Pedro Ramos Brandao

Ensuring the integrity, provenance, and auditability of the data on which security systems depend is a central challenge in financial cybersecurity, and one to which blockchain and distributed-ledger technologies are increasingly applied. AI-based threat detectors are only as trustworthy as the logs and training data they consume, yet those data are rarely tamper-evident or verifiable. Spear-phishing, a leading intrusion vector into financial institutions, is used throughout as the motivating application case. This paper examines how data quality and blockchain-enabled auditability jointly condition AI-driven threat detection in the financial sector, through a PRISMA 2020 systematic review of research published between 2020 and 2025. Searches across Scopus, Web of Science, IEEE Xplore, and the ACM Digital Library retrieved 250 records, of which 202 remained after deduplication and 59 met the eligibility and quality-appraisal criteria after a full bibliographic verification of publication dates. The review consolidates four literatures that have developed in parallel, namely blockchain-enabled auditability, data quality, AI-based detection, and financial cybersecurity, and derives a thematic taxonomy of how each treats log integrity, provenance, and data governance. It diagnoses a bounded structural gap: blockchain studies establish immutability and provenance but do not integrate them into operational detection pipelines, whereas AI-detection studies treat data auditability as an implicit assumption. Building on this, the article specifies an auditable Logging–Blockchain–AI (LBA) pipeline through five design requirements (R1–R5) covering ledger interoperability with heterogeneous logging infrastructure, on-ledger governance aligned with financial regulation, data-quality and drift monitoring, scalability, and explainability with human oversight. A bounded scope statement is made explicit: within the reviewed corpus, only six studies treat spear-phishing as their primary research problem, so the review characterises the data-quality and auditability foundations on which such detection depends rather than the detection literature itself. The contribution is analytical and agenda-setting.

The increasing dependence of modern societies on interconnected digital infrastructures has significantly amplified the importance of cybersecurity. At the same time, cyber threats have evolved from isolated technical incidents to complex strategic operations carried out by adaptive and persistent adversaries. Traditional security mechanisms, often based on static detection or predefined protection rules, are no longer sufficient to address these sophisticated threats. This chapter explores the integration of game theory and artificial intelligence as a strategic framework for strengthening cybersecurity defense architectures. By modeling cyber conflict as an interaction between rational adversaries, game-theoretic approaches provide analytical tools for understanding how attackers and defenders make strategic decisions under uncertainty. Attention is given to the modeling of Advanced Persistent Threats, which represent long-term, stealthy intrusion campaigns characterized by continuous adaptation and strategic planning. The chapter examines how concepts such as strategic equilibrium, leader–follower interactions, and dynamic games can be applied to represent attacker–defender dynamics in digital environments. In addition, it discusses how artificial intelligence techniques, including machine learning and anomaly detection, can enhance situational awareness and support strategic decision-making in cybersecurity systems. The integration of data-driven analytics with strategic modeling enables the development of adaptive defense architectures capable of anticipating adversarial behavior and responding to evolving threats. By combining insights from game theory, artificial intelligence, and cybersecurity research, this chapter highlights the potential of interdisciplinary approaches for designing resilient digital defense systems capable of protecting complex technological infrastructures.

Digital forensic investigation is increasingly constrained by the scale, heterogeneity, volatility, and legal sensitivity of contemporary digital evidence. Cloud infrastructures, mobile devices, endpoint telemetry, encrypted applications, containerized services, and adversarially manipulated logs have transformed digital forensics from a largely artifact-centered discipline into a problem of high-dimensional temporal reasoning under uncertainty. Artificial intelligence offers substantial opportunities to accelerate triage, prioritize evidence, reconstruct event sequences, and discover latent relationships among digital artifacts. However, opaque AI systems may undermine forensic soundness, reproducibility, interpretability, and evidential admissibility if they are not designed around forensic requirements. This article investigates the relationship between Digital Forensic Analysis and Artificial Intelligence and proposes ForenGRAF-AI, a complex yet feasible artifact for forensic investigation. ForenGRAF-AI is an explainable graph-temporal AI application that ingests heterogeneous forensic artifacts, constructs a signed temporal provenance graph, learns suspicious behavioral embeddings through graph neural networks and temporal variational modeling, estimates evidential relevance via Bayesian reasoning, and produces human-verifiable investigative hypotheses. The artifact is developed using the Design Science Research methodology, which is suitable when the research contribution is the construction and evaluation of an artifact intended to solve an identified organizational and technical problem [49]-[51]. The principal contribution is a mathematically grounded, explainable, and forensically constrained AI architecture for digital forensic triage and event reconstruction. Unlike conventional anomaly-detection systems, ForenGRAF-AI explicitly models evidential provenance, uncertainty, temporal causality, artifact reliability, and chain-of-custody requirements. The article presents the theoretical foundation, system architecture, mathematical formulation, data strategy, evaluation design, limitations, and implications for forensic practice. The proposed artifact is positioned not as an autonomous forensic decision-maker but as a decision-support system that assists qualified investigators while preserving transparency, repeatability, and evidential accountability.

Detecting anomalous events in high-dimensional behavioral data is a fundamental challenge in modern cybersecurity, particularly in scenarios involving stealthy advanced persistent threats (APTs). Traditional anomaly detection techniques rely on heuristic notions of distance or density yet rarely offer a mathematically coherent description of how sparse events can be formally empirically separated from the dominant behavioral structure. This study introduces a density–metric geometric space framework that unifies geometric, topological, and density-based perspectives into a single analytical model. Behavioral events are embedded in a five-dimensional Euclidean geometric space equipped with a neighborhood-based density operator. Anomalies are formally defined as points whose local density falls below a fixed threshold, and we show that such points occupy empirically distinct low-density regions of the induced metric space. The theoretical foundations are supported by experiments conducted on openly available cybersecurity datasets, including ADFA-LD and UNSW-NB15, where we demonstrate that low-density behavioral patterns correspond to structurally rare attack configurations. The proposed framework provides a mathematically grounded framework with empirical validation for why APT-like behaviors naturally emerge as sparse and weakly coherent regions in high-dimensional space. These results offer a principled basis for high-dimensional anomaly detection and open new directions for leveraging geometric learning in cybersecurity.

