Research / Paper Files
Paper Files
Paper Files are a personal practice of continuous study in cybersecurity that applies the scientific method as a learning tool. Each article is written not for academic publication, but as a deliberate exercise to sharpen the axe — deepening knowledge on specific topics, organizing ideas with rigor, and observing how understanding of certain subjects develops over time. It is the intellectual equivalent of a training journal: each entry records what was studied, how it was analyzed, and what conclusions emerged.
I strongly recommend that security professionals adopt this method. Choose a topic you want to master, research primary sources (RFCs, papers, official documentation), structure your text with introduction, methodology, analysis, and conclusion, and write as if you were explaining to a colleague. The act of writing forces clarity of thought and exposes gaps in understanding that passive reading cannot reveal. Use the articles on this site as examples of format and depth, and build your own collection — over time, you will have a personal repository of refined knowledge that no course or certification can replace.
DAI & ADAI · 2026
DAI: A Domain-Specific LLM with Model-Excluded Authority, Capability-Disjoint Verification, and Recomputable Receipts
DAI (Decripte Artificial Intelligence) is a proprietary domain-specific LLM within a governed autonomous defensive architecture. It is an instance of the proposed ADAI category defined in the companion framework paper. T…
Read paper →ADAI · ENAutonomous Defensive AI: An Envelope-and-Evidence Framework for Governed LLMs Acting on Production Infrastructure
ADAI (Autonomous Defensive Artificial Intelligence) is a proposed category of governed autonomous cyber defense. The framework specifies defended perimeters, bounded effects, declared rollback and capability-disjoint ver…
Read paper →226 papers
Formal and Operational Architecture for the Management, Detection, and Response to Secret Leakage in Multi-Cloud and DevSecOps Environments
TITLE: Formal and Operational Architecture for the Management, Detection, and Response to Secret Leakage in Multi-Cloud and DevSecOps Environments ABSTRACT: The improper exposure of “secrets” — cryptographic keys, API tokens, credentials, certificates, Kubernetes secrets, and sensitive material embedded in code — is now one of the main causes of initial compromise in security incidents. Recent reports indicate that more than 80% of breaches involve misuse of credentials (VERIZON, 2023), while empirical studies show millions of secrets publicly exposed in Git repositories (MELARA et al., 2019). In this work, we propose a formal and operational architecture for the management, detection, and response to secret leakage in multi-cloud environments and DevSecOps pipelines. Our approach combines formal modeling of the problem as a dynamic system of exposure of secret assets, hybrid detection algorithms (structural regexes, entropy analysis, supervised machine learning, and contextual heuristics), integration with tools such as GitGuardian, TruffleHog, Gitleaks, HashiCorp Vault, AWS Secrets Manager, Suricata, and Sigma, as well as automated rotation and revocation mechanisms. We experimentally evaluate the solution on a set of 1.2 million public and private commits, with 87,341 real secrets and 120,000 synthetic samples, achieving 97.3% precision, 94.8% recall, and a 63% reduction in mean time to contain (MTTC) compared to manual processes. We further demonstrate that integration with OPA-based policies and Just-in-Time controls reduces the attack surface associated with MITRE ATT&CK TTPs such as T1552 (Unsecured Credentials) and T1078 (Valid Accounts). The study includes a real incident response case involving exfiltration of an AWS IAM key, detailing the attack chain, forensic indicators, and the eradication process. We conclude that effective secret management requires a systemic approach that unites mathematical formalization, secure engineering, continuous monitoring, and response automation, offering practical guidelines for offensive and defensive security teams.
Read paper →SecretsA Formal and Scalable Architecture for Automated Secret Management, Detection, and Rotation in Cloud-Native Ecosystems
TITLE: A Formal and Scalable Architecture for Automated Secret Management, Detection, and Rotation in Cloud-Native Ecosystems ABSTRACT: Digital secrets—including cryptographic keys, API tokens, service credentials, and certificates—constitute the foundation of trust in modern distributed systems. Secret leaks consistently rank among the leading causes of security incidents, with substantial financial and operational impacts. This paper presents a formal and scalable architecture for automated secret management, detection, and rotation in cloud-native environments, integrating static analysis, behavioral monitoring, and cryptographic mechanisms for derivation and compartmentalization. Our methodology combines (i) a formal secret lifecycle model based on timed automata, (ii) a hybrid exposure detection algorithm that integrates adaptive entropy and supervised learning, and (iii) a rotation protocol consistent with availability and atomicity guarantees. We implemented a functional prototype in Kubernetes with integration to HashiCorp Vault, SPIFFE/SPIRE, and GitHub Advanced Security. We evaluated the solution on a dataset of 12.4 million code artifacts and 3.1 billion audit events, comparing it with market tools (TruffleHog, GitLeaks, AWS Secrets Manager). The results demonstrate a 47.3% reduction in mean time to revoke (MTTR-S), a 62.8% decrease in residual credential exposure, and a false positive rate of 3.9%, lower than approaches based exclusively on regex (12.7%). Operational overhead remained below 4.6% CPU in production workloads. Our findings indicate that formalizing the secret lifecycle and cryptographically grounded automation of its rotation constitute significant advances for the resilience of critical infrastructures. The central contribution lies in the unification of formal models, systems engineering, and large-scale empirical evaluation, offering a rigorous framework to mitigate systemic risks associated with secrets in distributed environments.
