Heum Park

Cyber Forensics Ontology for Cyber Criminal Investigation

We developed Cyber Forensics Ontology for the criminal investigation in cyber space. Cyber crime is classified into cyber terror and general cyber crime, and those two classes are connected with each other. The investigation of cyber terror requires high technology, system environment and experts, and general cyber crime is connected with general crime by evidence from digital data and cyber space. Accordingly, it is difficult to determine relational crime types and collect evidence. Therefore, we considered the classifications of cyber crime, the collection of evidence in cyber space and the application of laws to cyber crime. In order to efficiently investigate cyber crime, it is necessary to integrate those concepts for each cyber crime-case. Thus, we constructed a cyber forensics domain ontology for criminal investigation in cyber space, according to the categories of cyber crime, laws, evidence and information of criminals. This ontology can be used in the process of investigating of cyber crime-cases, and for data mining of cyber crime; classification, clustering, association and detection of crime types, crime cases, evidences and criminals.

Extended Relief Algorithms in Instance-Based Feature Filtering

This paper presents extended Relief algorithms and their use in instance-based feature filtering for document feature selection. The Relief algorithms are general and successful feature estimators that detect conditional dependencies of features between instances, and are applied in the preprocessing step for document classification and regression. Since the introduction the Relief algorithm, many kinds of extended Relief algorithms have been suggested as solutions to problems of redundancy, irrelevant and noisy features as well as Relief algorithms limitations in two-class and multi-class datasets. In this paper, we introduce additional problems including the negative influence of computation similarities and weights caused by the small number of features in an instance, the absence of nearest Hits or nearest Misses for some instances using Relief algorithms, and other of problems. We suggest new extended Relief algorithms to solve those problems, having in the course of our research, and experimented on the estimation of the quality of features from instances, and classified datasets, and having compared the results of the new extended Relief algorithms. Indeed in the experimental results, the new extended Relief algorithms showed better performances for all of the datasets than did the Relief algorithms

Ontology-based Approach to Intelligent Ubiquitous Tourist Information System

This paper presents an ontology-based approach to an intelligent ubiquitous tourist information system. With the recent advances in Internet and ubiquitous technologies, there is increasing use of intelligent tourist information services via the web and mobile systems. Recently, those services have provided integrated heterogeneous travel information, recommended tourist attractions tailored to user profiles and travelers’ preferences, as well as services for pedestrian travelers using mobile systems. In parallel to these developments, many studies on the ontological approach to intelligent tourist information services have been introduced. However, there have been only a few studies undertaken from the perspective of both tour services and travelers’ preferences using ontology. Thus, in this paper, we propose a tourist domain ontology that consists of concepts for tourist contents and locations, and a tour service application ontology for various intelligent tour services. In addition, according to those ontologies, we designed an Ontology-based Intelligent Ubiquitous Tourist Information System (OiUTIS) for an interactive tourist information service tailored to both tour services and travelers in ubiquitous environments. Application Ontology; Touirst Information System; Ubiquitous Tourist System; Intelligent Tour Service

Detection and Analysis of Trend Topics for Global Scientific Literature Using Feature Selection Based on Gini-Index

As the volume and diversity of scientific resources grows, trend detection and analysis have become much more important issues. A variety of trend detection, characterization, evaluation and visualization methodologies have been introduced for various application domains. In this paper, we consider detection of temporal trends for topics using feature selection and extraction of additional information from the subtopics of them, for the Global Trends Briefing (GTB) dataset. Thus, we propose a novel trend detection method using feature selection based on the Improved Gini-Index (I-GI) algorithm, which can obtain representative features for given topics. Second, with those features, we extract subtopics for the topics and visualize temporal/emerging/upward/ downward trends with them. Third, utilizing the relations among the subtopics, we obtain relevant documents and seed sentences that co-occur with the upper features for the topic. In addition, we can extract information to forecast future trends relevant to the issues: for example, financial market or emerging technology. In the experimental results, we could obtain good representative features, more specific trends for the topics, and additional useful information.

Similarity Measurement among Sectors Using Extended Relief-F Algorithm for Disk Recovery

This paper presents an approach to the recovery of damaged disks that measures the similarity among sectors using the Instance-based Feature Filtering algorithm and classification. After a hard-disk is destroyed, maliciously or accidentally, that hard-disk can be simply repaired using the recovery programs. However, there are always some sectors that cannot connect with the original file after recovery; typically, attempts are made to connect with the original file manually, or those attempts prove unsuccessful, the effort is abandoned. Therefore, an automatic process for finding the original file related to unconnected sectors is required. Typical methods assess the similarity among sectors and recommend relevant candidate sectors. Thus we propose an algorithm and process that can automatically find relevant sectors with the Extended Relief-F algorithm and the classifiers. We reformulated the Relief-F algorithm to select features by updating the difference functions and computation of the weight of features, apply those features to sectors, classify unconnected sectors, and recommend relevant candidate sectors. In the experiments, we also tested Information Gain, Odds Ratio and Relief-F for feature selection and compared them with the Extended Relief-F algorithm; additionally, we used the KNN and SVM classifiers for classification and estimation of relevant sectors. In the experimental results, the Extended Relief-F algorithm, compared with the others, performed best for all of the datasets.

