Ontology-Driven Cyber-Security Threat Assessment Based on Sentiment Analysis of Network Activity Data

Lundquist, Doug; Zhang, Xiaoying; Ouksel, Aris M.

doi:10.1109/iccac.2014.42

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…Most of the analyzed investigations use machine learning to evaluate sentiment. Natural language processing techniques are explored in the approaches proposed in [52,58,60,[62][63][64]74,75]; data mining and semantic analysis are used in [51]. In general, the studies presented better assertiveness measures in the categorization of sentiment using complementary approaches (e.g., semantic descriptors with ML) as compared with a single one.…”

Section: Discussion On Related Workmentioning

confidence: 99%

“…This characteristic was not present when using ontologies for semantic analysis of texts written in natural language. The use of NLP techniques used in conjunction with ontologies demonstrated good results in [74], reaching an accuracy of 86%.…”

Section: Discussion On Related Workmentioning

confidence: 99%

“…Existing studies (e.g., [60,63,74]) showed that a strategy that combines techniques (e.g., ontologies and ML) is a more viable path to deal with our problem.…”

Section: Discussionmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Discussion On Related Workmentioning

confidence: 99%

Section: Discussion On Related Workmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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“…c) Interoperability in result communication by multiple transport protocols and data formats using alerting mode and storage capabilities d) Interoperability in result visualization by integration with existing systems and dashboards. It allows the ability to build and deploy existing custom gadgets with existing dashboards and the ability of its integration with third-party visualization and reporting tools (Brohi et al, 2016;Lundquist et al, 2014).…”

Section: Data Visualizingmentioning

confidence: 99%

Big Data Interoperability for E-Governance

Mahmoud¹,

Omar²,

Ouksel³

2019

Journal of Computer Science

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The notion of governance is innate to any society in the word. The use of big-data allow governance as a competitive advantage to attract resources, with the emergence of the social web and exponential data generated accordingly. Such social web and exponential data can be analyzed to give the enlightened human beings about the good governance and maintain an intelligent global state of mega cities. The government agencies should collect data from heterogeneous sources. To improve the services and applications, these agencies offer the citizens and businesses, through big data analytics, which is a global opportunity to integrate a large number of public databases together, fraud detection and tax control activities. The impact of big data on these e-government applications strives to facilitate better governance by demonstrating its use in research and practice. Therefore, the current paper zeroes in on an approach based on the interoperability and integration of big data. In our approach, such data is gathered from several separate and heterogeneous government sources.

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“…Contudo, foram identificadas técnicas e aperfeiçoamentos na área de categorização de emoções e sentimentos que abordam intenções indiretamente. As propostas avaliadas fazem uso de técnicas de processamento de linguagem natural (Anzovino et al, 2018), (Appling et al, 2015), (Barreira et al, 2017), (Hagen et al, 2015), (Hu & Wang, 2016), (Justo et al, 2014), (Lundquist, Zhang, & Ouksel, 2015), (Maynard, Bontcheva, & Augenstein, 2016), mineração de dados e análise da semântica (García-Díaz et al, 2018). Neste sentido, 9 estudos (Anzovino et al, 2018), (Appling et al, 2015), (Barreira et al, 2017), (García-Díaz et al, 2018), (Hagen et al, 2015), (Hu & Wang, 2016), (Justo et al, 2014), (Lundquist et al, 2015), (Maynard et al, 2016) apresentaram melhores índices de assertividade na categorização de sentimentos.…”

Section: Discussão E Conclusãounclassified

Um Estudo Sobre Análise, Representação E Detecção De Intenções De Criminosos Em Postagens Em Mídia Social

Mendonça¹,

Rosa²,

Bonacin³

2019

Atas Da Conferência Ibero-Americana WWW/Internet 2019

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As mídias sociais se transformaram em um instrumento de comunicação entre criminosos, para planejamento e execução de crimes, bem como para recrutar novos membros. Eles utilizam diferentes estratégias de comunicação, tais como linguagens cifradas e gírias restritas a grupos ou facções para burlar investigações. A pesquisa por ferramentas e técnicas para apoiar a atividade investigativa e o processo de prevenção de crimes são de extrema importância; particularmente, a análise e detecção de intenções relacionadas a crimes. Os aspectos tecnológicos, humanos e sociais relacionados a este problema, torna-o um campo rico de estudo, envolvendo a interação entre IHC (Interação Humano-Computador) com diversas áreas de pesquisa. Pesquisas ligadas às áreas de segurança de informação, linguística, aprendizagem de máquina e processamento de linguagem natural, têm contribuído para o avanço na análise e detecção de intenções em mídia social. Este artigo apresenta uma revisão quasi-sistemática da literatura sobre análise, representação e detecção de intenções de criminosos em postagens em mídia social. 27 estudos foram analisados de acordo com as abordagens utilizadas (ex.: técnicas de aprendizagem de máquina), bem como seus fundamentos em aspectos linguísticos, ontologias, semiótica e teoria dos atos da fala. Os resultados apontam avanços na solução do problema e questões de pesquisas em aberto para a área de IHC e relacionadas.

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Ontology-Driven Cyber-Security Threat Assessment Based on Sentiment Analysis of Network Activity Data

Cited by 7 publications

References 17 publications

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Big Data Interoperability for E-Governance

Um Estudo Sobre Análise, Representação E Detecção De Intenções De Criminosos Em Postagens Em Mídia Social

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