Homeland and territorial security with AI E-Book

Bruno Ciroussel

• Herausgeber: BoD - Books on Demand
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 217

Beschreibung

This book explores the innovative concept of a knowledge cartridge dedicated to internal and territorial security, employing the Aitek platform - a cutting-edge AI and Big Data framework enriched by 25,000 man-days of research and development. It delves into the foundational principles of the knowledge cartridge system and subsequently addresses the critical challenges inherent in homeland and territorial security, with a particular focus on counter-terrorism efforts. Through a detailed examination, the thesis presents how artificial intelligence can be strategically applied to these challenges, offering a novel approach to enhancing security measures and operational efficiency within the domain of internal and territorial security. This work not only contributes to the academic discourse on AI's role in security but also proposes practical AI-driven solutions to complex security problems, thereby bridging the gap between theoretical research and real-world application.

Leseprobe

Table of contents

LIMINARY

INTRODUCTION

Chapter 0.0: reminder - the aitek platform

Chapter 0.1: reminder: Knowledge Cartridge

PART 1:

Chapter 1.0: homeland security concept

Chapter 1.1: Homeland in Aitek: Approach

Chapter 1.2: Homeland in Aitek : Model

Chapter 1.3: Homeland in Aitek : BigData

Chapter 1.4: Homeland in Aitek: Config

Chapter 1.5: Homeland in Aitek: Admin

Chapter 1.6: Homeland in Aitek: Supervisor

Chapter 1.7: Homeland in Aitek: Performance

Chapter 1.8: Homeland in Aitek: Dashboard

Chapter 1.9: Homeland in Aitek: Supervisor Setup

Chapter 1.10: Homeland Security +

Chapter 1.11: Homeland Security ++

PART 2:

Chapter 2.0: Territorial Security Concept (Aitek)

Chapter 2.1: Territorial Security Approch (Aitek)

Chapter 2.2: Territorial Security & Beacon (Aitek)

Chapter 2.3: Territorial Security & Architecture (Aitek)

PART 3:

Chapter 3.0: Aitek Plateforme Setup

Chapter 3.1: Knowledge Cartridge Setup

LIMINARY

“In the realm of safety, every journey begins with a single step towards vigilance.”

His work aims to describe the knowledge cartridge for homeland and territorial security, which leverages the artificial intelligence and big data engine, Aitek. The book begins by introducing the reader to the key functionalities of the Aitek AI engine, explains what a knowledge cartridge is, and then focuses on the features and implementation of the Homeland security knowledge cartridge. This cartridge autonomously manages internal security issues through the use of artificial intelligence. Furthermore, the Homeland++ extension, equipped with IoT Boxes and drones, facilitates automatic verification and targeted search for individuals or vehicles. Additionally, the territorial knowledge cartridge addresses automatic management of territorial security and the protection of sensitive areas such as borders, mining sites, nuclear power plants, and maritime terminals.

This publication is a continuation of my previous works, notably “Innovation Unleashed,” which serves as the conceptual manual for Aitek 6, and “Security and Human Factor,” based on my course taught at the Institute for the Fight Against Economic Crime (ILCE). The latter forms the foundation of the cybersecurity knowledge cartridge. Through these writings, my goal is to provide both a theoretical and practical framework that highlights technological innovations in security and defense, while emphasizing the critical importance of the human factor in the effective deployment of these advanced technologies.

INTRODUCTION

“A strong start is half the battle.”

In this revised and expanded version, it’s critical to appreciate that we introduce two principal products: the Aitek platform and the Knowledge Cartridge. The Aitek platform is an expansive collection of functionalities and technical frame-works engineered to deploy an AI-enhanced management environment without necessitating additional development. Thanks to its own methodology, it is powered by an AI agent generated by the platform, which is fueled by the meticulous governance of big data. The Knowledge Cartridge, conversely, is a specialized configuration of the platform, precisely tailored for specific industry requirements, enabling organizations to achieve mastery over their operational processes through pro-found industry insights.