The Method of Verification for Legal Admissibility of Digital Evidence using the Digital Forensics Ontology

ABSTRACT Although the various crime involved numerous digital evidence, the digital evidence is hard to be acknowledged as a evidence to proof the crime fact in court. We propose the method of verification for the legal admissibility of digital evidence using digital forensics ontology. In order to verify the legal admissibility of digital evidence, we will extend the digital ontology by standard digital forensics process from Digital Forensics Technical Manual defined by KNPA and set up the relation properties and the rule of property constraint to process class in the digital forensics ontology. It is possible for proposed ontology to utilize to plan the criminal investigation and to educate the digital forensics.Keywords:Digital Forensics Ontology, Digital Evidence, Verification For Legal Admissibility of Digital Evidence 1. 서 론 1) 정보화 사회가 정착되면서 컴퓨터와 인터넷의 사용이 일반인들의 생활에 많은 부분을 차지하고 있어 다양한 디지털 정보가 사용되고 있다. 한국의 경우 2007년도 상반기에 인터넷 사용자가 전체 인구의 75%를 차지하고 있다[1]. 또한 2003년도 버클리 대학의 연구 보고서에 따르면 전 세계적으로 생성되는 정보의 약 92% 이상이 디지털 형태로 나타나고 있다[2]. 따라서 인터넷 상에서 발생하는 사이버 범죄뿐만 아니라 실생활에서 발생하는 일반 범죄에서도 디지털 자

Two-phase prediction of protein functions from biological literature based on Gini-Index

This paper presents a two-phase prediction model for proteins and protein functions from biological literature based on Gini Index algorithm. As the volume and diversity of biological resources grows, computational protein function prediction become much more important. In this paper, we considered automatic annotation of the Gene Ontology (GO) by computational function prediction approaches entailing feature selection method based on Gini Index and protein function prediction model. Gini-Index has been used as a split measure for choosing the most appropriate splitting attribute in decision tree. Recently, the Gini-Index algorithm for feature selection in text categorization was introduced and proved to be good performances. Thus, we present a novel model to predict both multi-label proteins from PubMed literatures and their functions from protein-function of GO Annotation. First, we introduce a feature selection algorithm with Gini-Index expressions to predict proteins from PubMed and obtain proteintext subsets. Second, we propose a novel two-phase prediction method for proteins and their protein functions with those subsets. As experimental results, we evaluated the results of prediction for the proteins and their functions using the proposed methods. We have good performances notably overall for both of prediction of proteins and protein function from the biological literatures.

Filtering Methods for Feature Selection in Web-Document Clustering

This paper presents the results of a comparative study of filtering methods for feature selection in web document clustering. First, we focused on feature selection methods based on Mutual Information (MI) and Information Gain (IG). With those features and feature values, and using MI and IG, we extracted from documents representative max-value features as well as a representative cluster for a feature and a representative cluster for a document. Second, we tested the Max Feature Selection Method (MFSM) with those representative features and clusters, and evaluated the web-document clustering performance. However, when document sets yield poor clustering results by term frequency, we cannot obtain good features using the MFSM with the MI and IG values. Therefore, we propose new filtering methods, Min Count of Representative Cluster for a Feature (MCRCF) and Min Count of Representative Cluster for a Document (MCRCD). In the experimental results, the MFSM showed better performance than was achieved using only term frequency, MI and IG. And when we applied the new filtering methods for feature selection (MCRCF, MCRCD), the clustering performance improved notably. Thus we can assert that those filtering methods are effective means of feature selection and offer good performance in web document clustering.

A feature selection for Korean Web document clustering

This paper is a comparative study of feature selection methods for Korean Web documents clustering. First, we focused on how the term feature and the co-link of Web documents affect clustering performance. We clustered Web documents by native term feature, co-link and both, and compared the output results with the originally allocated category. And we selected term features for each category using X/sup 2/, information gain (IG), and mutual information (MI) from training documents, and applied these features to other experimental documents. In addition we suggested a new method named max feature selection, which selects terms that have the maximum count for a category in each experimental document, and applied X/sup 2/ (or MI or IG) values to each term instead of term frequency of documents, and clustered them. In the results, X/sup 2/ shows a better performance than IG or MI, but the difference appears to be slight. But when we applied the max feature selection method, the clustering performance improved notably. Max feature selection is a simple but effective means of feature space reduction and shows powerful performance for Korean Web document clustering.