• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.81-87
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• 2015

Recently, with the development of the ICT environment, the use of the software is growing rapidly. And the number of the web server software used with a variety of users is also growing. However, There are also various damage cases increased due to a software security vulnerability as software usage is increasing. Especially web shell hacking which abuses software vulnerabilities accounts for a very high percentage. These web server environment damage can induce primary damage such like homepage modification for malware spreading and secondary damage such like privacy. Source code weaknesses checking system is needed during software development stage and operation stage in real-time to prevent software vulnerabilities. Also the system which can detect and determine web shell from checked code in real time is needed. Therefore, in this paper, we propose the system improving security for web server by detecting web shell attacks which are invisible to existing detection method such as Firewall, IDS/IPS, Web Firewall, Anti-Virus, etc. while satisfying existing secure coding guidelines from development stage to operation stage.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.682-684
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• 2003

With the explosive rapid expansion of computer using during the past few years, security has become a crucial issue for modem computer systems. Today, there are many intrusion detection systems (IDS) on the Internet. A variety of intrusion detection techniques and tools exist in the computer security community such as enterprise security management system (ESM) and system integrity checking tools. However, there is a potential problem involved with intrusion detection systems that are installed locally on the machines to be monitored. If the system being monitored is compromised, it is quite likely that the intruder will after the system logs and the intrusion logs while the intrusion remains undetected. In this project KIT-I, we adopt remote logging server (RLS) mechanism, which is used to backup the log files to the server. Taking into account security, we make use of the function of SSL of Java and certificate authority (CA) based key management. Furthermore, Support Vector Machine (SVM) is applied in our project to detect the intrusion activities.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.481-500
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• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.928-931
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• 2002

Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.

• Journal of Astronomy and Space Sciences
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• v.36 no.4
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• pp.283-292
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• 2019

The purpose of this study was to develop a period analysis algorithm for detecting new variable stars in the time-series data observed by charge coupled device (CCD). We used the data from a variable star monitoring program of the CBNUO. The R filter data of some magnetic cataclysmic variables observed for more than 20 days were chosen to achieve good statistical results. World Coordinate System (WCS) Tools was used to correct the rotation of the observed images and assign the same IDs to the stars included in the analyzed areas. The developed algorithm was applied to the data of DO Dra, TT Ari, RXSJ1803, and MU Cam. In these fields, we found 13 variable stars, five of which were new variable stars not previously reported. Our period analysis algorithm were tested in the case of observation data mixed with various fields of view because the observations were carried with 2K CCD as well as 4K CCD at the CBNUO. Our results show that variable stars can be detected using our algorithm even with observational data for which the field of view has changed. Our algorithm is useful to detect new variable stars and analyze them based on existing time-series data. The developed algorithm can play an important role as a recycling technique for used data

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.3
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• pp.129-139
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• 2002

As internet expands, the possibility of attack through the network is increasing. So we need the technology which can detect the attack to the system or the network spontaneously. The purpose of this paper proposes the system to detect intrusion automatically using the Adaptive Resonance Theory2(ART2) which is one of artificial neural network The parameters of the system was tunned by ART2 algorithm using a lot of normal packets and various attack packets which were intentionally generated by attack tools. The results were compared and analyzed with conventional methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.5
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• pp.105-117
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• 2001

As we live in the 21st century, so called the "Information Age", network has become a basic establishment. However, we have found the different face that it also has been used as a tool of a unauthorized outflow and destruction of information. In recent years, beginner could easily get a hacking and weakness reference tools from internet. The menace of the situation has increased; the intellectual diverse offensive technique has become increasingly dangerous. The purpose of the thesis is to detect a abnormal packet for networking offense. In order to detect the packet, it gathers the packets and create inspection information that tells abnormality by using probability of special quality, then decision of intrusion is made by using a neural network.l network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.591-603
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• 2018

Windows Event Log is a format that records system log in Windows operating system and methodically manages information about system operation. An event can be caused by system itself or by user's specific actions, and some event logs can be used for corporate security audits, malware detection and so on. In this paper, we choose actions related to corporate security audit and malware detection (External storage connection, Application install, Shared folder usage, Printer usage, Remote connection/disconnection, File/Registry manipulation, Process creation, DNS query, Windows service, PC startup/shutdown, Log on/off, Power saving mode, Network connection/disconnection, Event log deletion and System time change), which can be detected through event log analysis and classify event IDs that occur in each situation. Also, the existing event log tools only include functions related to the EVTX file parse and it is difficult to track user's behavior when used in a forensic investigation. So we implemented new analysis tool in this study which parses EVTX files and user behaviors.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1037-1044
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• 2006

It's difficult to check cyber attacks and the performance of a security system in a real large-scale network. Generally, a new security system or the effect of a new security attack are checked by simulation. We use SSFNet to simulate our security system and cyber attack. SSFNet is an event-driven simulation tools based on process, which has a strength to be capable of expressing a large-scale network. But it doesn't offer any API's which can manipulate not only the related function of security but also the packet. In this paper, we developed a firewall and IPS class, used for a security system, and added to them components of SSFNet. The firewall is modelled a security system based on packet filtering. We checked the function of the firewall and the IPS with network modelled as using our SSFNet. The firewall blocks packets through rules of an address and port of packets. The result of this simulation shows that we can check a status of packets through a log screen of IPS installed in a router and confirm abnormal packet to be dropped.