DEKENEAS transforms previously unknown attacker behavior into structured, actionable cyber threat intelligence. By combining an AI-driven deception network with behavioral analysis, campaign reconstruction, advanced malware and phishing triage, criminal ecosystem monitoring, sector-aware context, and machine-consumable feeds, the platform helps organizations understand emerging threats earlier and operationalize that intelligence with less analyst effort.
Generate original cyber threat intelligence from direct attacker interaction across the internet rather than relying solely on third-party reporting, public disclosures, or retrospective indicators.
Identify emerging exploitation activity from attacker behavior, including pre-CVE and not-yet-labeled campaigns, by recognizing attack primitives and operational patterns rather than depending only on known signatures.
Reduce cognitive load, alert fatigue, and reliance on scarce senior expertise through structured findings, prioritization, contextual analysis, and analyst-style explanations.
DEKENEAS Cyber Threat Intelligence provides continuous visibility into the external threat environment by observing attacker behavior directly across the internet. The platform produces first-party intelligence from an AI-driven deception network, enriches it with malware and phishing analysis, correlates it with vulnerability intelligence and criminal ecosystem monitoring, and delivers the resulting intelligence through APIs, feeds, and an analyst-facing dashboard. Rather than merely aggregating existing threat data, DEKENEAS is designed to convert unfamiliar and emerging attacker activity into structured intelligence that security teams can use immediately.
Capture attacker activity directly through protocol inference, adaptive low-interaction emulation, and distributed first-party external telemetry designed to observe how adversaries actually probe, test, and exploit exposed technologies.
Transform unfamiliar attacker activity into structured intelligence through behavioral classification, campaign synthesis, infrastructure mapping, malware analysis, phishing triage, and contextual analyst-oriented interpretation.
Deliver intelligence through dashboard views, APIs, and machine-consumable feeds so security teams can integrate it directly into operational workflows and defensive controls.
Attackers scan, probe, stage infrastructure, test exploits, harvest credentials, deploy malware, and trade access long before malicious activity becomes visible inside a target organization. Teams that rely mainly on known indicators, public disclosures, or internally observed compromise events are often forced into a reactive position after attacker workflows are already mature and difficult to distinguish from legitimate activity.
DEKENEAS extends visibility beyond the perimeter into the environments where attacks are developed, tested, staged, and monetized. It helps organizations identify what matters earlier, understand attacker behavior in context, and reduce the time and effort required to move from observation to operational response.
Identify reconnaissance, exploit testing, staging activity, and criminal ecosystem signals before internal compromise indicators emerge.
Move from isolated indicators to a broader understanding of attacker workflow, supporting infrastructure, and operational intent.
Lower cognitive load and manual triage effort by delivering structured findings, prioritization, and analyst-style interpretation instead of raw event streams.
Convert unknown behavior into usable intelligence that can be consumed immediately across investigation, detection, and response workflows.
The platform is built around an AI-driven deception network that analyzes incoming packets and attacker behavior to infer the targeted protocol, then dynamically activates the appropriate low-interaction emulation logic. This allows DEKENEAS to engage a broad range of attacker interactions instead of being limited to a narrow set of predefined services. Distributed deception infrastructure associated with real sector and geographic context improves realism, reduces easy fingerprinting, and makes the resulting intelligence materially more relevant to the organizations it serves.
The platform analyzes packet characteristics and interaction patterns to determine what service or protocol an attacker expects to reach, then adapts the deception layer accordingly.
Unused IP space can be redirected into the deception network to support realistic collection, improve sector and regional relevance, and generate telemetry grounded in real external attack conditions.
AI-assisted analysis, behavioral clustering, campaign synthesis, infrastructure mapping, malware classification, phishing scoring, HUMINT-supported monitoring, and feed generation convert raw attacker activity into operational CTI.
DEKENEAS collects telemetry from direct attacker interaction, infers the targeted service, activates matching emulation logic, classifies behavior, correlates related events across sensors, reconstructs campaigns, maps supporting infrastructure, and exposes the resulting intelligence through APIs, feeds, and the dashboard.
Observe reconnaissance, protocol fingerprinting, exploitation attempts, staged file transfer, malware delivery, shell command injection, endpoint abuse, and administrative enumeration directly from attacker interactions.
Differentiate broad internet background noise from more meaningful reconnaissance, active exploitation, malware staging, and follow-on attacker behavior.
Group related events according to infrastructure, payload characteristics, request structure, timing, and campaign logic to reveal coordinated threat activity.
Trace scanning nodes, payload staging servers, malware delivery hosts, and other supporting infrastructure to build a fuller operational view of a campaign.
Route suspicious binaries, URLs, leak references, access-sale indicators, and newly disclosed vulnerabilities into specialized intelligence workflows for deeper contextual analysis.
Deliver intelligence through dashboard views, APIs, and machine-consumable threat feeds so analysts and downstream systems can use it without delay.
DEKENEAS does not simply collect external threat data and send more raw events to analysts. The platform processes activity through classification, contextualization, clustering, scoring, summarization, and analyst-style explanation so teams can work from structured findings rather than fragmented signals. This reduces dependence on scarce senior analyst expertise, lowers the skill barrier for effective use, and accelerates the cybersecurity cycle from external observation to internal prioritization, mitigation, and operational response.
Organize observed traffic into meaningful categories such as reconnaissance, exploitation, malware delivery, exploitation activity, and command-and-control-related behavior.
Generate analyst-style notes and contextual assessments that help teams understand why a finding matters, what it likely represents, and how it should be interpreted.
Help analysts focus attention on high-value findings instead of broad background noise, reducing manual triage overhead and alert fatigue.
