An Approach to Designing Very Fast Approximate String Matching Algorithms
IEEE Transactions on Knowledge and Data Engineering
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
International Journal of Human-Computer Studies
Active Cyber Attack Model for Network System's Vulnerability Assessment
ICISS '08 Proceedings of the 2008 International Conference on Information Science and Security
Cyber attack modeling and simulation for network security analysis
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Introduction to Information Retrieval
Introduction to Information Retrieval
Cyberpandemics: History, Inevitability, Response
IEEE Security and Privacy
ODE: Ontology-assisted data extraction
ACM Transactions on Database Systems (TODS)
Ontology-based information extraction: An introduction and a survey of current approaches
Journal of Information Science
Probabilistic Topic Models for Learning Terminological Ontologies
IEEE Transactions on Knowledge and Data Engineering
The Media: A Terrorist Tool or a Silent Ally?
EISIC '11 Proceedings of the 2011 European Intelligence and Security Informatics Conference
The dark web portal project: collecting and analyzing the presence of terrorist groups on the web
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
IEEE Transactions on Intelligent Transportation Systems
Data Extraction for Deep Web Using WordNet
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Instant messengers IMs and social networking sites SNS such as Facebook may contain harmful and suspicious messages, which is of national security concerns. Organised crimes have adopted online chatting technique to send these suspicious messages as these systems have all the facilities and could serve as platform to spread across their information widely through socio-engineered and general text messages. A solution to this problem is to detect suspicious messages from the typed messages. In this paper, we proposed a suspicious message detection system SMDs to detect suspicious messages. SMDs framework makes use of databases where instant messages are stored and an ontology information extraction technique which is able to detect suspicious messages using probabilistic models. The objective of SMDs framework is to trace the identified criminals by browsing their profile details available from their e-mail account, where suspicious messages are discovered during online chat. Experimental analysis is evaluated using the user generated content UGC testbed which consist of suspicious messages for eight different test cases with user-defined threshold value tested using SMDs. The results obtained shows high precision rate compared to the existing state-of-the-art systems.