The Aitek platform represents a comprehensive solution designed to facilitate the application of artificial intelligence (AI) within various organizational frameworks, entirely circumventing the need for data scientists or software engineers. This platform is not merely an assembly of tools; it is an integrated ecosystem, complete with a proprietary methodology that seamlessly melds with an organization’s pre-existing systems. It empowers the automatic application of AI-derived knowledge across a spectrum of operational domains, from strategic planning to routine tasks, with its architecture primed for adaptability and scalability. This design philosophy ensures the platform’s relevance and utility in the face of evolving organizational demands and the fluid dynamics of data ecosystems.

At its core, the Aitek platform capitalizes on the potential of big data through its vector-based analytics. Amidst the deluge of data characteristic of the digital age, the platform sets itself apart not just by aggregating extensive data sets but by deploying sophisticated analytical tools and machine learning algorithms to sift through this data, yielding practical insights. These technologies are continually refined through exposure to new data, enhancing the precision and applicability of the insights provided.

The Knowledge Cartridge offers a bespoke adaptation of the Aitek platform, devised to address distinct business needs without resorting to traditional IT development. This customization extends far beyond basic adjustments, embedding industry-specific knowledge, methodologies, and benchmarks deep within the platform’s functionalities. As a result, the Knowledge Cartridge does more than deliver a custom AI solution; it serves as a compendium of industry intelligence, equipping organizations with the capabilities to navigate their sectors with unparalleled proficiency and insight.

Collectively, the Aitek platform and the Knowledge Cartridge present a synergistic pairing, combining the transformative power of AI with deep, industry-specific knowledge. This combination not only streamlines and enhances operational efficiency but also fosters innovation and leadership within industries. Through this innovative approach, the Aitek solution redefines the role of AI in business, elevating it from a mere efficiency tool to a pivotal force for growth and competitive differentiation.

Chapter 0.0: reminder - the aitek platform

“AI the silent guardian, tirelessly vigilant, transforming shadows into light”

The objective of this paper is not merely to reiterate the contents of the Aitek 6 manual but to distill its essential elements to elucidate the concept of the knowledge cartridge. Aitek represents a versatile platform featuring a semantic methodology for describing an environment, termed the ‘knowledge builder’. This modeling process yields a comprehensive description and data catalog, facilitating an understanding of and interaction with the environment, weighted according to risk and performance metrics.

This data catalog, structured according to a performance and risk hierarchy, adopts a fractal-like logic. It spans from strategic considerations down to operational activities, categorized across three axes: outcomes, expertise, and resources, each with their temporal, analytical, and performance aggregation dimensions. The ‘Data Wizard’ module connects this structured framework to physical data, whether structured or unstructured, culminating in a detailed vector base description encompassing “replicat” processes, aggregation, auto-ML, clustering, and Query 360 operations.

Populating the vector base unfolds in three phases:

PHASE 1:

A unified replica is created, formatting and denormalizing data to the most granular level of the model’s analytical entities and cleansing the data. This phase integrates representative metadata from unstructured data into the replica.

PHASE 2:

The system clusters indicators both globally and by semantic node within the model, calculating percentiles for each cluster and node, and categorizing outliers in a classified list by family within the replica. Two intelligent agents are generated for predicting each measurement/indicator and percentile. These agents extend the model with one agent per cluster and another for the entire dataset. Initially, the system evaluates and selects the optimal algorithm from our library based on the accuracy rate. Each prediction runs two algorithms in parallel, with the result being a weighted average, akin to a random forest algorithmic approach.

PHASE 3:

Flowmarts are created as types of datamarts through aggregation by the model’s entities on a daily, monthly, quarterly, and yearly basis from the replica, loaded into a PostgreSQL-type DBMS. Outliers are also loaded into DBMS tables.

This detailed framework for the knowledge cartridge within Aitek not only showcases the platform’s robustness in handling and analyzing complex datasets but also demonstrates its pioneering approach to semantic AI modeling and data management, paving the way for advanced analytical capabilities in various operational contexts.

