• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.1
• /
• pp.79-90
• /
• 2017

If digital forensic investigators can utilize file access logs when they audit insider information leakage cases or incident cases, it would be helpful to understand user's behaviors more clearly. There are many known artifacts related to file access in MS Windows. But each of the artifacts often lacks critical information, and they are usually not preserved for enough time. So it is hard to track down what has happened in a real case. In this thesis, I suggest a method to utilize SACL(System Access Control List) which is one of the audit functions provided by MS Windows. By applying this method of strengthening the Windows's audit settings, even small organizations that cannot adopt security solutions can build better environment for conducting digital forensic when an incident occurs.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.9
• /
• pp.2442-2450
• /
• 1999

The audit logging system is to write the details of systems use and access on networks. These details are used for trailing the route, when illegal access or using system resource is occurred on networks. The logging system therefore, might be the first target of intruder. We developed the logging system which writes the information of logging and command execution on UNIX system. And we prepared the self-protecting functions of blocking intruder's attack on the logging system. They are protecting the logging process and the log file. To protect the logging process, we made it keep changing the process ID to avoid the intruder's attack. To protect the log file, we use hard link and mandatory file locking, so it can make it impossible to delete or change log file.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.772-786
• /
• 2021

The process of tracking suspicious behavior manually on a system and gathering evidence are labor-intensive, variable, and experience-dependent. The system logs are the most important sources for evidences in this process. However, in the Microsoft Windows operating system, the action events are irregular and the log structure is difficult to audit. In this paper, we propose a model that overcomes these problems and efficiently analyzes Microsoft Windows logs. The proposed model extracts lists of both common and key events from the Microsoft Windows logs to determine detailed actions. In addition, we show an approach based on the proposed model applied to track illegal file access. The proposed approach employs three-step tracking templates using Elastic Stack as well as key-event, common-event lists and identify event lists, which enables visualization of the data for analysis. Using the three-step model, analysts can adjust the depth of their analysis.

• The KIPS Transactions:PartD
• /
• v.10D no.5
• /
• pp.773-780
• /
• 2003

SecuROS(Secure & Reliable Operating System) prevents and blocks possible system cracking by implementing additional security functions in FreeBSD 4.3 operating system (OS) kernel, including access control, user authentication, audit trail, encryption file system and trusted channel. This paper describes access control technique, which is one of core technologies of SecuROS, introduces the implementations of DAC, MAC and RBAC, all of which are corresponding access control policies, and show security and results of performance measurement on the basis of application of access control policies. Finally, security and performance between conventional OS environment and environment adopting access control policy is described.

• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.397-406
• /
• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.