Kisung Park

Confident wrapper-type semi-supervised feature selection using an ensemble classifier

Feature selection is an important data preprocessing step in pattern recognition. Recently, a wrapper-type semi-supervised feature selection method, known as FW-SemiFS, was proposed to overcome the small labeled sample problem of supervised feature selection. FW-SemiFS does not consider the confidence of predicted unlabeled data, but rather evaluates the relevance of features according to their frequency. Such frequencies are obtained via iterative supervised sequential forward feature selection (SFFS). However, the large amount of computational time associated with iterative SFFS is detrimental to FW-SemiFS. Furthermore, this relevance evaluation method eliminates the primary advantage of wrapper-type feature selection: the ability to evaluate the discriminative power of a combination of features. In this paper, we propose a new wrapper-type semi-supervised feature selection framework that can select a more relevant feature subset using confident unlabeled data. The proposed framework, called ensemble-based semi-supervised feature selection (EN-SemiFS), employs an ensemble classifier that supports the estimation of the confidence of unlabeled data. We analyzed the relationship between wrapper-type feature selection and the confidence of unlabeled data and explored how this relationship can make the semisupervised feature selection framework faster and more accurate. The experimental results revealed that the proposed method can select a more relevant feature subset when compared to existing methods.

An Efficient Method for Computing Similarity Between Frequent Subgraphs

Frequent sub graph mining and graph similarity measures are fundamental and prominent graph analytical techniques. These techniques are often applied together in many graph mining techniques such as clustering and classification. However, these techniques suffer from long running times because frequent sub graph mining and graph similarity measures have been applied independently. In this paper, we propose an efficient method that measures similarity between frequent sub graphs. Our method exploits byproducts of frequent sub graph mining for avoiding costly common sub graph search required in similarity measures. Through experiments on real world graph data, we show that our method measures similarities among all pair of frequent sub graphs within practical time.

Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks

The k-Nearest Neighbors (kNN) query is an important spatial query in mobile sensor networks. In this work we extend kNN to include a distance constraint, calling it a l-distant k-nearest-neighbors (l-kNN) query, which finds the k sensor nodes nearest to a query point that are also at l or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l- kNN query can be used in most kNN applications for the case of well distributed query results. To process an l-kNN query, we must discover all sets of kNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-kNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l- kNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting k sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l- kNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the kNN query in terms of energy efficiency, query latency, and accuracy.

RDB2RDF: completed transformation from relational database into RDF ontology

One of the most advantages of the Semantic Web is to augment the data with a well-defined meaning and linking between data by using the RDF ontology language. Today most of data are stored in relational databases. In order to reuse and infer this data on the Semantic Web, there is a need for converting the data stored in relational databases to the form of RDF. Some approaches have been proposed, however, most of them transform a single table into RDF triples. This paper presents RDB2RDF, a complete method to transform all tables in the relational database into RDF ontology. The transformation makes it possible to reverse RDF ontology to relational tables. Most of all, all the steps in RDB2RDF are done automatically without any user intervention.

Disk-based shortest path discovery using distance index over large dynamic graphs

Abstract The persistent alternation of the internet world is changing networks rapidly. Shortest path discovery, especially over dynamic networks such as web page links, telephone or route networks, and ontologies, has received intense attention because of its importance for services in IoT. For example, when a new road is newly opened or becomes unavailable for any unexpected reason, the shortest paths must be recomputed. The system should respond promptly to its users with the updated recommended paths. In this paper, we propose a disk-based shortest path method that updates the shortest paths in a very large dynamic graph efficiently. The proposed method uses partial shortest paths as indices for efficient shortest path discovery. We classify the changes in the graph into four cases, such as the insertion or deletion of edges and the increase or decrease of edge weights. Our proposed strategy considers updating only the corresponding parts of the indices for each case. Our experiments on real-world dynamic datasets verify that the proposed framework updates the shortest paths 4 to 50 times faster than the existing type of framework.

iTri: Index-based triangle listing in massive graphs

Abstract Triangle listing is a basic operator when dealing with many graph problems. However, in-memory algorithms do not work well with recently developed massive graphs such as social networks because these graphs cannot be accommodated in the memory. Thus, external memory-based algorithms have been proposed recently, but these approaches still require frequent multiple scans of the whole graph on the disk and large volumes of calculations are performed that involve the whole graph during every iteration. In this study, we propose a novel index-based method for listing triangles in massive graphs. First, we present new notions for the vertex range index and potential cone vertex index. Next, we propose an index join-based triangle listing algorithm. Our method accesses the indexed data asynchronously and joins them to list triangles using a multi-threaded parallel processing technique. Based on experiments, we demonstrate that our algorithm outperforms the state-of-the-art solution methods by three to eight times in terms of the wall clock time.