In our proposed model, a notable innovation is the mechanism to address the absence of specific indicators within the datalake. The system initiates an automated process to generate order entries and corresponding SQL tables, effectively creating a dynamic linkage to the datalake. This ensures the model’s adaptability and scalability by allowing for the seamless integration of emerging data sources, thereby maintaining its relevance and utility over time.

Furthermore, the model integrates a sophisticated system for capturing both analogic and digital data emanating from a variety of sensors and cameras. These devices are interfaced with an IoT Box, which in turn is connected to a dedicated data replica. This architecture facilitates the aggregation of data across all IoT Boxes within the same category, promoting a cohesive data analysis framework.

Each IoT Box is constructed as a cluster of three nano PCs, typified by Raspberry Pi units, serving distinct functions: data acquisition and preprocessing, data processing, and data storage and communication. The architecture of the IoT Box is designed to ensure efficient data handling, from initial capture through to analysis and storage.

At the core of the data processing unit is a four-layer neural network, tailored to classify outputs into four categories: alarms, verification requirements, errors, or non-issues. This neural network architecture is pivotal for the system’s ability to share or update its knowledge base. It achieves this through the transmission of a weight matrix of the neurons to the IoT Box, which then applies gradient descent for optimization.

The employment of a four-layer neural network is strategic, providing a balance between complexity and computational efficiency. This design enables the rapid processing of data and the generation of actionable insights, with the added capability of knowledge transfer and updating. The IoT Box’s processing unit does not independently learn; instead, it applies updates received from the central system, ensuring consistency and accuracy across the network.

This integrated system exemplifies a forward-thinking approach to data analysis, combining the Internet of Things (IoT) with advanced machine learning techniques. The result is a robust framework capable of processing vast amounts of data in real-time, while also possessing the flexibility to adapt to new data sources and analytical requirements. This innovation holds significant potential for applications requiring real-time data analysis and decision-making, offering a scalable and adaptable solution in the ever-evolving landscape of data science.

In the supervisor module, the semantic tree of the model translates percentiles into percentages of performance and risk. The proportion of outliers within the cluster associated with the business interpretation node is converted into a probability percentage. The count of indicators not linked to the information flow provides a measure of completeness. The model is constructed top-down, from mission to activity, whereas the calculation and propagation of performance/risk metrics move bottom-up, from the indicator level to the mission. Additionally, the system offers a generic dashboard creation tool with forecasting and simulation capabilities.

The system employs a pattern based on a combination of performance percentages for each model node, termed a diagnostic. The interface allows for manual generation of these diagnostics.

Anomalies are processed according to settings defined for each model node. For each type of outlier, an alarm is configured, which, depending on its type (manually or automatically dismissible), is connected to an action plan. An action plan comprises a set of actions and phases executed by designated individuals or resources within a theoretical timeframe. The transition from one phase to another occurs either automatically, after a set duration (timer), based on feedback from an AI program, upon detecting a file or specific information within a file, receiving an email, or via a voice over IP call. This structured approach ensures a comprehensive and responsive system capable of adapting to dynamic operational environments, offering a sophisticated framework for managing performance and risk across various organizational levels.

In the diagnostic component, which acts as a “geographic” distance based on combinations of performance metrics within the model, aggregates information using percentile bases. Each diagnostic is linked to two types of alarms: a preventative alarm and a corrective alarm, each connected to its respective action plan, for prevention and correction respectively.

If the “similarity” score falls between 60% and 70%, the system activates the preventative alarm. Should the score exceed this range, the corrective alarm is triggered. This mechanism ensures that potential issues are addressed proactively before escalating, while more severe discrepancies prompt immediate corrective measures to mitigate risks and optimize operational efficiency. This dual-alarm system facilitates a nuanced approach to managing operational dynamics, allowing for both anticipatory adjustments and direct interventions as dictated by the evolving situational context.