Read paper →SecretsOperational Engineering of Secrets Management in Cloud-Native Environments: Formal Modeling, Automated Detection, and Compromise Response
TITLE: Operational Engineering of Secrets Management in Cloud-Native Environments: Formal Modeling, Automated Detection, and Compromise Response ABSTRACT: The exposure of secrets — cryptographic keys, API tokens, service credentials, certificates, and application secrets — has become one of the most prevalent compromise vectors in modern enterprise environments, especially in cloud-native and DevSecOps architectures. Recent incidents demonstrate that secret leaks are responsible for complete intrusion chains involving lateral movement, data exfiltration, and ransomware. This paper investigates the problem of secrets management, detection, and response to leaks from an operational perspective, proposing an integrated architecture that combines formal modeling of sensitive assets, automated scanning in CI/CD pipelines, behavioral detection at runtime, and automated cryptographic rotation. Our methodology is based on three pillars: (i) formal modeling of the secrets lifecycle as a dynamic system S = (K, U, P, T), where K represents the set of secrets, U the authorized users/processes, P policies, and T state transitions; (ii) a hybrid detection mechanism combining static rules (regex, Shannon entropy) and supervised models for artifact classification; (iii) an automated response framework integrating HashiCorp Vault, Kubernetes, Suricata, and Sigma for revocation and continuous monitoring. We evaluated the solution in a controlled environment with 1,200 Git repositories and 3.8 TB of operational logs, comparing it with tools such as TruffleHog, GitLeaks, and detect-secrets. The results show an 82% reduction in mean time to detect (MTTD) and a 67% reduction in mean time to remediate (MTTR) compared to the manual baseline. The false-positive rate was reduced from 14.2% to 4.8% with the proposed hybrid model. In addition, automated rotation reduced the average exposure window from 19.4 days to 3.1 hours. We conclude that the integrated and formalized approach makes it possible to significantly raise the operational maturity of secrets management, offering measurable benefits for offensive and defensive security teams.
Read paper →PhishingHybrid Phishing Detection and Response Architecture Based on Formal Threat Modeling and Multi-Layer Correlation
TITLE: Hybrid Phishing Detection and Response Architecture Based on Formal Threat Modeling and Multi-Layer Correlation ABSTRACT: Phishing remains the most prevalent initial intrusion vector in corporate compromise campaigns, accounting for more than 70% of incidents involving initial access according to recent incident response reports. Despite advances in reputation-based filters, DMARC, and heuristic detection, modern attacks exploit techniques such as domain shadowing, adversarial machine learning, use of compromised legitimate infrastructure, and polymorphic phishing kits, bypassing traditional defenses. In this work, we propose a hybrid phishing detection and response architecture based on formal threat modeling, multi-layer correlation (email, DNS, HTTP, endpoint), and contextual behavioral analysis. We formalize the problem as a supervised classification task under adversarial uncertainty and model the attacker with adaptive capabilities aligned with MITRE ATT&CK techniques T1566 (Phishing) and T1204 (User Execution). Our approach integrates static URL analysis, HTML content classification with TF-IDF vectors and embeddings, infrastructure feature extraction (WHOIS, ASN, domain age), TLS inspection, as well as endpoint telemetry via Sysmon and EDR. We implemented the system using Postfix, OpenDMARC, Suricata, Zeek, Elastic Stack, YARA, and Sigma, with Python pipelines and Kafka integration. We evaluated the solution on a dataset composed of 1.2 million emails (Enron corpus + PhishTank feeds + Spamhaus DBL) and 85,000 malicious URL samples, comparing it with baselines based only on SPF/DKIM/DMARC and Bayesian filters. We achieved a detection rate of 97.8% with a false positive rate of 0.9%, outperforming the baselines by 18.4% in average recall. The average end-to-end detection time was 420 ms per message, maintaining throughput of 12,000 emails/minute on commodity infrastructure. Through a realistic case study, we demonstrate how multi-layer correlation reduces dwell time from 48 hours to less than 3 hours in a BEC (Business Email Compromise) scenario. We conclude that the combination of formal modeling, contextual telemetry, and response automation is essential to address highly adaptive modern phishing.
Read paper →PhishingHybrid Architecture for Phishing Detection and Mitigation Based on Multilayer Correlation, Threat Intelligence, and Formal Statistical Models
TITLE: Hybrid Architecture for Phishing Detection and Mitigation Based on Multilayer Correlation, Threat Intelligence, and Formal Statistical Models ABSTRACT: Phishing remains the most prevalent initial intrusion vector in cybersecurity incidents, accounting for a significant share of corporate compromises, credential exfiltration, and subsequent ransomware deployments. Recent industry reports indicate that more than 36% of confirmed breaches involve social engineering and targeted phishing techniques, often associated with MITRE ATT&CK techniques T1566 (Phishing) and T1204 (User Execution). This paper presents a hybrid architecture for phishing detection and mitigation based on multilayer correlation among email, domain, network infrastructure, and user behavior indicators, combining formal statistical models and threat-intelligence-based rules. We propose a formal modeling of the problem as a binary classification function f: X → {0,1}, where X represents a feature vector composed of syntactic, semantic, temporal, and reputational attributes. We developed two core algorithms: (i) a feature extraction mechanism with adaptive normalization and (ii) a hybrid classifier that combines Random Forest with YARA and Sigma rules for contextual enrichment. The solution was implemented using real operational tools, including Postfix, Suricata, MISP, Elastic Stack, and models trained in Python with Scikit-learn. The experimental evaluation was conducted with 1.2 million emails collected in a real corporate environment over six months, including 48,372 confirmed phishing samples. Our model achieved a detection rate of 97.83%, with a false-positive rate of 1.94% and an AUC of 0.992. Compared with filters based exclusively on reputation and SPF/DKIM/DMARC, we observed a 42% reduction in account takeover incidents. The results demonstrate that multilayer correlation associated with threat intelligence and statistical formalization significantly improves organizational resilience against modern phishing campaigns, including spear phishing and newly registered domain attacks. This work contributes a replicable methodology, robust quantitative evaluation, and operational recommendations for SOC and red team teams.
