeter defense alarms.

To expose these hidden threat layers, security analysts must shift focus from single file-scanning controls to comprehensive network metadata analysis, checking what occurred before a connection was established and tracking where a host pivoted immediately afterward.

 

Analyzing the Four Primary Protocol Vectors of Lateral Movement

Adversaries favor native operating system tools because they guarantee execution while bypassing traditional software blocklists. Security teams must monitor four common protocol architectures for signs of operational abuse:

1. SMB and Windows Administrative Shares (ADMIN$)

Server Message Block (SMB) handles standard file distribution and printer mapping across Windows networks. However, its built-in administrative shares—specifically ADMIN$, which exposes the remote host’s system root directory—present a major exploitation risk. Gaining access to this share allows an attacker to drop binaries, stage execution scripts, and move tools laterally across the environment.

Network Traffic Detection Indicators

Passive NDR engines monitor the application layer of an SMB session to track three critical variables: the active SMB protocol version, the explicit share paths being called, and associated file write/read metrics. While a routine administrator connection rarely triggers unexpected application binaries, an adversarial pivot frequently pairs share access with immediate tool compilation. For instance, detecting an active ADMIN$ session immediately followed by a file operation involving unapproved execution layers (such as a local python.exe deployment) serves as a high-fidelity indicator of compromise.

Investigation Checklist

  • Initiator Verification: Correlate the source IP address against authorized administrative jump hosts and active change management logs.
  • Post-Access Triggers: Audit the connection payload to check whether the share access was immediately followed by binary file drops or unauthorized script staging.

2. PSExec Service Spawning

PSExec is a lightweight, command-line remote administration utility from the Microsoft Sysinternals suite. It allows IT teams to execute commands on remote endpoints without initializing a full interactive desktop session. Attackers leverage this exact capability to achieve remote shell execution across target subnets.

Network Traffic Detection Indicators

PSExec leaves a distinct signature in network traffic due to its underlying mechanics. Every execution begins by establishing a connection over SMB port 445 to the target’s IPC$ share, followed immediately by installing and starting a temporary Windows service named PSEXESVC. Because this traffic is transmitted in clear text over the wire, an NDR platform can read the exact command string passed to the remote host, providing direct evidence of adversarial intent.

Investigation Checklist

  • Operator Authentication: Flag any instances of PSEXESVC initialization executing outside standard operational maintenance hours or on endpoints with no historical record of remote administration.
  • Command String Extraction: Inspect the parsed application metadata to analyze the exact command string executed by the service, prioritizing any obfuscated strings or unmapped binary calls.

3. Remote Desktop Protocol (RDP) Sessions

Remote Desktop Protocol (RDP) provides full graphical interface access to remote target machines. If an adversary harvests valid corporate credentials via phishing or local credential dumping, they can initialize an authenticated RDP session to interact directly with internal file networks, bypassing endpoint malware detection layers.

Network Traffic Detection Indicators

Because RDP session traffic is encrypted natively, security analysts cannot directly inspect in-session keystrokes or file actions from network flows alone. Investigation must therefore pivot to analyzing connection metadata, tracking variables like source-destination IP pairs, session durations, and geographic origin indicators.

Session duration metadata provides deeper insights than most analysts realize. While a brief internal RDP session might appear benign, it must be evaluated alongside the prior activity baseline of the initiating host. If that host demonstrated anomalous system queries or unmapped database access immediately before opening the RDP session, the connection is likely part of a lateral chain. Analysts can leverage peer graphing to trace every internal endpoint the host interacted with immediately after the session ended to define the complete blast radius.

Investigation Checklist

  • Pre-Session Host Baseline: Analyze the historical activity of the source device to determine if unusual communication trends or scanning behavior preceded the session.
  • Downstream Peer Graphing: Leverage network peer graphing to map out and audit every subsequent internal connection initialized by the target host after the RDP session closed.

4. LLMNR Poisoning (Link-Local Multicast Name Resolution)

Link-Local Multicast Name Resolution (LLMNR) serves as a fallback name resolution protocol when standard DNS queries fail. When a Windows endpoint cannot locate a target hostname via DNS, it broadcasts a multicast packet across the local network segment asking if any peer knows the address, allowing any device on the subnet to respond.

Network Traffic Detection Indicators

An attacker can exploit this behavior by running tools like Responder to listen for these multicast queries on UDP port 5355. The attacking device sends a spoofed unicast response claiming to be the target host, forcing the victim machine to attempt authentication and transmit its NTLM credential hash over the wire. A legitimate LLMNR exchange occurs exclusively between a client and a valid asset holder; detecting a unicast response originating from an unexpected IP address with no prior communication history indicates an active poisoning attempt.

Investigation Checklist

  • Responder Validation: Compare the IP address of the unicast responder against the authoritative hostname registry, and flag any nodes attempting to answer queries for unmapped domains.

 

Unifying Parallel Detection Methodologies

Isolating sophisticated lateral movement requires running multiple, complementary analytics engines simultaneously to eliminate individual visibility blind spots:

Detection VectorCore Analytical FocusLateral Movement Insight Contribution
Network Behavior Analysis (NBA)Establishes a dynamic baseline of traffic volumes, connection durations, and peer pairings.Flags structural anomalies, such as a workstation suddenly initiating unmapped connections to high-value database segments.
Intrusion Detection System (IDS)Applies deterministic signature matching against known threat actor methodologies.Instantly identifies specific exploit strings and known post-exploitation framework patterns, regardless of baseline trends.
Log Correlation & ProcessingAggregates application and event logs from endpoints, directories, and internal services.Enriches network flow metrics with explicit system details, including Windows Event IDs and active process creations.

When these detection layers operate in tandem within a unified NDR console, disjointed alerts turn into a clear attack timeline. For example, if an IDS signature flags an anomalous ADMIN$ connection while the behavior analysis engine simultaneously logs an unusual surge in internal peer links from that same device, analysts are no longer looking at random noise—they are tracking an active compromise chain.

 

From Real-Time Triage to Retrospective Forensics

Lateral movement is a progressive sequence that unfolds across multiple protocols, devices, and subnets over time. Because threat actors use standard administrative tools to blend in, catching them requires deep, continuous network visibility to map out both pre-alert behaviors and downstream activities.

This visibility remains valuable long after an active incident is contained. Maintaining a long-term network metadata repository allows security teams to run retrospective analysis months after an event. This historical record ensures your enterprise can confidently execute deep threat hunting exercises, satisfy regulatory compliance audits, and verify the absolute closure of a breach.

About GREYCORTEX
GREYCORTEX uses advanced artificial intelligence, machine learning, and data mining methods to help organizations make their IT operations secure and reliable.

MENDEL, GREYCORTEX’s network traffic analysis solution, helps corporations, governments, and the critical infrastructure sector protect their futures by detecting cyber threats to sensitive data, networks, trade secrets, and reputations, which other network security products miss.

MENDEL is based on 10 years of extensive academic research and is designed using the same technology which was successful in four US-based NIST Challenges.

About Version 2 Digital

Version 2 Digital is one of the most dynamic IT companies in Asia. The company distributes a wide range of IT products across various areas including cyber security, cloud, data protection, end points, infrastructures, system monitoring, storage, networking, business productivity and communication products.

Through an extensive network of channels, point of sales, resellers, and partnership companies, Version 2 offers quality products and services which are highly acclaimed in the market. Its customers cover a wide spectrum which include Global 1000 enterprises, regional listed companies, different vertical industries, public utilities, Government, a vast number of successful SMEs, and consumers in various Asian cities.

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