• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.575-585
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• 2015

The traffic classification is a preliminary and essential step for stable network service provision and efficient network resource management. However, the payload signature-based method has significant drawbacks in high-speed network environment that the processing speed is much slower than other methods such as header-based and statistical methods. In addition, as signature numbers are increasing, traffic analysis speed also declines because of signature matching method that does not consider analytic efficiency of each signature and traffic occurrence feature. In this paper, we propose a signature list reordering method in order by analytic value of each signature. When we reordered the signature list by the proposed method, we achieved about 30% improvement in speed of the traffic analysis compared with random signature list.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.555-562
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• 2009

In malware accident investigation, the most important thing is detection of malicious code. Signature based anti-virus softwares have been used in most of the accident. Malware can easily avoid signature based detection by using packing or encryption method. Because of this, packed file detection is also important. Detection methods can be divided into signature based detection and entropy based detection. Signature based detection can not detect new packing. And entropy based detection has a problem with false positive. We provides detection method using entropy statistics of entry point section and 'write' properties of essential characteristic of packed file. And then, we show packing detection tool and evaluate its performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1234-1244
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• 2009

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services and applications on Internet, which makes the need of application-level traffic classification important for the efficient management and control of network resources. Although lots of methods for traffic classification have been introduced in literature, they have some limitations to achieve an acceptable level of performance in terms of accuracy and completeness. In this paper we propose an application traffic classification method using statistic signatures, defined as a directional sequence of packet size in a flow, which is unique for each application. The statistic signatures of each application are collected by our automatic grouping and extracting mechanism which is mainly described in this paper. By matching to the statistic signatures we can easily and quickly identify the application name of traffic flows with high accuracy, which is also shown by comprehensive excrement with our campus traffic data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.4
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• pp.191-199
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• 2014

Currently, HTTP traffic has been developed rapidly due to appearance of various applications and services based web. Accordingly, HTTP Traffic classification is necessary to effective network management. Among the various signature-based method, Payload signature-based classification method is effective to analyze various aspects of HTTP traffic. However, the payload signature-based method has a significant drawback in high-speed network environment due to the slow processing speed than other classification methods such as header, statistic signature-based. Therefore, we proposed various classification method of HTTP Traffic based HTTP signatures of hierarchical structure and to improve pattern matching speed reflect the hierarchical structure features. The proposed method achieved more performance than aho-corasick to applying real campus network traffic.

• The KIPS Transactions:PartC
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• v.18C no.4
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• pp.243-250
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• 2011

Nowadays, the traffic type and behavior are extremely diverse due to the appearance of various services on Internet, which makes the need of traffic identification important for efficient operation and management of network. In recent years traffic identification methodology using statistical features of flow has been broadly studied. We also proposed a traffic identification methodology using payload size distribution in our previous work, which has a problem of low completeness. In this paper, we improved the completeness by solving the PSD conflict using IP and port. And we improved the accuracy by changing the distance measurement between flow and statistic signature from vector distance to per-packet distance. The feasibility of our methodology was proved via experimental evaluation on our campus network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.519-525
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• 2013

The traffic classification is a preliminary and essential step for stable network service provision and efficient network resource management. However, the payload signature-based method has a significant drawback in high-speed network environment that the processing speed is much slower than other method such as header-based and statistical methods. In this paper, We propose the server IP, Port cache-based traffic classification method using application traffic locality to improve the processing speed of traffic classification. The suggested method achieved about 10 folds improvement in processing speed and 10% improvement in completeness over the payload-based classification system.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.8
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• pp.335-342
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• 2013

Nowadays, the traffic type and behavior are extremely diverse due to the growth of network speed and the appearance of various services on Internet. For efficient network operation and management, the importance of application-level traffic identification is more and more increasing in the area of traffic analysis. In recent years traffic identification methodology using statistical features of traffic flow has been broadly studied. However, there are several problems to be considered in the identification methodology base on statistical features of flow to improve the analysis accuracy. In this paper, we recognize these problems by analyzing the ground-truth traffic and propose the solution of these problems. The four problems considered in this paper are the distance measurement of features, the selection of the representative value of features, the abnormal behavior of TCP sessions, and the weight assignment to the feature. The proposed solutions were verified by showing the performance improvement through experiments in campus network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.600-607
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• 2011

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic identification becomes important for the effective use of network resources. In this paper, we present an Internet application traffic identification method based on flow correlation to overcome limitation of signature-based identification methods and to improve performance (completeness) of it. The proposed method can identify unidentified flows from signature-based method using flow correlation between identified and unidentified flows. We propose four separate correlation methods such as Server-Client, Time, Host-Host, and Statistic correlation and describe a flow correlation-based identification system architecture which incorporates the four separate methods. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.7211-7218
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• 2015

Tens of thousands of malicious codes are generated on average in a day. New types of malicious codes are surging each year. Diverse methods are used to detect such codes including those based on signature, API flow, strings, etc. But most of them are limited in detecting new malicious codes due to bypass techniques. Therefore, a lot of researches have been performed for more efficient detection of malicious codes. Of them, visualization technique is one of the most actively researched areas these days. Since the method enables more intuitive recognition of malicious codes, it is useful in detecting and examining a large number of malicious codes efficiently. In this paper, we analyze the relationships between malicious codes and Native API functions. Also, by applying the word cloud with text mining technique, major Native APIs of malicious codes are visualized to assess their maliciousness. The proposed malicious code analysis method would be helpful in intuitively probing behaviors of malware.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.17-24
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• 2012

This paper suggests Effectiveness Evaluation Methods of DDoS attacks countermeasures model by simulation. According to the security objectives that are suggested by NIST(National Institute of Standards and Technology), It represents a hierarchical Effectiveness Evaluation Model. we calculated the weights of factors that security objectives, security controls, performance indicator through AHP(Analytic Hierarchy Process) analysis. Subsequently, we implemented Arena Simulation Model for the calculation of function points at the performance indicator. The detection and protection algorithm involve methods of critical-level setting, signature and anomaly(statistic) based detection techniques for Network Layer 4, 7 attacks. Proposed Effectiveness Evaluation Model can be diversely used to evaluate effectiveness of countermeasures and techniques for new security threats each organization.