An ontological representation of a taxonomy for cybercrime

The modern phenomenon of cybercrime raises issues and challenges on a scale that has few precedents. A particular central concern is that of establishing clarity about the conceptualization of cybercrime and its growing economic cost to society. A further related concern is focused on developing appropriate legal and policy responses in a context where crime transcends national jurisdictions and physical boundaries. Both are predicated on a better understanding of cybercrime. Efforts at defining and classifying cybercrime by the use of taxonomies to date have largely been descriptive with resulting ambiguities. This paper contributes a semi-formal approach to the development of a taxonomy for cybercrime and offers the conceptual language and accompanying constraints with which to describe cybercrime examples. The approach uses the ontology development platform, Protégé and the Unified Modeling Language (UML) to present an initial taxonomy for cybercrime that goes beyond the descriptive accounts previously offered. The taxonomy is illustrated with examples of cybercrimes both documented in the Protégé toolset and also using UML

An ontological representation of a taxonomy for cybercrime

The modern phenomenon of cybercrime raises issues and challenges on a scale that has few precedents. A particular central concern is that of establishing clarity about the conceptualization of cybercrime and its growing economic cost to society. A further related concern is focused on developing appropriate legal and policy responses in a context where crime transcends national jurisdictions and physical boundaries. Both are predicated on a better understanding of cybercrime. Efforts at defining and classifying cybercrime by the use of taxonomies to date have largely been descriptive with resulting ambiguities. This paper contributes a semi-formal approach to the development of a taxonomy for cybercrime and offers the conceptual language and accompanying constraints with which to describe cybercrime examples. The approach uses the ontology development platform, Protégé and the Unified Modeling Language (UML) to present an initial taxonomy for cybercrime that goes beyond the descriptive accounts previously offered. The taxonomy is illustrated with examples of cybercrimes both documented in the Protégé toolset and also using UML

Cyber Threat Intelligence Model: An Evaluation of Taxonomies, Sharing Standards, and Ontologies within Cyber Threat Intelligence

Cyber threat intelligence is the provision of evidence-based knowledge about existing or emerging threats. Benefits of threat intelligence include increased situational awareness and efficiency in security operations and improved prevention, detection, and response capabilities. To process, analyze, and correlate vast amounts of threat information and derive highly contextual intelligence that can be shared and consumed in meaningful times requires utilizing machine-understandable knowledge representation formats that embed the industry-required expressivity and are unambiguous. To a large extend, this is achieved by technologies like ontologies, interoperability schemas, and taxonomies. This research evaluates existing cyber-threat-intelligence-relevant ontologies, sharing standards, and taxonomies for the purpose of measuring their high-level conceptual expressivity with regards to the who, what, why, where, when, and how elements of an adversarial attack in addition to courses of action and technical indicators. The results confirmed that little emphasis has been given to developing a comprehensive cyber threat intelligence ontology with existing efforts not being thoroughly designed, non-interoperable and ambiguous, and lacking semantic reasoning capability

Conceptualizing Cybercrime: Definitions, Typologies and Taxonomies

Cybercrime is becoming ever more pervasive and yet the lack of consensus surrounding what constitutes a cybercrime has a significant impact on society, legal and policy response, and academic research. Difficulties in understanding cybercrime begin with the variability in terminology and lack of consistency in cybercrime legislation across jurisdictions. In this review, using a structured literature review methodology, key cybercrime definitions, typologies and taxonomies were identified across a range of academic and non-academic (grey literature) sources. The findings of this review were consolidated and presented in the form of a new classification framework to understand cybercrime and cyberdeviance. Existing definitions, typologies and taxonomies were evaluated, and key challenges were identified. Whilst conceptualizing cybercrime will likely remain a challenge, this review provides recommendations for future work to advance towards a universal understanding of cybercrime phenomena as well as a robust and comprehensive classification system

Towards a cyberterrorism life-cycle (CLC) model

Cyberterrorism has emerged as a new threat in the Information and Communication Technology (ICT) landscape. The ease of use, affordability, remote capabilities and access to critical targets makes cyberterrorism a potential threat to cause wide-scale damage. Cyberterrorism is often incorrectly perceived as encompassing all cybercrimes. However, cyberterrorism differs from cybercrime in various ways including motivation, attack goals, techniques and effects. Motivations for cyberterrorism, which is similar to terrorism in general, stem from religious, social and political views. Cyberterrorists generally would seek to have high impact in order to gain publicity for their cause, whereas cybercriminals often prefer to have their acts undetected in order to hide their financial theft, fraud or espionage. Therefore, there are various factors that drive the development of a cyberterrorist. This paper proposes a model for the development of cyberterrorism in order to show the various influential forces. The Cyberterrorism Life-Cycle (CLC) model presented in this paper is composed of five phases: Prepare, Acquaint, Choose, Execute, and Deter (PACED). In addition the paper looks at various factors, including social, practices, objectives, targets and countermeasures, which are mapped onto the PACED phases in order to show the interaction and dynamic nature during the life-cycle development

Cyber-victimization Trends in Trinidad & Tobago: The Results of An Empirical Research