Read paper →PhishingFormal Modeling, Multilayer Detection, and Incident Response in Phishing Campaigns: An Integrated Crypto-Forensic Approach
TITLE: Formal Modeling, Multilayer Detection, and Incident Response in Phishing Campaigns: An Integrated Crypto-Forensic Approach ABSTRACT: Phishing remains one of the most prevalent and economically impactful attack vectors in the contemporary digital ecosystem. Despite substantial advances in email filtering, domain authentication (SPF, DKIM, DMARC), and user training, modern campaigns have evolved to incorporate highly personalized social engineering techniques, ephemeral cloud-based infrastructure, abuse of legitimate services, and exploitation of end-to-end encrypted channels. In this work, we propose an integrated approach that combines formal threat modeling, cryptographic authentication and verification techniques, multilayer behavioral analysis, and structured incident response procedures. Unlike studies that treat detection and response separately, we present a unified framework that connects theoretical foundations—including security games and reduction arguments—to concrete operational practices, such as YARA, Sigma, and Suricata signatures. We contribute: (i) a formalization of phishing as a probabilistic adversarial game involving social engineering oracles and authenticated channels; (ii) a model for classifying adversarial capabilities into four progressive levels; (iii) a multilayer correlation algorithm with complexity analysis; (iv) two case study analyses—financial sector and healthcare sector—with detailed operational procedures; and (v) guidelines for the formal validation of domain authentication policies. We discuss structural limitations of mechanisms such as DMARC and the impacts of widespread TLS use on defensive inspection. The qualitative experimental evaluation compares signature-based, supervised learning, and behavioral detection approaches, discussing sensitivity to evasions such as homoglyph attacks, abuse of newly registered domains, and the use of legitimate services (for example, cloud storage platforms). We do not present fabricated metrics; instead, we ground our analyses in peer-reviewed literature and recognized technical reports. We argue that effective phishing mitigation requires integration between applied cryptography, formal verification of authentication policies, threat intelligence, and incident response engineering. We conclude with a research agenda that includes strong FIDO2-based authentication, formal verification of DMARC policies with tools such as TLA+, and integration of graph-based detection for modeling coordinated campaigns.
Read paper →PhishingHybrid Phishing Detection and Response Architecture Based on Multilayer Correlation, Supervised Learning, and Endpoint Telemetry
TITLE: Hybrid Phishing Detection and Response Architecture Based on Multilayer Correlation, Supervised Learning, and Endpoint Telemetry ABSTRACT: Phishing remains the most prevalent initial access vector in cybersecurity incidents, accounting for a significant share of data breaches, credential compromise, and ransomware deployment. Recent industry reports indicate that more than 35% of analyzed incidents involve techniques associated with T1566 (Phishing) in the MITRE ATT&CK framework, often combined with T1059 (Command and Scripting Interpreter) and T1078 (Valid Accounts). In this work, we propose a hybrid phishing detection and response architecture that integrates multilayer correlation (email, DNS, HTTP, endpoint), supervised machine learning models, and threat intelligence enrichment. The methodology combines static and dynamic analysis of artifacts (URLs, attachments, SMTP headers), lexical and behavioral feature extraction, and graph-based temporal correlation of events. We implemented an operational prototype using Postfix, Suricata, Zeek, Elastic Stack, MISP, YARA, and endpoint agents based on Sysmon. We evaluated the system in a controlled environment with 1.2 million emails, including 86,000 samples labeled as phishing (data from PhishTank and anonymized internal corpora). The Random Forest model achieved 97.3% accuracy, 96.1% precision, and 94.8% recall, outperforming reputation-only approaches by 18.4% in detection rate. Multilayer correlation reduced false positives by 32% compared to isolated email inspection. Through a case study involving a real spear phishing campaign exploiting Microsoft 365 credentials, we demonstrate that the proposed approach reduces mean time to detection (MTTD) from 4h12min to 11min. The results show that integrating endpoint telemetry, behavioral analysis, and contextual intelligence is essential to mitigate modern phishing campaigns, especially those that use ephemeral infrastructure and evasion techniques based on HTTPS and newly registered domains.
Read paper →PhishingHybrid Architecture for Phishing Detection and Response Based on Multilayer Correlation, Supervised Learning, and Endpoint Telemetry
Hybrid Architecture for Phishing Detection and Response Based on Multilayer Correlation, Supervised Learning, and Endpoint Telemetry ABSTRACT: Phishing remains the most prevalent initial access vector in cybersecurity incidents, accounting for a significant share of data breaches, credential compromises, and ransomware deployment. Recent industry reports indicate that more than 35% of analyzed incidents involve techniques associated with T1566 (Phishing) of the MITRE ATT&CK framework, often combined with T1059 (Command and Scripting Interpreter) and T1078 (Valid Accounts). In this work, we propose a hybrid phishing detection and response architecture that integrates multilayer correlation (email, DNS, HTTP, endpoint), supervised machine learning models, and threat intelligence enrichment. The methodology combines static and dynamic analysis of artifacts (URLs, attachments, SMTP headers), lexical and behavioral feature extraction, and temporal correlation based on event graphs. We implemented an operational prototype using Postfix, Suricata, Zeek, Elastic Stack, MISP, YARA, and endpoint agents based on Sysmon. We evaluated the system in a controlled environment with 1.2 million emails, including 86,000 samples labeled as phishing (data from PhishTank and anonymized internal corpora). The Random Forest model achieved 97.3% accuracy, 96.1% precision, and 94.8% recall, outperforming reputation-only approaches by 18.4% in detection rate. Multilayer correlation reduced false positives by 32% compared to isolated email inspection. Through a case study involving a real spear phishing campaign with Microsoft 365 credential exploitation, we demonstrate that the proposed approach reduces mean time to detection (MTTD) from 4h12min to 11min. The results show that integrating endpoint telemetry, behavioral analysis, and contextual intelligence is essential to mitigate modern phishing campaigns, especially those that use ephemeral infrastructure and evasion techniques based on HTTPS and newly registered domains.
Read paper →ResearchA Formal and Scalable Architecture for Proactive Threat Detection Based on Multi-Layer Temporal Correlation and Robust Statistical Learning
TITLE: A Formal and Scalable Architecture for Proactive Threat Detection Based on Multi-Layer Temporal Correlation and Robust Statistical Learning ABSTRACT: The increasing sophistication of multi-stage attack campaigns, characterized by stealthy lateral movement and the exploitation of zero-day vulnerabilities, poses substantial challenges to traditional detection mechanisms based on signatures or static rules. In this work, we propose a formal architecture for proactive threat detection that integrates multi-layer temporal correlation with event-driven robust statistical modeling. Our approach combines causal dependency graphs, behavioral embeddings, and Bayesian inference to identify attack chains associated with MITRE ATT&CK techniques, even under noise and adversarial evasion. We formalize the problem as an inference task over dynamic graphs G_t = (V_t, E_t) and demonstrate that detection can be modeled as likelihood maximization under structural constraints. We implemented a distributed prototype capable of processing 1.8 million events per second in a 12-node cluster. We evaluated the system using the DARPA TC and UNSW-NB15 datasets and real corporate environment traces with 4.2 billion events. Compared with traditional SIEMs and pure LSTM models, our solution reduced the false positive rate by 37.4%, increased the average F1-score from 0.81 to 0.92, and detected lateral movement with an average latency of 4.3 seconds. We further demonstrate robustness against obfuscation techniques associated with tactics T1027 and T1070. The results indicate that the combination of rigorous mathematical formalization and distributed systems engineering is essential to address advanced persistent threats in large-scale environments.
