• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.5
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• pp.368-376
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• 2013

The importance of application traffic identification is emphasized for the efficient network management with recent rapid development of internet. In this paper, we present the application traffic identification method using the behavior based signature to improve the previous limitations. The behavior based signature is made by combining the existing various traffic features, and uses the Inter-Flow unit that is combination of the first request packet of each flow. All signatures have 100% precision when measured the accuracy of 5 applications using at home and abroad to prove the feasibility of the proposed signature.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.720-728
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• 2003

Blind signature is such a signature scheme that conceals the contents of signature itself and who is the user of the signature make user's anonymity possible. For this reason, they are used in security services such as electronic cashes and electronic votes in which the behavior of actor should not be exposed. In this paper we propose an ID-based blind signature scheme from Gap Diffie-Hellman group. Its security is based on the hardness of Computational Diffie-Hellman Problem. Proposed scheme efficiently improve against existing blind signature scheme by using two-pass protocol between two users and by reducing computation process. Therefore it can be used efficiently in wireless PKI environment.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3011-3019
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• 1999

Denial of service is about knocking off services, without permission for example through crashing the whole system. This kind of attacks are easy to launch and it is hard to protect a system against them. The basic problem is that Unix assumes that users on the system or on other systems will be well behaved. This paper analyses system-based inside denial of services attack(DoS) and system metric for performance of each machine provided. And formalize the conclusions results in ways that clearly expose the performance impact of those observations. So, we present new approach. It is detecting DoS attack using performance signature for system and program behavior. We present new approach. It is detecting DoS attack using performance signature for system and program behavior. We believe that metric will be to guide to automated development of a program to detect the attack. As a results, we propose the AIDPS(Architecture for Intrusion Detection using Performance Signature) model to detect DoS attack using performance signature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.5
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• pp.939-946
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• 2013

This paper proposes normal behavior profiling methods for anomaly detection in IEC 61850 based substation network. Signature based security solutions, currently used primarily, are inadequate for APT attack using zero-day vulnerabilities. Recently, some researches about anomaly detection in control network are ongoing. However, there are no published result for IEC 61850 substation network. Our proposed methods includes 3-phase preprocessing for MMS/GOOSE packets and normal behavior profiling using one-class SVM algorithm. These approaches are beneficial to detect APT attacks on IEC 61850 substation network.

• Smart Media Journal
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• v.8 no.4
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• pp.25-32
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• 2019

While the development of the Internet has made information more accessible, this also has provided a variety of intrusion paths for malicious programs. Traditional Signature-based malware-detectors cannot identify new malware. Although Dynamic Analysis may analyze new malware that the Signature cannot do, it still is inefficient for detecting variants while most of the behaviors are similar. In this paper, we propose a detection method using behavioral similarity with existing malicious codes, assuming that they have parallel patterns. The proposed method is to extract the behavior targets common to variants and detect programs that have similar targets. Here, we verified behavioral similarities between variants through the conducted experiments with 1,000 malicious codes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5219-5243
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• 2019

In electronic ticketing system, the malicious behavior of scalpers damages the customer's interest and disturbs the normal order of market. In order to solve the problem of scalpers, we took two steps. Firstly, we established the electronic ticketing system based on the consortium blockchain (CB-ETS). By establishing CB-ETS, we can make the ticketing market develop better in a controlled environment and be managed by the members in the consortium blockchain. Secondly, we put forward a kind of taxation mechanism for suppressing scalpers based on CB-ETS. Together with the regulatory mechanism, our scheme can effectively reduce the scalpers' profits and further inhibit scalpers. Through the above two steps, the scheme can effectively resist the malicious behavior of scalpers. Among them, in the process of transferring tickets, we optimized the transfer mechanism to achieve a win-win situation. Finally, we analyzed the security and efficiency of our scheme. Our scheme realizes the anonymity through the mixed currency protocol based on ring signature and guarantees the unforgeability of tickets by multi-signature in the process of modifying the invalidity of tickets. It also could resist to Dos attacks and Double-Spending attacks. The efficiency analysis shows that our scheme is significantly superior to relevant works.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.173-186
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• 2011

Due to the disadvantages of signature-based computer virus detection techniques, behavior-based detection methods have developed rapidly in recent years. However, current popular behavior-based detection methods only take API call sequences as program behavior features and the difference between API calls in the detection is not taken into consideration. This paper divides virus behaviors into separate function modules by introducing DLLs into detection. APIs in different modules have different importance. DLLs and APIs are both considered program calling resources. Based on the calling relationships between DLLs and APIs, program calling resources can be pictured as a tree named program behavior resource tree. Important block structures are selected from the tree as program behavior features. Finally, a virus detection model based on behavior the resource tree is proposed and verified by experiment which provides a helpful reference to virus detection.

• Journal of KIISE
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• v.45 no.3
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• pp.294-302
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• 2018

With the emergence of the new service industry due to the development of information and communication technology, cyber space risks such as personal information infringement and industrial confidentiality leakage have diversified, and the security problem has emerged as a critical issue. In this paper, we propose a behavior-based anomaly detection method that is suitable for real-time and large-volume data analysis technology. We show that the proposed detection method is superior to existing signature security countermeasures that are based on large-capacity user log data according to in-company personal information abuse and internal information leakage. As the proposed behavior-based anomaly detection method requires a technique for processing large amounts of data, a real-time search engine is used, called Elasticsearch, which is based on an inverted index. In addition, statistical based frequency analysis and preprocessing were performed for data analysis, and the DBSCAN algorithm, which is a density based clustering method, was applied to classify abnormal data with an example for easy analysis through visualization. Unlike the existing anomaly detection system, the proposed behavior-based anomaly detection technique is promising as it enables anomaly detection analysis without the need to set the threshold value separately, and was proposed from a statistical perspective.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1170-1178
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• 2010

Recently as the number of users and application traffic is increasing on high speed network, the importance of application traffic classification is growing more and more for efficient network resource management. Although a number of methods and algorithms for traffic classification have been introduced, they have some limitations in terms of accuracy and completeness. In this paper we propose an application traffic classification based multi-level architecture which integrates several signature-based methods and behavior algorithm, and analyzes traffic using correlation among traffic flows. By strengthening the strength and making up for the weakness of individual methods we could construct a flexible and robust multi-level classification system. Also, by experiments with our campus network traffic we proved the performance and validity of the proposed mechanism.

• International Commerce and Information Review
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• v.9 no.3
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.