For the diagnostic component, which acts as a “geographic” distance based on combinations of performance metrics within the model, information is aggregated based on percentile rankings. Each diagnostic is linked to two alarms: a preventative alarm and a corrective alarm, with each connected to its specific action plan, for prevention and correction, respectively.

When the “similarity” falls between 60% and 70%, the system activates the preventative alarm. If the similarity exceeds this threshold, the corrective alarm is engaged. It’s crucial to note that any action plan can be scheduled to trigger at a specific time (dd.mm.yyyy hh:mm:ss), but this necessitates assigning the necessary resources for that date and the theoretical duration of the action plan.

Upon the activation of an action plan, the responsible party allocates resources, and the AI suggests available resources of each required type. For certain action plans, the AI can be configured to automatically assign resources and dispatch mission orders to everyone involved, including the plan’s overseer.

The AI updates a resource planning schedule (human, financial, and material) that is visible through filters in either a Gantt/PERT chart or calendar format. This system ensures efficient resource management and timely action plan execution, enabling a proactive and responsive approach to operational challenges.

The Aitek vector base is a complex structure composed of four sub-bases, designed to systematically enhance and evolve through continuous improvement following the Deming PDCA (Plan-Do-Check-Act) cycle. This scientific approach is not only innovative but also pivotal in advancing the field of artificial intelligence and big data analytics.

The first sub-base, known as the “DO” base, establishes the foundation of this system. It acts as a bridge, structuring the vast and often chaotic datalake into coherent vectors aligned with a semantic model. This structuration is critical for transforming raw data into a format that is not only manageable but also meaningful and directly linked to the operational objectives of the system.

Once the “DO” phase is implemented, the system enters a dynamic state, characterized by its alarms and action plans. Information derived from these alarms and diagnostics— because they are tethered to the semantic model—gets reintegrated into the datalake, ensuring coherence and relevance. This reintegration process corresponds to the “CHECK” subbase, where the system evaluates its performance and the efficacy of its responses.

Subsequent to the “CHECK” phase, the “ACT” sub-base comes into play. Here, information generated by the execution of action plans linked to the model is gathered. This not only includes the outcomes of these plans but also the insights and learnings derived from their application. This iterative learning process is fundamental to the PDCA cycle, facilitating not just correction but also innovation.

Finally, the “PLAN” sub-base encapsulates the resources and time series data that supported the execution of action plans. By integrating these elements in a coherent manner, the system ensures that all operational activities are rooted in strategic planning and informed decision-making.

The amalgamation of these four sub-bases—underpinned by auto-ML algorithms and pattern recognition—enables the AI to self-improve and evolve. This self-ameliorating mechanism is designed to refine the system’s performance continuously, ensuring it becomes more effective and efficient over time. By leveraging the PDCA cycle, the Aitek vector base exemplifies how structured, data-driven approaches can revolutionize the capability of AI systems to adapt and thrive in complex operational environments.

The Query 360 functionality within AITEK’s solution provides users with a powerful tool to access a vast amount of data quickly and accurately, enabling comprehensive information retrieval, such as all the relevant information associated with a specific individual or entity.

When a user initiates a Query 360 request, they specify a defined time period and an analysis key that serves as a search criterion. The system then begins the search process by examining the elements present in the burn list. This list contains “sensitive” elements that are not intended to be visible to everyone but still remain in the database for analysis and machine learning purposes.

Following this initial step, a series of subsequent actions, including dispatching, execution, and fetching, are performed to obtain the desired elements and store them in a separate database. This dedicated database acts as a display interface, presenting the retrieved data to the user in a structured and organized manner.

In parallel with the Query 360 execution, a report is generated that summarizes the data retrieved from the Query. This report provides a comprehensive overview of the information accessed during the Query 360 process, offering insights, trends, and analysis related to the specified time period and analysis key.

The Query 360 functionality enables users to access a wide range of data efficiently and effectively, facilitating in-depth analysis and informed decision-making. By leveraging the burn list to ensure data privacy and security, users can confidently explore and retrieve relevant information while maintaining appropriate data governance. The accompanying report adds value by providing a concise summary of the retrieved data, further enhancing the user’s understanding and enabling them to derive actionable insights from the Query 360 results.