This essay highlights the importance of leveraging Artificial Intelligence (AI) tools to combat Advanced Persistent Threats (APTs). Central to this study is the theoretical framework describing the design and evaluation of algorithms that analyze extensive datasets of security-related consultation items. Algorithms for aggregating such datasets are also developed, along with a detailed methodology to ensure adequate statistical significance. To test the research hypothesis, the designed algorithms were used to analyze a large dataset containing detailed information on APT campaigns. APT campaigns involve a series of distinct attacks aimed at gaining control over computational assets. These campaigns are significant cybersecurity threats because they are difficult to detect and are part of a lucrative black market worth hundreds of billions of dollars. Refined tools for launching and managing APT campaigns are supplied to criminal organizations and state actors through Growth Hacking, Inbound Marketing, and other B2B services. Networks of controlled computational assets infiltrated by sell-side ad networks are extensively exploited by these tools. Improved algorithms have been developed to coordinate content consumption and carry out hacking and phishing attacks. Data are stored in a unique, bottom-up event model that describes the lifecycle of APT campaigns and accounts for the limited knowledge of their participants. In the spirit of open science, scripts for collection, deduplication, labeling, and feature engineering, along with dataset splits, data dictionaries, and other materials, have been shared alongside the results of this study. The developed algorithms were used to train and evaluate predictive models for the lifecycle stages of APT campaigns and the decision to engage with them. By providing new domain knowledge and insights, this study encourages researchers to co-create domain-specific machine-learning algorithms with practitioners focusing on countermeasures and emphasizes the need for a new multidisciplinary field merging “Growth Hacking, Inbound Marketing, and Early-Stage Venture Cyber Security VC Strategies” 1.

Advanced Persistent Threats (APTs) are among the most dangerous and sophisticated types of cyberattacks, capable of infiltrating enterprise networks and staying hidden for long periods while stealing data or causing damage. Unlike random cyber threats, APTs use stealth, persistence, and adaptive tactics to evade traditional defenses. This article examines the unusual activities caused by APTs within network environments, focusing on how Artificial Intelligence (AI) can be used to detect these anomalies effectively. We start by explaining the basics of APTs, their lifecycle, and features that make detection dif-ficult. Then, we review a wide range of anomaly detection methods, especially those driv-en by AI. A detailed mathematical framework for identifying anomalies is presented, in-cluding hypothesis testing, change-point detection, probabilistic models, and graph-based learning techniques. The development section describes AI-based detection methods, with mathematical details and pseudo-code. Results from controlled experiments and bench-marks like UNSW-NB15, CIC-IDS2017, and CTU-13 are analyzed, followed by a discus-sion of strengths, limitations, and future research directions. The article wraps up with strategies for deployment and a plan for real-world implementation.

The exponential growth in global data generation has elevated the role of data centers in modern society. However, their immense energy requirements raise significant environmental concerns. This paper aims to demonstrate that current innovations in data center cooling systems, server placement architectures, and virtualization techniques are not only technologically advanced but also critical drivers of energy sustainability. Through an in-depth review of current research, development of key technological pathways, and detailed discussion supported by 40 scholarly references, we establish that sustainable data centers are not a futuristic ideal but a present necessity. The analysis is grounded in rigorous scientific methodologies, including thermodynamic modeling, computational fluid dynamics (CFD), and workload orchestration frameworks. By integrating energy-aware designs with cutting-edge software deployment models, data centers are being transformed from energy-intensive infrastructures into hubs of sustainable computational power. This transformation is supported not only by theoretical principles but also by a growing body of empirical data that demonstrates marked improvements in energy usage efficiency (PUE), carbon footprint (CUE), and overall sustainability metrics.

The exponential growth in global data generation has elevated the role of data centers in modern society. However, their immense energy requirements raise significant environ-mental concerns. This paper aims to demonstrate that current innovations in data center cooling systems, server placement architectures, and virtualization techniques are not on-ly technologically advanced but also critical drivers of energy sustainability. Through an in-depth review of current research, development of key technological pathways, and de-tailed discussion supported by 40 scholarly references, we establish that sustaina-ble data centers are not a futuristic ideal but a present necessity. The analysis is grounded in rigorous scientific methodologies, including thermodynamic modeling, computational fluid dynamics (CFD), and workload orchestration frameworks. By integrating energy-aware designs with cutting-edge software deployment models, data centers are being transformed from energy-intensive infrastructures into hubs of sustainable computational power. This transformation is supported not only by theoretical principles but also by a growing body of empirical data that demonstrates marked improvements in energy usage efficiency (PUE), carbon footprint (CUE), and overall sustainability metrics.

In recent years, artificial intelligence (AI) has emerged as a transformative force across various sectors of modern society, reshaping economic landscapes, social interactions, and ethical considerations. This paper explores the multifaceted impact of AI, analyzing its implications for employment, privacy, and decision-making processes. By synthesizing recent research and case studies, we investigate the dual nature of AI as both a catalyst for innovation and a source of potential disruption. The findings highlight the necessity for proactive governance and ethical frameworks to mitigate risks associated with AI deployment while maximizing its benefits. Ultimately, this paper aims to provide a comprehensive understanding of how AI is redefining human experiences and societal norms, encouraging further discourse on the sustainable integration of these technologies in everyday life.