Shorten the path from unfamiliar attacker observation to operational understanding and action for threats that are still emerging and not yet broadly documented.
The platform’s detection model is fundamentally behavioral. It looks for attack primitives such as exploitation artifacts, shell metacharacter injection, parameter abuse, protocol misuse, manipulation of configuration templates, misuse of administrative handlers, and command download chains. This enables DEKENEAS to identify emerging exploitation campaigns at an early stage, including cases where there is no assigned CVE, no established signature, and no broad industry labeling yet.
DEKENEAS does not depend on known exploit signatures or public naming. It recognizes attacker behavior that may appear before formal vulnerability disclosure, public reporting, or industry-wide attribution.
This is especially important for routers, embedded devices, edge technologies, management interfaces, and long-tail software environments where exploitation frequently outpaces disclosure and patching.
DEKENEAS combines multiple intelligence modules into one operational system so attacker observations can be investigated, interpreted, contextualized, and transformed into usable intelligence products. The result is a platform designed not only to collect signals, but to produce intelligence that can support operational defense and decision-making.
Correlates deception telemetry into campaigns, reconstructs attacker behavior across the lifecycle, maps supporting infrastructure, and supports IOC export in formats such as STIX 2.1.
Provides IOC research for IPs, domains, hashes, URLs, and related artifacts with enriched infrastructure, timing, DNS, WHOIS, HTTP, TLS, and malware context to support deeper inquiry.
Uses machine learning, controlled rendering, screenshots, visual and behavioral analysis, and contextual analyst notes to evaluate suspicious URLs and credential-harvesting pages.
Uses a ten-model meta-model, multi-signal evidence, MITRE ATT&CK mapping, and malware analyst notes to assess suspicious binaries beyond basic signature matching and reputation-only approaches.
Tracks exposed data, compromised access sales, and criminal ecosystem activity across open, deep, and dark web sources, including closed and restricted threat actor communities.
Provide situational awareness, anomaly detection, sector-specific threat briefings, and relevance-based vulnerability context to support better prioritization and decision-making.
The platform is built around an API-first architecture. Backend intelligence services are exposed through APIs and machine-consumable feeds containing enriched attacker IPs, assessed risk levels, geographic metadata, recommended actions, command-and-control infrastructure, malware hashes, phishing URLs, and related operational context. This allows organizations to operationalize external intelligence directly within SIEM, SOAR, TIP, detection engineering, and broader security automation workflows.
Distribute enriched intelligence such as attacker IPs, risk levels, locations, command-and-control indicators, malware hashes, phishing URLs, and supporting metadata for immediate operational use.
Feed intelligence directly into SIEM systems, threat intelligence platforms, SOAR workflows, detection engineering pipelines, and other defensive controls so intelligence can inform action, not just review.
DEKENEAS gives security teams visibility into the early stages of the attack lifecycle from an external vantage point. By combining first-party telemetry, behavioral analysis, campaign reconstruction, advanced malware and phishing intelligence, criminal ecosystem monitoring, sector-aware context, and operational delivery, the platform helps organizations detect earlier, understand faster, reduce analyst effort, and strengthen defensive controls before emerging threats escalate into internal incidents.
Identify reconnaissance, exploitation, malware staging, phishing preparation, and criminal ecosystem signals before threats reach your environment or become broadly recognized.
Gain structured understanding of attacker workflows, campaign evolution, infrastructure chains, and sector targeting instead of working from disconnected signals and isolated indicators.
Classify suspicious binaries and phishing pages using multi-signal evidence and contextual analysis rather than relying only on signatures, lists, or reputation systems.
Shorten the path from attacker observation to operational defense through structured intelligence, analyst-ready explanations, APIs, and machine-consumable output.
The platform is especially valuable for organizations that are likely to be targeted by internet-scale cyber activity or that operate infrastructure attractive to attackers, including telecommunications providers, financial institutions, technology companies, public-sector organizations, critical infrastructure operators, and national or sectoral CERTs.
| Organization Type | How CTI Helps |
|---|---|
| Telecommunications Providers | Gain sector-specific telemetry and intelligence about scanning, edge infrastructure targeting, phishing, malware staging, and access-sale activity affecting internet-facing networks and services. |
| Financial Institutions | Track phishing, credential threats, exposed access, and vulnerability relevance in a sector that is continuously targeted, highly exposed, and rapidly monetized by threat actors. |
| Technology Companies | Monitor emerging exploitation campaigns, suspicious binaries, attacker infrastructure, and evolving threat activity affecting public-facing platforms, applications, services, and software supply chains. |
| Public Sector & Critical Infrastructure | Understand emerging attacker behavior before it is broadly recognized, formally labeled, or translated into generic market reporting, enabling earlier defensive preparation. |
| CERTs & Security Teams | Investigate IOCs, triage malware and phishing, monitor criminal ecosystems, reduce manual analysis burden, and enrich detection and response pipelines with machine-consumable intelligence. |
Common use cases include early campaign detection, IOC investigation, vulnerability prioritization, malware and phishing triage, criminal ecosystem monitoring, sector-focused threat awareness, and enrichment of operational detection and response pipelines.
DEKENEAS captures attacker behavior directly and transforms it into structured intelligence at the earliest stages of threat development. By combining an AI-driven, sector-aware deception network with behavioral analysis, campaign reconstruction, infrastructure mapping, advanced malware and phishing intelligence, HUMINT-supported monitoring, and operational delivery, the platform helps organizations understand earlier, decide faster, reduce analytical burden, and strengthen defenses before emerging threats escalate into internal incidents.