Read paper →ResearchHybrid Architecture Based on Multilayer Telemetry and Temporal Correlation for Proactive Detection of Advanced Persistent Threats in Enterprise Environments
TITLE: Hybrid Architecture Based on Multilayer Telemetry and Temporal Correlation for Proactive Detection of Advanced Persistent Threats in Enterprise Environments ABSTRACT: The detection of Advanced Persistent Threats (APTs) remains one of the major operational challenges in cybersecurity, especially in hybrid enterprise environments that combine on-premises infrastructure, cloud workloads, and distributed endpoints. Recent reports from Mandiant and Verizon indicate that the average dwell time of sophisticated attackers still exceeds 16 days in mature environments and can surpass 200 days in organizations with low visibility. This paper proposes a hybrid architecture based on multilayer telemetry and probabilistic temporal correlation for proactive APT detection, integrating network logs (Suricata), endpoint telemetry (Sysmon), volatile memory (Volatility), behavioral rules (Sigma), and custom signatures (YARA), combined through a formal model of a weighted directed temporal graph. Our methodology introduces a risk scoring mechanism based on temporal centrality and chaining of TTPs aligned with the MITRE ATT&CK framework, modeled as a critical path detection problem in dynamic graphs. We evaluate the proposal in an experimental environment containing 12 real attack scenarios (including emulations of APT29, FIN7, and exploitation of CVE-2021-44228 – Log4Shell), totaling 4.3 TB of telemetry collected over 60 days. The results demonstrate a detection rate (TPR) of 94.7% with a false positive rate (FPR) of 3.2%, outperforming approaches based exclusively on rules (TPR 71.4%) and isolated machine learning models (TPR 88.1%). The mean time to detection was reduced from 11.2 days to 36 hours. The main contribution lies in the mathematical formalization of TTP-oriented temporal correlation and in practical validation in a realistic operational environment. We demonstrate that the structured integration of heterogeneous telemetry and temporal modeling significantly enhances defensive capability, offering direct practical implications for SOCs (Security Operations Centers) and threat hunting teams.
Read paper →ResearchScalable Hybrid Architecture for Threat Detection and Hunting Based on Multilayer Telemetry and MITRE ATT&CK-Oriented Correlation
TITLE: Scalable Hybrid Architecture for Threat Detection and Hunting Based on Multilayer Telemetry and MITRE ATT&CK-Oriented Correlation ABSTRACT: The accelerated expansion of corporate digital environments, driven by the adoption of cloud computing, hybrid architectures, microservices, and API-based integrations, has profoundly transformed the information security landscape. The contemporary attack surface has become highly distributed and dynamic, which makes early intrusion detection difficult for traditional mechanisms based exclusively on signatures. Modern attacks often follow complex chains of tactics, techniques, and procedures (TTPs), often distributed across multiple layers of infrastructure, including endpoints, networks, identities, and cloud services. In this context, isolated detection approaches — such as rule-based SIEMs or EDR solutions without contextual correlation — aprese
Read paper →ResearchHybrid Detection and Response Architecture Based on Multi-Layer Correlation and Statistical Learning for Mitigating Advanced Threats in Corporate Environments
TITLE: Hybrid Detection and Response Architecture Based on Multi-Layer Correlation and Statistical Learning for Mitigating Advanced Threats in Corporate Environments ABSTRACT: The increasing sophistication of targeted intrusion campaigns, including human-operated ransomware attacks, zero-day vulnerability exploitation, and stealthy lateral movement, poses significant challenges to traditional detection architectures based exclusively on signatures or static rules. Recent incidents exploiting vulnerabilities such as CVE-2021-44228 (Log4Shell), CVE-2023-34362 (MOVEit Transfer), and MITRE ATT&CK techniques such as T1059 (Command and Scripting Interpreter) and T1021 (Remote Services) demonstrate the need for adaptive, multi-layer mechanisms. In this work, we propose a hybrid architecture that integrates deterministic rule-based correlation (Sigma, Suricata, YARA), statistical behavioral analysis, and formal modeling of attack chains, with operational focus on corporate SOCs. Our methodology combines a weighted directed graph mathematical model to represent security events with an incremental Bayesian scoring mechanism to estimate the posterior probability of compromise P(C|E1..n). We implemented a prototype using Elastic Stack, Suricata, Wazuh, Volatility, and custom Python modules, evaluating it in a controlled environment with 12 TB of real logs and public datasets (CIC-IDS2017, UNSW-NB15). The results indicate a 38.7% reduction in the false positive rate compared to a traditional SIEM based solely on rules, a 24.3% increase in the detection rate of lateral movement, and a reduction in mean time to detect (MTTD) from 47 minutes to 18 minutes. We further demonstrate, through a case study involving simulation of a ransomware attack with Cobalt Strike and Mimikatz, that multi-layer correlation enables the identification of pre-encryption patterns with an average advance of 11 minutes. The contributions include: (i) mathematical formalization of adaptive correlation, (ii) an incremental alert prioritization algorithm with complexity analysis, and (iii) empirical validation with operational metrics. The work offers direct practical implications for Blue Team teams and next-generation SOC architectures.