KNOWLEDGEMART SHARING

In the realm of enhancing organizational security measures, the sharing of data between entities has emerged as a critical strategy. To address this need, Aitek has innovated a system inspired by the principles of the “Schengen system,” designed to streamline the process of data sharing among organizations. This chapter explores the architecture and operational mechanisms of this shared vector database system, emphasizing its role in facilitating the exchange of selected data vectors or sub-vectors among authorized organizations.

The Concept of a Shared Vector Database: at the core of Aitek’s solution is the shared vector database, a centralized repository that enables the sharing and access of data vectors or sub-vectors among participating organizations. This section delves into the design principles of the shared vector database, outlining how it mirrors the Schengen system’s openness within a controlled and secure framework.

Security Measures and Data Access Control: implementing stringent security protocols is paramount to ensuring that only authorized parties can access the shared data. This part of the chapter examines the security measures in place, including encryption, access controls, and authentication mechanisms, to safeguard the data from unauthorized access or misuse.

Rights and Authorizations for Data Sharing: central to the operation of the shared vector database is the system of rights and authorizations that governs data access and usage. This section details the process by which organizations are granted specific permissions to view and utilize the shared data, emphasizing the criteria and considerations involved in granting these rights.

Selective Data Sharing and Relevance: The decision-making process surrounding which data to share is critical and is undertaken with meticulous care by each participating organization. The criteria for selecting data for sharing are explored, focusing on the relevance and utility of the data in enhancing security measures across the collaborative network.

Collaboration and Enhanced Security Through Data Sharing: the shared vector database system represents a significant advancement in collaborative security efforts, enabling organizations to pool their resources, knowledge, and data to collectively enhance security measures. This section discusses the benefits of such collaboration, including improved data analysis, shared expertise, and a unified approach to addressing security challenges.

Data Security and Privacy Considerations: while the system facilitates unprecedented levels of cooperation among organizations, it also raises important considerations regarding data security and privacy. This part of the chapter addresses these concerns, detailing the safeguards and protocols in place to ensure that sensitive information is protected and that the system’s integrity is maintained.

Aitek’s shared vector database system, inspired by the Schengen system, marks a significant innovation in the field of organizational security. By enabling the controlled sharing of data among authorized organizations, the system enhances the collective ability to improve security measures. This chapter has provided a comprehensive overview of the system’s architecture, operational mechanisms, and the careful balance it strikes between openness and security, highlighting its potential to transform collaborative security efforts while maintaining a steadfast commitment to data privacy and protection.

The Knowledge Mart within the vector database of the Aitek platform is an aggregation of data drawn from a variety of heterogeneous sources, both structured and unstructured, housed within a data lake and owned by distinct entities. For instance, in the realm of homeland security, data might originate from the central bank, air and border police, or telecommunications operators. This diversity presents a unique challenge; if an error is discovered within the returned information of a Query 360 operation, any corrective measures (“patches”) applied to the Knowledge Mart during the next refresh could potentially be undone or only partially retained until the original data source addresses the error.

Aitek manages this situation through a sophisticated mechanism within its data management infrastructure. Upon identifying an error, the platform utilizes its Data Wizard module to access the source’s ownership details stored within its populated dictionary. At the moment of applying an update to correct the error, a “expiration date” is assigned to the internal modification, by default set to one month. Concurrently, the system dispatches an email to the data source’s owner, detailing the necessary corrections along with a deadline, also typically set for one month. This initiates a patching process for the database to correct historical data.

During the expiration period, the update process will adjust the incremental data loaded onto a temporary replica—prior to clustering and percentile calculations—to ensure the patch’s impact is minimized. Beyond the expiration date, the system will remove the correction from its update mechanism, operating under the assumption that the data owner has made the requisite adjustments upstream.

