#shodan — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #shodan, aggregated by home.social.
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🔹 🛠️ Tool: ThreatSentry AI
ThreatSentry AI is presented as an enterprise-focused threat-hunting platform that automates external asset discovery, enriches findings from multiple sources, and applies ensemble machine learning to prioritize risk. The project lists PyQt5 for UI, scikit-learn for ML, and SQLAlchemy for persistence, and names EclipseManic as project lead.
🔹 Core pipeline and integrations
The platform performs continuous external visibility via Shodan queries (preset and custom), extracts service banners across common products (examples in the project include Apache, Nginx, MySQL, IIS), and correlates banner data with NVD CVE information. CVSS-based severity classification is applied where CVE matches are found; the README notes that CVE metrics are updated only when vulnerabilities are identified to avoid data loss.
🔹 Machine learning and scoring
The risk engine is described as an ensemble combining Random Forest, Gradient Boosting, and Neural Network components. Models evaluate 40+ attributes spanning temporal context (exposure duration, patch lag), network position (service criticality, segmentation), behavioral signals (authentication failures, traffic anomalies), and compliance impact (data sensitivity, regulatory exposure). Each risk prediction includes a confidence score in the 0–1 range. The system is described as having configurable automatic retraining with analyst feedback integration for continuous learning.
🔹 Platform capabilities and outputs
ThreatSentry AI emphasizes proactive alerting and executive-ready dashboards that surface high-risk assets ahead of incidents. Preset Shodan queries are provided for common service classes (SSL, RDP, ICS/Modbus), with support for organization-specific custom queries. The architecture is described as extensible for integrating internal systems (SIEM, CMDB, patch sources) although specifics are implementation-dependent.
🔹 Project context
The README highlights single-developer authorship with assistance from AI development tools for code generation and documentation. The repo frames the project as addressing alert fatigue, fragmented data, and reactive security postures by converting multi-source telemetry into prioritized, confidence-scored intelligence.
🔹 Hashtags
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🔹 🛠️ Tool: ThreatSentry AI
ThreatSentry AI is presented as an enterprise-focused threat-hunting platform that automates external asset discovery, enriches findings from multiple sources, and applies ensemble machine learning to prioritize risk. The project lists PyQt5 for UI, scikit-learn for ML, and SQLAlchemy for persistence, and names EclipseManic as project lead.
🔹 Core pipeline and integrations
The platform performs continuous external visibility via Shodan queries (preset and custom), extracts service banners across common products (examples in the project include Apache, Nginx, MySQL, IIS), and correlates banner data with NVD CVE information. CVSS-based severity classification is applied where CVE matches are found; the README notes that CVE metrics are updated only when vulnerabilities are identified to avoid data loss.
🔹 Machine learning and scoring
The risk engine is described as an ensemble combining Random Forest, Gradient Boosting, and Neural Network components. Models evaluate 40+ attributes spanning temporal context (exposure duration, patch lag), network position (service criticality, segmentation), behavioral signals (authentication failures, traffic anomalies), and compliance impact (data sensitivity, regulatory exposure). Each risk prediction includes a confidence score in the 0–1 range. The system is described as having configurable automatic retraining with analyst feedback integration for continuous learning.
🔹 Platform capabilities and outputs
ThreatSentry AI emphasizes proactive alerting and executive-ready dashboards that surface high-risk assets ahead of incidents. Preset Shodan queries are provided for common service classes (SSL, RDP, ICS/Modbus), with support for organization-specific custom queries. The architecture is described as extensible for integrating internal systems (SIEM, CMDB, patch sources) although specifics are implementation-dependent.
🔹 Project context
The README highlights single-developer authorship with assistance from AI development tools for code generation and documentation. The repo frames the project as addressing alert fatigue, fragmented data, and reactive security postures by converting multi-source telemetry into prioritized, confidence-scored intelligence.
🔹 Hashtags
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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#MongoBleed and #Shodan is a dangerous combination. #security
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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Internet of insecure things: daily #random #shodan #screenshot [#iot #shodansafari]-->
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@marlin
Hier ein paar Infos über die zusätzlichen Angriffsvektoren bei #IPv6. Bei richtiger Konfiguration ist IPv6 natürlich nicht unsicherer als #IPv4.#netsec #ITSecurity #networking #netzwerke #Rechnernetze #Shodan #IP #Internet
@bsi -
Finding thousands of exposed Ollama instances using Shodan
https://blogs.cisco.com/security/detecting-exposed-llm-servers-shodan-case-study-on-ollama
#HackerNews #Finding #Exposed #Instances #Shodan #Ollama #Cybersecurity #SecurityResearch
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Giving a Face to the Malware Proxy Service ‘Faceless’ https://krebsonsecurity.com/2023/04/giving-a-face-to-the-malware-proxy-service-faceless/ #DenisViktorovichPankov #ConstellaIntelligence #Ne'er-Do-WellNews #lesstroy@mgn.ru #AccessApproved #LibertyReserve #WebFraud2.0 #RileyKilmer #Flashpoint #Omega^gg4u #VadimPanov #Faceless #U1018928 #asus666 #Gaihnik #MrMurza #spur.us #Shodan
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"Mehr als 15 Millionen verwundbare Systeme mit #Schwachstellen aus dem Known-Exploited-Vulnerabilities-Catalog (#KEV) der US-Cyber-#Sicherheitsbehörde #CISA haben IT-Sicherheitsforscher von Rezilion mit der Datenbank #Shodan aufgespürt."
#KRITIS #Security #Exploits #Windows #AdobeFlashPlayer #InternetExplorer #MicrosoftOffice #GoogleChrome #AppleiOS #CiscoIOS #IOSXE
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#ComedyTouchTouch1000 #CLARKE #Pree #Dorian #MAXMX43 #TheMan #TAALR #Giant #A.L.I.E #Vigil #Stella #Overmind #V #A.D.I.S.N. #TheQuail #Gideon #Orak #Brainiac #Batcomputer #CerebroCerebra #Computo #Ultron #MotherBox #Fate #Banana,Jr.6000 #Max #Auntie #A.I.D.A. #Kilg%re #Project2501 #Yggdrasil #DTXPC #Beast666 #HOMER #TheMagi #Toy #Virgo #Praetorius #Erwin #AIMA #Answertron2000 #iFruit #EnnesbyLunesbyPeteyTAGAthens #Melchizedek #Merlin #Normad #Aura #TreeDiagram #Europa #Benson #PRISM #MotherBrain #GW #MotherBrain #BaseCochiseAI #DIA51 #Noah #DurandalLeelaTycho #TraxusIV #LINC #0D-10 #Prometheus #SEED #AM #CABAL #EVA #KAOS #MotherBrain #XenocidicInitiative #PC #Pokedex #Centralconsciousness #GOLAN #PipBoy2000/PipBoy3000 #ZAX #ACE #Sol—9000SystemDeus #FATE #NEXUSIntruderProgram #SHODAN #XERXES #IcarusDaedalusHeliosMorpheusTheOracle #Mainframe #343GuiltySpark #Calculator #Cortana #DeadlyBrain #PETs #Thiefnetcomputer #Adam #AuraMorganna #Dr.Carroll #TheController #ADA #IBIS #2401PenitentTangent #Angel #Durga/Melissa/Yasmine #TheMechanoids #TEC-XX #Dvorak #TemperNet #Animus #AuroraUnit #TheCatalysttheIntelligence