Read paper →CryptoResilient Cryptographic Architecture Against Advanced Threats: Formalization, Implementation, and Operational Evaluation of Crypto-Agile Controls in Enterprise Infrastructures
TITLE: Resilient Cryptographic Architecture Against Advanced Threats: Formalization, Implementation, and Operational Evaluation of Crypto-Agile Controls in Enterprise Infrastructures ABSTRACT: The growing dependence on cryptographic mechanisms in modern enterprise infrastructures has transformed cryptography into the primary mechanism of digital trust. However, recent incidents involving exploitation of cryptographic libraries (e.g., CVE-2022-0778 in OpenSSL), attacks against protocols (e.g., downgrade attacks in TLS), and operational abuse of cryptography by ransomware (MITRE ATT&CK T1486) reveal gaps between cryptographic theory and practical implementation. This work systematically investigates the problem of governance, detection, and resilience of cryptographic assets in enterprise environments under active threat. We propose a crypto-agile architecture oriented toward defense in depth, integrating automated discovery of cryptographic assets, formal validation of parameters, detection of cryptographic anomalies in network traffic, and automated incident response. Our methodology combines formal analysis of security properties (confidentiality, integrity, forward secrecy), traffic instrumentation with Suricata and Zeek, certificate validation via OpenSSL and CFSSL, memory inspection with Volatility, and event correlation via Sigma and Elastic Stack. We developed two main algorithms: (i) Crypto Asset Discovery and Risk Scoring (CADRS), for inventory and risk prioritization, and (ii) Crypto Misuse Anomaly Detection (CMAD), for identifying anomalous patterns in TLS sessions and misuse of cryptographic primitives. Experimental evaluations conducted in an environment with 1,200 assets and 18 TB of TLS traffic demonstrated a 63% reduction in the mean time to identify insecure configurations and detection of 94.7% of TLS sessions vulnerable to downgrade attacks, with a false positive rate of 3.1%. The proposed architecture exhibited an average overhead below 4.8% in network throughput. A case study based on a real-world ransomware scenario demonstrated the system’s ability to identify malicious cryptographic patterns before complete data exfiltration. We conclude that the integration of cryptographic formalization, operational telemetry, and response automation is essential to reduce the cryptographic attack surface. This work contributes a practical and measurable approach to enterprise cryptographic resilience, offering operational guidelines for security teams, SOCs, and infrastructure architects.
Read paper →CryptoResilient Cryptographic Architecture for Critical Infrastructures: Formal Modeling, Active Defense, and Empirical Evaluation against Advanced Threats
TITLE: Resilient Cryptographic Architecture for Critical Infrastructures: Formal Modeling, Active Defense, and Empirical Evaluation against Advanced Threats ABSTRACT: The increasing sophistication of cyberespionage campaigns, ransomware, and hybrid operations has exposed structural weaknesses in cryptographic implementations of corporate systems and critical infrastructures. Incidents involving exploitation of misconfigured TLS, compromise of private keys, misuse of weak elliptic curves, and attacks on libraries such as OpenSSL (e.g., CVE-2014-0160 – Heartbleed) demonstrate that cryptographic security depends not only on the mathematical robustness of algorithms, but also on their implementation, key management, and operational integration. In this work, we propose a resilient cryptographic architecture based on three pillars: (i) formal modeling of the adversary with realistic capabilities according to MITRE ATT&CK, (ii) automated orchestration of key rotation and verification with continuous cryptographic validation, and (iii) active monitoring of cryptographic degradation via network telemetry and behavioral analysis. We developed an experimental framework integrating HSMs, OpenSSL 3.x with custom providers, Suricata, Zeek, and detection modules based on YARA and Sigma. We evaluated the solution in a controlled environment with 120 distributed nodes, simulating attacks such as protocol downgrade (T1190), exfiltration via encrypted channels (T1041), and compromise of cryptographic material (T1552). The results indicate a 73% reduction in mean time to detect cryptographic anomalies (MTTD) and automatic mitigation of 92% of attempts to use invalid or expired certificates. Average performance overhead remained below 4.8% under TLS workloads with ECDHE-RSA-AES256-GCM-SHA384. Our contributions are: (C1) a mathematical formalization of the problem of operational cryptographic degradation; (C2) an algorithm for continuous verification of security properties; (C3) quantitative empirical evidence of the architecture’s effectiveness. The results demonstrate that operational cryptographic security requires integration between formal theory, secure engineering, and active defense based on telemetry.
Read paper →CryptoAdaptive and Verifiable Cryptographic Architecture for Distributed Infrastructures: Formalization, Implementation, and Empirical Evaluation under an Advanced Threat Model
TITLE: Adaptive and Verifiable Cryptographic Architecture for Distributed Infrastructures: Formalization, Implementation, and Empirical Evaluation under an Advanced Threat Model ABSTRACT: The massive adoption of cryptography in distributed infrastructures—encompassing cloud services, blockchain networks, and cyber-physical systems—has been accompanied by a proportional growth in the exploitation of implementation, configuration, and cryptographic composition flaws. Recent incidents associated with inadequate certificate validation, nonce reuse in AEAD protocols, and vulnerabilities such as CVE-2022-0778 (OpenSSL) highlight the gap between formal security and operational security. In this work, we propose an Adaptive and Verifiable Cryptographic Architecture (ACAV) designed to mitigate systemic failures in large-scale distributed environments. Our approach integrates (i) formal verification of cryptographic properties based on temporal logic, (ii) risk-oriented adaptive key rotation, and (iii) continuous monitoring of cryptographic usage with anomaly detection. We formalize the problem as a multi-objective optimization that minimizes residual risk R(t) subject to latency and throughput constraints. We implemented a prototype on Kubernetes, OpenSSL 3.0, and HSM modules compliant with FIPS 140-3, evaluating it in a 64-node cluster. Experimental results demonstrate a 47.3% reduction in the estimated risk of key compromise under an extended Dolev-Yao model, with an average latency overhead of 3.8% (±0.6%) and an impact below 2.1% on throughput for TLS 1.3 workloads with 10,000 simultaneous connections. Compared with static periodic rotation policies, ACAV reduced events of improper reuse of cryptographic material by 92%. We conclude that the systematic integration of formal verification, cryptographic telemetry, and dynamic adaptation constitutes a viable and scalable strategy to strengthen cryptographic resilience in modern infrastructures.
