Visible to the public Biblio

Filters: Keyword is keyword extraction  [Clear All Filters]
2017-05-19
Sheeba, J. I., Devaneyan, S. Pradeep.  2016.  Recommendation of Keywords Using Swarm Intelligence Techniques. Proceedings of the International Conference on Informatics and Analytics. :8:1–8:5.

Text mining has developed and emerged as an essential tool for revealing the hidden value in the data. Text mining is an emerging technique for companies around the world and suitable for large enduring analyses and discrete investigations. Since there is a need to track disrupting technologies, explore internal knowledge bases or review enormous data sets. Most of the information produced due to conversation transcripts is an unstructured format. These data have ambiguity, redundancy, duplications, typological errors and many more. The processing and analysis of these unstructured data are difficult task. But, there are several techniques in text mining are available to extract keywords from these unstructured conversation transcripts. Keyword Extraction is the process of examining the most significant word in the context which helps to take decisions in a much faster manner. The main objective of the proposed work is extracting the keywords from meeting transcripts by using the Swarm Intelligence (SI) techniques. Here Stochastic Diffusion Search (SDS) algorithm is used for keyword extraction and Firefly algorithm used for clustering. These techniques will be implemented for an extensive range of optimization problems and produced better results when compared with existing technique.

2017-03-07
Allawi, M. A. A., Hadi, A., Awajan, A..  2015.  MLDED: Multi-layer Data Exfiltration Detection System. 2015 Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec). :107–112.

Due to the growing advancement of crime ware services, the computer and network security becomes a crucial issue. Detecting sensitive data exfiltration is a principal component of each information protection strategy. In this research, a Multi-Level Data Exfiltration Detection (MLDED) system that can handle different types of insider data leakage threats with staircase difficulty levels and their implications for the organization environment has been proposed, implemented and tested. The proposed system detects exfiltration of data outside an organization information system, where the main goal is to use the detection results of a MLDED system for digital forensic purposes. MLDED system consists of three major levels Hashing, Keywords Extraction and Labeling. However, it is considered only for certain type of documents such as plain ASCII text and PDF files. In response to the challenging issue of identifying insider threats, a forensic readiness data exfiltration system is designed that is capable of detecting and identifying sensitive information leaks. The results show that the proposed system has an overall detection accuracy of 98.93%.