Cybertechnology has brought benefits to the Caribbean in the form of new regional economic and social growth. In the last years, Caribbean countries have also become attractive targets for cybercrime due to increased economic success and online presence with a low level of cyber resilience. This study examines the online-related activities that affect cybercrime victimization by using the Routine Activity Theory (RAT). The present study seeks to identify activities that contribute to different forms of cybercrime victimization and develop risk models for these crimes, particularly the understudied cyber-dependent crimes of Hacking and Malware. It also aims to explore if there are similarities or differences in factors leading to victimization, which correlate to the classification of crimes as either cyber-dependent or cyber-enabled. The data analysis suggests that there is significant applicability for RAT in explaining Online Harassment victimization, while the usability of the RAT for predicting Malware victimization proved to be minimal, with only two significant variables being identified, with both being associated with Capable Guardianship

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

Key steps for the construction of a glossary based on FunGramKB Term Extractor and referred to international cooperation against organised crime and terrorism

The employment of new technological instruments for the processing of natural languages is crucial to improve the way humans interact with machines. The Functional Grammar Knowledge Base (FunGramKB henceforth) has been designed to cover Natural Language Processing (NLP henceforth) tasks in the area of Artificial Intelligence. The multipurpose lexical conceptual knowledge base FunGramKB is capable of combining linguistic knowledge and human cognitive abilities within its system as a whole. The conceptual module of FunGramKB contains both common-sense knowledge (Ontology), procedural knowledge (Cognicon) as well as knowledge about named entities representing people, places, organisations or other entities (Onomasticon). The Onomastical component is used to process the information from the perspective of specialised discourse. The definition in Natural Language of a consistent list of encyclopaedic terms existent referred to the legislation and to entities which fight against organised crime and terrorism existent in the GCTC would be the stepping stone for the future development of the Onomasticon. The FunGramKB Term Extractor (FGKBTE henceforth) is used to process the information. To cope with the inclusion of the terms in the Onomasticon according to the Conceptual Representation Language (COREL henceforth) schemata, the DBpedia project has been of paramount importance to develop specific patterns for the structure of the definitions.El empleo de nuevas herramientas tecnológicas para el Procesamiento del Lenguaje Natural (PLN en adelante) es fundamental para mejorar la forma en que las máquinas se relacionan con los seres humanos. FunGramKB ha sido diseñada para abordar tareas de PLN inmersas en el área de la Inteligencia Artificial. La base de conocimiento léxico conceptual multipropósito FunGramKB es capaz de combinar el conocimiento lingüístico con las habilidades cognitivas humanas dentro de su sistema como conjunto. El modulo conceptual de FunGramKB se basa en el sentido común (Ontología) y en el conocimiento procedimental (Cognicón), a la vez que en el conocimiento sobre entidades nombradas que representan personas, lugares, organizaciones u otras entidades (Onomasticon). La definición en Lenguaje Natural de una lista consistente de términos enciclopédicos concerniente tanto a instrumentos legales como a organizaciones que luchan contra el crimen organizado y el terrorismo que se ha incluido en el GCTC supondrá un gran adelanto en aras al futuro desarrollo del Onomasticon. El FGKBTE se usa para procesar la información. Con vistas a incluir los términos en el Onomasticón de acuerdo al esquema COREL, el proyecto DBpedia ha sido de una importancia fundamental para desarrollar patrones determinados con los que estructurar las definiciones.Universidad de Granada. Departamento de Filologías Inglesa y Alemana. Máster en Lingüística y Literatura Inglesas, curso 2013-201

A semantic ontology for disaster trail management system

Disasters, whether natural or human-made, leave a lasting impact on human lives and require mitigation measures. In the past, millions of human beings lost their lives and properties in disasters. Information and Communication Technology provides many solutions. The issue of so far developed disaster management systems is their inefficiency in semantics that causes failure in producing dynamic inferences. Here comes the role of semantic web technology that helps to retrieve useful information. Semantic web-based intelligent and self-administered framework utilizes XML, RDF, and ontologies for a semantic presentation of data. The ontology establishes fundamental rules for data searching from the unstructured world, i.e., the World Wide Web. Afterward, these rules are utilized for data extraction and reasoning purposes. Many disaster-related ontologies have been studied; however, none conceptualizes the domain comprehensively. Some of the domain ontologies intend for the precise end goal like the disaster plans. Others have been developed for the emergency operation center or the recognition and characterization of the objects in a calamity scene. A few ontologies depend on upper ontologies that are excessively abstract and are exceptionally difficult to grasp by the individuals who are not conversant with theories of the upper ontologies. The present developed semantic web-based disaster trail management ontology almost covers all vital facets of disasters like disaster type, disaster location, disaster time, misfortunes including the causalities and the infrastructure loss, services, service providers, relief items, and so forth. The objectives of this research were to identify the requirements of a disaster ontology, to construct the ontology, and to evaluate the ontology developed for Disaster Trail Management. The ontology was assessed efficaciously via competency questions; externally by the domain experts and internally with the help of SPARQL queries