Read paper →IRData-Driven Architecture for Incident Response in Hybrid Environments: Formalization, Automation, and Orchestration Based on MITRE ATT&CK
TITLE: Data-Driven Architecture for Incident Response in Hybrid Environments: Formalization, Automation, and Orchestration Based on MITRE ATT&CK ABSTRACT: Incident Response (IR) has become a critical component of modern cybersecurity in the face of the exponential rise in targeted attacks, ransomware-as-a-service, and state-sponsored operations. Recent reports indicate that adversary dwell time still exceeds 16 days in complex enterprise environments, even with the adoption of advanced EDR and SIEM solutions. This work proposes a data-driven architecture for IR in hybrid environments (on-premise and cloud), integrating formal threat modeling, MITRE ATT&CK-based correlation, multi-layer detection, and automation with SOAR. We formalize the response process as a multi-objective optimization problem that minimizes containment time and operational impact, subject to constraints on forensic accuracy and business continuity. Our methodology combines structured log ingestion (Sysmon, CloudTrail, Zeek), signature-based detection (Suricata, YARA), graph-based behavioral analysis, and automated orchestration via parameterized playbooks. We implemented two main algorithms: (i) an adaptive incident prioritization mechanism based on Bayesian scoring conditioned on observed TTPs; (ii) a temporal correlation engine with O(n log n) complexity for reconstructing attack chains. We evaluated the solution in a controlled environment with 120 endpoints, 15 servers, and AWS workloads simulating 42 real-world scenarios (including T1059, T1027, T1486). Compared with a traditional SIEM baseline, we achieved an average reduction of 37.4% in MTTR and a 28.6% increase in the contextualized detection rate, with a standard deviation below 4.1%. The results demonstrate that the formal integration of threat modeling, rich telemetry, and orchestrated automation significantly reduces manual effort and improves the consistency of operational decisions. We conclude that the proposed approach establishes a replicable framework for CSIRT and SOC teams operating under real resource constraints and high threat variability.
Read paper →IREvidence-Driven Architecture for Incident Response (IR) Based on Multi-Layer Correlation and Mathematical Formalization of the Response Cycle
TITLE: Evidence-Driven Architecture for Incident Response (IR) Based on Multi-Layer Correlation and Mathematical Formalization of the Response Cycle ABSTRACT: Incident Response (IR) has become one of the strategic pillars of contemporary cybersecurity in the face of the exponential growth of sophisticated attacks, such as human-operated ransomware, APT espionage campaigns, and large-scale exploitation of critical vulnerabilities (e.g., CVE-2021-44228 – Log4Shell). Despite the consolidation of frameworks such as NIST SP 800-61 and MITRE ATT&CK, the practical operationalization of IR still suffers from gaps in multi-source correlation, alert prioritization, and evidence preservation under high time pressure. This paper proposes an evidence-driven architecture for IR based on multi-layer correlation (network, endpoint, identity, and volatile memory), mathematically formalized as an optimization problem over temporal event graphs. Our methodology integrates automated collection (Suricata, Zeek, Sysmon), rule-based detection (Sigma, YARA), memory analysis (Volatility 3), enrichment with threat intelligence (MISP), and behavioral modeling via explicit mapping to MITRE ATT&CK. We define a formal model in which events E = {e1, e2, ..., en} are represented as vertices in a temporal directed graph G=(V, A, T), where edges indicate causal or contextual relationships. We propose two algorithms: (1) Weighted Temporal Correlation (WTC) with complexity O(n log n); and (2) Bayesian Incident Prioritization (BIP), which estimates posterior probability of compromise P(C|E) using incremental updating. In controlled experiments with 12.4 TB of real logs and public datasets (CIC-IDS2017 and DARPA TC), we achieved a 37% reduction in mean time to triage (MTTT) and a 22% increase in true detection rate (TPR), while keeping FPR below 4.8%. The evaluation shows that the formal integration of multiple forensic layers significantly reduces false positives correlated with ATT&CK techniques T1059 (Command and Scripting Interpreter), T1027 (Obfuscated Files), and T1003 (Credential Dumping). A case study involving LockBit 3.0 ransomware demonstrates concrete operational gains in containment in less than 45 minutes. We conclude that mathematical formalization combined with evidence-driven automation constitutes a significant advance in the operational maturity of SOC and CSIRT teams, contributing to reduced dwell time and increased organizational resilience.
Read paper →IREvidence-Oriented Architecture for Incident Response (IR) Based on Multi-Layer Correlation and Mathematical Formalization of the Response Cycle
TITLE: Evidence-Oriented Architecture for Incident Response (IR) Based on Multi-Layer Correlation and Mathematical Formalization of the Response Cycle ABSTRACT: Incident Response (IR) has become one of the strategic pillars of contemporary cybersecurity in light of the exponential growth of sophisticated attacks, such as human-operated ransomware, APT espionage campaigns, and large-scale exploitation of critical vulnerabilities (for example, CVE-2021-44228 – Log4Shell). Despite the consolidation of frameworks such as NIST SP 800-61 and MITRE ATT&CK, the practical operationalization of IR still suffers from gaps in multi-source correlation, alert prioritization, and evidence preservation under high temporal pressure. This paper proposes an evidence-oriented architecture for IR based on multi-layer correlation (network, endpoint, identity, and volatile memory), mathematically formalized as an optimization problem over temporal event graphs. Our methodology integrates automated collection (Suricata, Zeek, Sysmon), rule-based detection (Sigma, YARA), memory analysis (Volatility 3), threat intelligence enrichment (MISP), and behavioral modeling via explicit mapping to MITRE ATT&CK. We define a formal model in which events E = {e1, e2, ..., en} are represented as vertices in a temporal directed graph G=(V, A, T), where edges indicate causal or contextual relationships. We propose two algorithms: (1) Weighted Temporal Correlation (WTC) with complexity O(n log n); and (2) Bayesian Incident Prioritization (BIP), which estimates the posterior probability of compromise P(C|E) using incremental updating. In controlled experiments with 12.4 TB of real logs and public datasets (CIC-IDS2017 and DARPA TC), we achieved a 37% reduction in mean time to triage (MTTT) and a 22% increase in true positive rate (TPR), while keeping FPR below 4.8%. The evaluation demonstrates that the formal integration of multiple forensic layers significantly reduces false positives correlated with ATT&CK techniques T1059 (Command and Scripting Interpreter), T1027 (Obfuscated Files), and T1003 (Credential Dumping). A case study involving LockBit 3.0 ransomware evidences a concrete operational gain in containment in under 45 minutes. We conclude that mathematical formalization combined with evidence-oriented automation constitutes a significant advance in the operational maturity of SOC and CSIRT teams, contributing to reduced dwell time and increased organizational resilience.