This approach underscores Aitek’s proactive strategy in ensuring data integrity within its KnowledgeMart. By instituting a temporary patch system coupled with an automated notification and expiration framework, Aitek navigates the complexities of managing data from diverse and autonomous sources. This methodology not only maintains the accuracy and reliability of the platform’s data analytics capabilities but also encourages data source owners to promptly address and rectify identified inaccuracies. It represents a balanced collaboration between Aitek’s systemic capabilities and the accountability of external data providers, thereby enhancing the overall quality and trustworthiness of the data within the platform’s ecosystem.

The implementation of an incremental refresh mode within a data lake environment presents a significant challenge due to the asynchronous and independent updates of data sources. Achieving coherence in the sequencing within the Knowledge Mart is crucial for maintaining data integrity and relevance. The granularity of coherence for the refresh process is determined by the timestamp of the components within the conceptual object (pre-replica). However, to ensure overall data consistency, a forced refresh of any remaining components is conducted once daily. This includes integrating the latest known versions of conceptual object components that have not been updated, thus preserving the continuity and integrity of the data.

Moreover, the re-calculation of clustering on the most recent additions to the Knowledge Mart is performed by default once a month. This process is vital for maintaining the structured organization of data within the Knowledge Mart, allowing for efficient retrieval and analysis. The default monthly schedule for clustering re-calculation ensures that the system remains scalable and responsive to new data without overwhelming computational resources. However, the system offers flexibility through the ability to manually trigger this re-calculation process via an action plan. This manual intervention capability is critical for addressing specific analytical needs or data integrity issues that arise from sudden changes or additions to the data lake.

This incremental refresh strategy reflects a sophisticated approach to managing the complexities of a dynamic data environment. By leveraging timestamps for granularity and instituting both automated and manual mechanisms for data refresh and clustering re-calculation, the system ensures that the Knowledge Mart remains a reliable and coherent resource for data analysis. This methodology underscores the importance of adaptability and precision in the management of big data, enabling organizations to derive actionable insights from their data assets while maintaining high standards of data quality and integrity.

Chapter 0.1: reminder: Knowledge Cartridge

“In the domain of security: knowledge is the fortress, vigilance is its guardian.”

The knowledge cartridge represents a specialized configuration set for the Aitek engine, tailored to specific industry needs and designed to be replicable. This configuration encapsulates the essence of Aitek’s artificial intelligence and big data capabilities (vectoriel database), fine-tuned to address the unique challenges and opportunities within a particular sector.

Each cartridge integrates a comprehensive suite of parameters, algorithms (generate by auto-ML), and semantic models, aligning the engine’s functionality with industry-specific requirements. This enables the Aitek platform to quickly adapt to different operational contexts, ensuring that its predictive analytics, data management, and decision-support tools are directly relevant to the targeted industry’s needs.

The replicability of these cartridges is a crucial aspect, allowing for the rapid deployment of Aitek’s technology across various sectors with minimal customization effort. This feature not only accelerates the implementation process but also ensures consistency in performance and outcomes across different applications.

By leveraging the knowledge cartridge approach, businesses can harness the power of AI and big data analytics without the need for extensive reconfiguration or development from scratch. This method provides a scalable, efficient pathway to digital transformation, enabling organizations to stay at the forefront of innovation in their respective industries.

This involves the semantic model, encompassing generic strategic requirements, the standard organization of business units, and classic professions implicated in the industry. It also requires a deep understanding of the risks, processes, and operational and support activities specific to the concerned industry. Additionally, it’s essential to know how, in best practices, the desired impact is measured (Results), that execution follows best practices (Know-how), and that resources are utilized optimally (Means).

Identifying relevant analysis entities and optimal business aggregation entities is crucial. These should be significant enough to ensure that the flowmarts and diagnostics are comprehensive. This step involves creating the knowledge builder. This part must be completed in close collaboration with one or more industry experts and an expert in the Aitek methodology. The quality of the model will directly correlate with the quality of the knowledge cartridge, underlining the importance of expert input in shaping the semantic structure and ensuring the cartridge’s effectiveness in addressing industry-specific challenges.