Security analysis and enhancements of an improved multi-factor biometric authentication scheme

Many remote user authentication schemes have been designed and developed to establish secure and authorized communication between a user and server over an insecure channel. By employing a secure remote user authentication scheme, a user and server can authenticate each other and utilize advanced services. In 2015, Cao and Ge demonstrated that An’s scheme is also vulnerable to several attacks and does not provide user anonymity. They also proposed an improved multi-factor biometric authentication scheme. However, we review and cryptanalyze Cao and Ge’s scheme and demonstrate that their scheme fails in correctness and providing user anonymity and is vulnerable to ID guessing attack and server masquerading attack. To overcome these drawbacks, we propose a security-improved authentication scheme that provides a dynamic ID mechanism and better security functionalities. Then, we show that our proposed scheme is secure against various attacks and prove the security of the proposed scheme using BAN Logic.

An effective graph summarization and compression technique for a large-scaled graph

Graphs are widely used in various applications, and their size is becoming larger over the passage of time. It is necessary to reduce their size to minimize main memory needs and to save the storage space on disk. For these purposes, graph summarization and compression approaches have been studied in various existing studies to reduce the size of a large graph. Graph summarization aggregates nodes having similar structural properties to represent a graph with reduced main memory requirements. Whereas graph compression applies various encoding techniques so that the resultant graph needs lesser storage space on disk. Considering usefulness of both the paradigms, we propose to obtain best of the both worlds by combining summarization and compression approaches. Hence, we present a greedy-based algorithm that greatly reduces the size of a large graph by applying both the compression and summarization. We also propose a novel cost model for calculating the compression ratio considering both the compression and summarization strategies. The algorithm uses the proposed cost model to determine whether to perform one or both of them in every iteration. Through comprehensive experiments on real-world datasets, we show that our proposed algorithm achieves a better compression ratio than only applying summarization approaches by up to 16%.

Secure Authentication Protocol for Wireless Sensor Networks in Vehicular Communications

With wireless sensor networks (WSNs), a driver can access various useful information for convenient driving, such as traffic congestion, emergence, vehicle accidents, and speed. However, a driver and traffic manager can be vulnerable to various attacks because such information is transmitted through a public channel. Therefore, secure mutual authentication has become an important security issue, and many authentication schemes have been proposed. In 2017, Mohit et al. proposed an authentication protocol for WSNs in vehicular communications to ensure secure mutual authentication. However, their scheme cannot resist various attacks such as impersonation and trace attacks, and their scheme cannot provide secure mutual authentication, session key security, and anonymity. In this paper, we propose a secure authentication protocol for WSNs in vehicular communications to resolve the security weaknesses of Mohit et al.’s scheme. Our authentication protocol prevents various attacks and achieves secure mutual authentication and anonymity by using dynamic parameters that are changed every session. We prove that our protocol provides secure mutual authentication by using the Burrows–Abadi–Needham logic, which is a widely accepted formal security analysis. We perform a formal security verification by using the well-known Automated Validation of Internet Security Protocols and Applications tool, which shows that the proposed protocol is safe against replay and man-in-the-middle attacks. We compare the performance and security properties of our protocol with other related schemes. Overall, the proposed protocol provides better security features and a comparable computation cost. Therefore, the proposed protocol can be applied to practical WSNs-based vehicular communications.

Correlated subgraph search for multiple query graphs in graph streams

In real-world, there are many dynamic graph databases. Correlation mining is one of important analysis method in these dynamic graph databases. CGStream has been proposed to discover correlated graphs efficiently in graph streams. However, CGStream suffers from long running time for multiple queries. CGStream must perform frequent subgraph mining n times for n queries to generate candidate patterns. Many of the candidate patterns are redundant because multiple queries can have the same candidates. In this paper, we propose an efficient framework, MCGStream that supports correlated subgraph search for multiple queries in graph streams. The proposed framework generates no redundant candidate pattern for multiple queries by performing frequent subgraph mining with the global lower frequency bound. We propose the method that determines the global lower frequency bound for multiple queries. We build a correlation candidate tree to maintain the candidate patterns and their mapping to queries. Several optimization techniques are applied to the correlation candidate tree to reduce the search space. Experiments show that the proposed method efficiently processes multiple queries compared with CGStream by up to 35%.