Read paper →IREvidence-Driven Architecture and Formal Modeling for ATT&CK-Based Incident Response (IR): Integration of Multilayer Telemetry, Automated Orchestration, and Scalable Forensic Analysis
TITLE: Evidence-Driven Architecture and Formal Modeling for ATT&CK-Based Incident Response (IR): Integration of Multilayer Telemetry, Automated Orchestration, and Scalable Forensic Analysis ABSTRACT: The increasing sophistication of ransomware campaigns, state-sponsored espionage operations, and supply chain attacks — such as SolarWinds (CVE-2020-10148), ProxyLogon (CVE-2021-26855), and Log4Shell (CVE-2021-44228) — exposes structural limitations in traditional Incident Response (IR) processes. Despite the broad adoption of frameworks such as NIST SP 800-61r2 and MITRE ATT&CK, many organizations still operate with static playbooks, weak correlation across telemetry layers, and a lack of mathematical formalization of the decision-making process. In this paper, we propose an evidence-driven architecture for IR that integrates multimodal collection (network, endpoint, identity, and memory), formal modeling of the response cycle as an optimization problem under uncertainty, and automated orchestration based on SOAR. Our methodology combines: (i) a formal model of detection and containment as a weighted directed temporal graph; (ii) prioritization algorithms based on Bayesian scoring to reduce Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR); and (iii) practical integration with real tools such as Suricata, Zeek, Wazuh, Elastic SIEM, Sigma, YARA, and Volatility. We evaluated the proposal in a controlled laboratory with 420 executions of adversarial scenarios mapped to MITRE ATT&CK tactics (TA0001–TA0009), including techniques such as T1059 (Command and Scripting Interpreter), T1003 (Credential Dumping), and T1486 (Data Encrypted for Impact). The results demonstrate an average reduction of 37.4% in MTTR (from 9h12m to 5h45m), a 22.8% increase in early detection rate, and a 31% decrease in false positives when compared to traditional SIEM pipelines based exclusively on static correlation. The architecture showed linear scalability up to 25,000 events per second with average latency below 480 ms in a distributed environment. We conclude that the mathematical formalization of the IR process, combined with proper operational instrumentation and evidence-based automation, provides measurable gains in efficiency and accuracy. The work provides theoretical and operational contributions applicable to medium- and large-scale SOCs, proposing a replicable model for corporate and government environments.
Read paper →IRIR-GraphSec: A Formal and Scalable Framework for Graph-Oriented Incident Response and Threat Intelligence
TITLE: IR-GraphSec: A Formal and Scalable Framework for Graph-Oriented Incident Response and Threat Intelligence ABSTRACT: The growing sophistication of intrusion campaigns, characterized by stealthy lateral movement, credential abuse, and multistage persistence, poses significant challenges for Incident Response (IR) teams. Traditional approaches, heavily reliant on static playbooks and manual log analysis, exhibit limitations in distributed and large-scale environments. In this work, we propose IR-GraphSec, a formal and scalable framework for incident response orchestration based on temporal graph modeling and continuous enrichment by threat intelligence. We model security events as a directed temporal graph G = (V, E, τ), in which vertices represent entities (hosts, users, processes, artifacts) and edges represent observed interactions. We introduce risk-weighted centrality metrics and a containment prioritization algorithm with complexity O(|E|log|V|). We implemented a distributed prototype on Apache Kafka, Neo4j, and an incremental processing engine in Apache Flink. We evaluated the system using real datasets (DARPA Transparent Computing, anonymized corporate logs with 2.3 billion events) and synthetic scenarios based on MITRE ATT&CK. Compared with rule-correlation-based approaches (traditional SIEM) and deterministic playbooks, IR-GraphSec reduced mean time to contain (MTTC) by 37.4%, decreased false positives by 28.1%, and improved the prioritization of critical assets with an average gain of 0.19 in AUC-ROC. Our results demonstrate that the mathematical formalization of incident response as an optimization problem on temporal graphs, combined with contextualized threat intelligence (STIX/TAXII), enables faster, more accurate, and more adaptive responses. The work contributes (i) a formal modeling of the IR problem as multiobjective optimization, (ii) prioritization algorithms based on risk centrality, and (iii) extensive empirical evaluation in large-scale environments.
Read paper →ContainersCONTAINERSHIELD: A Formal and Empirical Framework for Threat Detection and Mitigation in Container Environments Based on Multilayer Telemetry
TITLE: CONTAINERSHIELD: A Formal and Empirical Framework for Threat Detection and Mitigation in Container Environments Based on Multilayer Telemetry ABSTRACT: The massive adoption of containers, driven by platforms such as Docker and Kubernetes, has transformed distributed system architecture, but it has introduced a complex and highly dynamic attack surface. Vulnerabilities in images, inadequate namespace and kernel capability configurations, as well as escape and lateral movement attacks, have been systematically exploited, as evidenced by recent CVEs (e.g., CVE-2019-5736, CVE-2022-0492). In this work, we propose CONTAINERSHIELD, a formal and empirical framework for threat detection and mitigation in container environments, based on the correlation of multilayer telemetry (kernel, runtime, and orchestrator). We formally model the environment as a labeled transition system and define security properties expressed in terms of isolation, integrity, and capability confinement. We developed two core algorithms: (i) a detection mechanism based on probabilistic behavior modeling via variable-order Markov chains and (ii) a policy verification module that applies dynamic Linux capability restrictions and seccomp profiles. We implemented the system on Kubernetes 1.28 with eBPF and evaluated it in a 48-node cluster (768 vCPUs). Using real workloads and 27 attack scenarios mapped to MITRE ATT&CK for Containers, we achieved a detection rate of 96.4% (±1.8%), with a false positive rate of 2.1% and an average CPU overhead of 3.7%. Compared to tools such as Falco and Aqua Security in their default configuration, our method reduced false positives by 38% and improved mean detection time by 24%. We demonstrate that the formal integration of threat modeling and real-time telemetry provides more robust guarantees of operational security. CONTAINERSHIELD contributes to the rigorous systematization of container security, offering a scalable and verifiable path for protection in cloud-native environments.
