• Convergence Security Journal
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• v.21 no.3
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• pp.31-38
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• 2021

Tor, known as Onion Router, guarantees strong anonymity. For this reason, Tor is actively used not only for criminal activities but also for hacking attempts such as rapid port scan and the ex-filtration of stolen credentials. Therefore, fast and accurate detection of Tor traffic is critical to prevent the crime attempts in advance and secure the organization's information system. This paper proposes a novel classification model that can detect Tor traffic and classify the traffic types based on CNN(Convolutional Neural Network). We use UNB Tor 2016 Dataset to evaluate the performance of our model. The experimental results show that the accuracy is 99.98% and 97.27% in binary classification and multiclass classification respectively.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.73-86
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• 2010

In proportion to the rapid increase in the number of Web users, web attack techniques are also getting more sophisticated. Therefore, we need not only to detect Web attack based on the log analysis but also to extract web attack events from audit information such as Web firewall, Web IDS and system logs for detecting abnormal Web behaviors. In this paper, web attack detection system was designed and implemented based on integrated web audit data for detecting diverse web attack by generating integrated log information generated from W3C form of IIS log and web firewall/IDS log. The proposed system analyzes multiple web sessions and determines its correlation between the sessions and web attack efficiently. Therefore, proposed system has advantages on extracting the latest web attack events efficiently by designing and implementing the multiple web session and log correlation analysis actively.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.2
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• pp.87-94
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• 2017

Microsoft Office is an office suite of applications developed by Microsoft. Recently users with malicious intent customize Office files as a container of the Malware because MS Office is most commonly used word processing program. To attack target system, many of malicious office files using a variety of skills and techniques like macro function, hiding shell code inside unused area, etc. And, people usually use two techniques to detect these kinds of malware. These are Signature-based detection and Sandbox. However, there is some limits to what it can afford because of the increasing complexity of malwares. Therefore, this paper propose methods to detect malicious MS office files in Computer forensics' way. We checked Macros and potential problem area with structural analysis of the MS Office file for this purpose.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.65-75
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• 2009

The development of IT technology, Internet popularity is increasing geometrically. However, as its side effect, the intrusion behaviors such as information leakage for key system and infringement of computation network etc are also increasing fast. The attack traffic detection method which is suggested in this study utilizes the Snort, traditional NIDS, filters the packet with false positive among the detected attack traffics using Nmap information. Then, it performs the secondary filtering using nessus vulnerability information and finally performs correlation analysis considering appropriateness of management system, severity of signature and security hole so that it could reduce false positive alarm message as well as minimize the errors from false positive and as a result, it raised the overall attack detection results.

• Journal of KIISE
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• v.43 no.3
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• pp.289-295
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• 2016

Malware authors spread malware variants in order to evade detection. It's hard to detect malware variants using static analysis. Therefore dynamic analysis based on API call information is necessary. In this paper, we proposed a malware family recommendation method to assist malware analysts in classifying malware variants. Our proposed method extract API call information of malware families by dynamic analysis. Then the multiple sequence alignment technique was applied to the extracted API call information. A signature of each family was extracted from the alignment results. By the similarity of the extracted signatures, our proposed method recommends three family candidates for unknown malware. We also measured the accuracy of our proposed method in an experiment using real malware samples.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.357-367
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• 2018

As information technology of modern society develops, various malicious codes with the purpose of seizing or destroying important system information are developing together. Among them, ransomware is a typical malicious code that prevents access to user's resources. Although researches on detecting ransomware performing encryption have been conducted a lot in recent years, no additional methods have been proposed to recover damaged files after an attack. Also, because the similarity comparison technique was used without considering the repeated encryption, it is highly likely to be recognized as a normal behavior. Therefore, this paper implements a filter driver to control the file system and performs a similarity comparison method that is verified based on the analysis of the encryption pattern of the ransomware. We propose a system to detect the malicious process of the accessed process and recover the damaged file based on the cloud storage.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.648-649
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• 2013

최근 컴퓨터 시스템과 네트워크의 발전으로 인해 다양한 악성코드들이 네트워크 상에서 유포되고 있다. 이러한 악성코드들을 빠른 시간에 분석해서 악성 여부와 그 행위를 파악하기 위해 많은 자동 분석 시스템들이 개발되어 사용되고 있지만, 이들 대부분이 가상머신 기반으로 동작하기 때문에 최근의 악성코드들은 가상머신 환경을 탐지하여 가상머신 상에서는 본연의 기능을 수행하지 않도록 제작되어 있다. 본 논문에서는 기존의 악성코드 자동 분석 시스템이 가상머신을 기반으로 하는 것을 개선해서 실제 컴퓨터를 사용해서 자동 분석할 수 있는 시스템을 제안한다.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1185-1195
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• 2014

Domestic and international CERTs are carrying out security monitoring and response services based on security devices for intrusion incident prevention and damage minimization of the organizations. However, the security monitoring and response service has a fatal limitation in that it is unable to detect unknown attacks that are not matched to the predefined signatures. In recent, many approaches have adopted the darknet technique in order to overcome the limitation. Since the darknet means a set of unused IP addresses, no real systems connected to the darknet. Thus, all the incoming traffic to the darknet can be regarded as attack activities. In this paper, we present a collection and analysis method of malicious URLs based on darkent traffic for advanced security monitoring and response service. The proposed method prepared 8,192 darknet space and extracted all of URLs from the darknet traffic, and carried out in-depth analysis for the extracted URLs. The analysis results can contribute to the emergence response of large-scale cyber threats and it is able to improve the performance of the security monitoring and response if we apply the malicious URLs into the security devices, DNS sinkhole service, etc.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.