• Journal of KIISE:Databases
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• v.36 no.2
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• pp.63-72
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• 2009

Digital forensics refers to finding electronic evidences related to crimes. As cyber crimes are increasing daily, digital forensics for finding electronic evidences is also becoming important. At present, various aspects of digital forensics have being researched including the overall process model and analysis techniques such as network forensics, system forensics and database forensics for digital forensics. Regarding database forensics, only analysis techniques dependent on specific vendors have been suggested. And general process models and analysis techniques which can be used in various databases have not been studied. This paper proposes an integrated process model and analysis technique for database forensics. The proposed database forensics model (DFM) allows us to solve problems and analyze databases according to the situation and purpose, and to use a standard model and techniques for various database analyses. In order to test our model(DFM), we applied it to various database analyses. And we confirmed the results of our experiment that it can be applicable to acquisition in the scene as well as analysis of data relationships.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.123-128
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• 2020

As the use of digital devices is becoming more commonplace, digital forensics techniques recover data to collect physical evidence during the investigation. Among them, the file forensics technique recovers deleted files, therefore, it can recover the database by recovering all files which compose the database itself. However, if the record is deleted from the database, the modified record contents will not be restored even if the file is recovered. For this reason, the database forensics technique is required to recover deleted records. Database forensics obtains metadata from database configuration files and recovers deleted records from data files. However, record recovery is difficult if database metadata such as block size cannot be obtained from the database. In this paper, we propose three methods for obtaining block size, which is database metadata. The first method uses the maximum size of free space in the block, and the second method uses the location where the block appears. The third method improves the second method to find the block size faster. The experimental results show that three methods can correctly find the block size of three DBMSes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1557-1569
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• 2017

In recent times, the Internet of Things (IoT) has rapidly emerged as one of the most influential information and communication technologies (ICT). The various constituents of the IoT together offer novel technological opportunities by facilitating the so-called "hyper-connected world." The fundamental tasks that need to be performed to provide such a function involve the transceiving, storing, and analyzing of digital data. However, it is challenging to handle voluminous data with IoT devices because such devices generally lack sufficient computational capability. In this study, we examine the IoT from the perspective of security and digital forensics. SQLite is a light-weight database management system (DBMS) used in many IoT applications that stores private information. This information can be used in digital forensics as evidence. However, it is difficult to obtain critical evidence from IoT devices because the digital data stored in these devices is frequently deleted or updated. To address this issue, we propose Schema Pattern-based Recovery (SPaRe), an SQLite recovery scheme that leverages the pattern of a database schema. In particular, SPaRe exhaustively explores an SQLite database file and identifies all schematic patterns of a database record. We implemented SPaRe on an iPhone 6 running iOS 7 in order to test its performance. The results confirmed that SPaRe recovers an SQLite record at a high recovery rate.

• KSCI Review
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• v.14 no.2
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• pp.225-234
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• 2006

Computer Forensics functions by defending the effects and extracting the evidence of the side effects for production at the court. Has the faultlessness of the digital evidence been compromised during the investigation, a critical evidence may be denied or not even be presented at the trial. The presented monograph will deliberate the faultlessness-establishing chain procedures in disk forensics. system forensics, network forensics, mobile forensics and database forensics. Once the faultlessness is established by the methods proposed, the products of investigation will be adopted as a leading evidence. Moreover, the issues and alternatives in the reality of digital investigation are presented along with the actual computer forensics cases, hopefully contributing to the advances in computer digital forensics and the field research of information security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.123-134
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• 2014

As the data gets bigger recently, the demand for relational database management system (RDBMS) and NoSQL DBMS to process big data has been increased consistently. The digital forensic investigation method for RDBMS has been studied actively, but that for NoSQL DBMS, which is popularly used nowadays, has almost no research. This paper proposes the digital forensic investigation process and method for MongoDB, the most popularly used among NoSQL DBMS.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.5
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• pp.117-126
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• 2016

Recently, increasing utilization of Big Data or Social Network Service involves the increases in demand for NoSQL Database that overcomes the limitations of existing relational database. A forensic examination of Relational Database has steadily researched in terms of Digital Forensics. In contrast, the forensic examination of NoSQL Database is rarely studied. In this paper, We introduce Redis (which is) based on Key-Value Store NoSQL Database, and research the collection and analysis of forensic artifacts then propose recovery method of deleted data. Also we developed a recovery tool, it will be verified our recovery algorithm.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.175-184
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• 2006

Computer Forensics functions by defending the effects and extracting the evidence of the side effects for production at the court. Has the faultlessness of the digital evidence been compromised during the investigation, a critical evidence may be denied or not even be presented at the trial. The presented monograph will deliberate the faultlessness-establishing chain procedures in disk forensics, system forensics, network forensics, mobile forensics and database forensics. Once the faultlessness is established by the methods proposed, the products of investigation will be adopted as a leading evidence. Moreover, the issues and alternatives in the reality of digital investigation are presented along with the actual computer forensics cases, hopefully contributing to the advances in computer digital forensics and the field research of information security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.405-415
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• 2020

With the increase in smartphone user, mobile forensics has become an essential element in modern digital forensic investigation. Mobile messenger data is very important data in mobile forensics because it can acquire information such as user's life pattern and mental state. In order to analyze messenger data, a decryption technique of an encrypted messenger data is required. Since most messengers provide a message deleting function, a technique for recovering deleted messages is required. WeChat Messenger, a messenger used by about 1 billion people around the world, uses IMEI (International Mobile Equipment Identity) information to encrypt data and provides message deletion function. In this paper, we propose a data decryption method in the absence of IMEI information and propose a method for recovering deleted messages using FTS (Full Text Search) database created for full-text search function of SQLite database.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.184-186
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• 2022

Recently, as interest in personal information protection has increased, various apps for personal information protection have emerged. These apps protect data in various formats, such as photos, videos, and documents containing personal information, using encryption and hide functions. These apps can have a positive effect on personal information protection, but in digital forensics, they act as anti-forensic because they can be difficult to analyze data during the investigation process. In this paper, finds out PIN, an access control function, through reverse engineering on Calculator - photo vault, one of the personal information protection apps, and files such as photos and documents to which encryption and hide were applied. In addition, the vulnerability to this app was analyzed by research decryption for database files where logs for encrypted and hide files are stored.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.12
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• pp.487-496
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• 2017

In MySQL InnoDB, there are two ways of storing data. One is to create a separate tablespace for each table and store it separately. Another is to store all table and index information in a single system tablespace. You can use this information to recover deleted data from the record. However, in most of the current database forensic studies, the former is actively researched and its structure is analyzed, whereas the latter is not enough to be used for forensics. Both approaches must be analyzed in terms of database forensics because their storage structures are different from each other. In this paper, we propose a method for recovering deleted records in a method of storing records in IBDATA file, which is a single system tablespace. First, we analyze the IBDATA file to reveal its structure. And introduce delete record recovery algorithm which extended to an unallocated page area which was not considered in the past. In addition, we show that the recovery rate is improved up to 68% compared with the existing method through verification using real data by implementing the algorithm as a tool.