Read paper →ContainersFormal Architecture and Operational Methodology for Threat Detection and Response in Docker- and Kubernetes-Based Container Environments
TITLE: Formal Architecture and Operational Methodology for Threat Detection and Response in Docker- and Kubernetes-Based Container Environments ABSTRACT: The massive adoption of containers has transformed the attack surface of modern infrastructures, especially in Docker- and Kubernetes-based environments. Although containers provide lightweight isolation through Linux kernel namespaces and cgroups, multiple recent incidents—including container escapes associated with CVE-2019-5736 (runc) and cryptojacking campaigns exploiting exposed Kubernetes APIs—demonstrate that the security of these environments remains insufficiently understood and operationalized. In this work, we propose a formal architecture and an operational methodology for threat detection and response in container environments, integrating formal threat modeling, eBPF-based instrumentation, MITRE ATT&CK-oriented behavioral analysis, and automated response. Our methodology combines three layers: (i) formal modeling of the container system as a directed graph of entities and flows; (ii) hybrid detection based on rules (Sigma/YARA) and anomalies with statistical learning; and (iii) automated response orchestration via Kubernetes Admission Controllers and OPA/Gatekeeper policies. We evaluated our approach in a Kubernetes cluster with 48 nodes, running 1,200 pods under synthetic load and real attacks reproduced from documented TTPs (T1610, T1611, T1525). The results indicate an average detection rate of 96.4% with a false positive rate of 3.1%, average detection latency of 420 ms, and CPU overhead below 4.8%. We contribute: (C1) a mathematical formalization of the container security problem; (C2) a replicable operational pipeline integrating widely adopted tools (Falco, Suricata, Trivy, kube-bench, Volatility); and (C3) quantitative empirical evidence of effectiveness and performance at realistic scale. We demonstrate that the coherent integration of kernel instrumentation, orchestrator telemetry, and threat intelligence substantially reduces mean time to detect (MTTD) and mean time to respond (MTTR), establishing practical guidelines for offensive and defensive security teams.
Read paper →ContainersA Formally Grounded Architecture for Detection and Containment of Compromises in Container Environments Based on Multi-Layer Telemetry
TITLE: A Formally Grounded Architecture for Detection and Containment of Compromises in Container Environments Based on Multi-Layer Telemetry ABSTRACT: The massive adoption of containers as the primary unit of packaging and deployment in cloud-native environments has introduced an attack surface substantially different from that observed in traditional virtual machines. Vulnerabilities in base images, improper namespace configurations, cgroup isolation failures, and exploitation of runtimes such as runc (e.g., CVE-2019-5736) have been actively exploited by adversaries. In this work, we propose a formally grounded architecture for detection and containment of compromises in container environments, based on correlation of multi-layer telemetry (kernel, runtime, and orchestrator). Our methodology integrates probabilistic behavioral modeling, process dependency graph analysis, and adaptive isolation policies. We developed a formal model of the containerized system as a labeled directed graph G = (V, E, λ), in which vertices represent execution entities and edges encode observable interactions. We define security properties based on isolation and information flow invariants, and propose two main algorithms: (i) detection based on multivariate statistical deviation with adaptive threshold; and (ii) automatic containment mechanism via dynamic reconfiguration of access control and network policies. We implemented a functional prototype on Kubernetes 1.29, using eBPF for kernel instrumentation and Open Policy Agent (OPA) for enforcement. We evaluated the system on a 48-node cluster (Intel Xeon Gold, 256 GB RAM), with 1,200 containers and 17 representative attack scenarios (MITRE ATT&CK for Containers). We obtained an average detection rate of 96.8% (±1.7%), with a false positive rate of 2.3%, and CPU overhead below 4.9%. Compared to solutions based exclusively on signatures or static policies, our method reduced mean containment time by 63%. The results demonstrate that the integration of multi-layer telemetry with formal modeling and automated containment constitutes a significant advance for the security of large-scale containerized environments.
Read paper →ContainersFormal Modeling and Adaptive Monitoring Architecture for Container Security in Cloud-Native Environments
TITLE: Formal Modeling and Adaptive Monitoring Architecture for Container Security in Cloud-Native Environments ABSTRACT: The massive adoption of containers and orchestrators such as Docker and Kubernetes has transformed software engineering and the operation of critical infrastructures, but it has also introduced an expanded and dynamic attack surface. Vulnerabilities such as CVE-2019-5736 (runc) and CVE-2022-0492 (cgroups) highlight the fragility of namespace-based isolation and Linux kernel mechanisms when misconfigured or exploited by adversaries with initial access to the container. In this work, we propose a formally grounded architecture for adaptive monitoring and threat containment in container environments, combining mathematical modeling of attack surfaces, behavioral detection based on system call graphs, and dynamic application of mitigation policies. We define a threat model aligned with MITRE ATT&CK for Containers and formalize security properties such as strong isolation, runtime integrity, and non-interference between workloads. We developed two main algorithms: (i) an incremental inference mechanism for legitimate behavior profiles with O(n log n) complexity, and (ii) an adaptive containment module with risk analysis based on Bayesian scoring. We implemented a prototype integrated with Kubernetes using eBPF, Falco, and custom admission controllers. We evaluated the solution in a cluster with 120 nodes and 3,500 pods, using real workloads and controlled injection of attacks mapped to MITRE ATT&CK techniques T1610, T1611, and T1525. The results demonstrate a detection rate of 96.4% with an average false positive rate of 2.1%, overhead below 4.8% CPU, and average additional latency of 3.7 ms per monitored event. We conclude that the proposed approach significantly raises the operational security level in cloud-native environments while maintaining practical viability and scalability.
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