• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.701-713
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• 2021

Race Condition is a vulnerability in which two or more processes input or manipulate a common resource at the same time, resulting in unintended results. This vulnerability can lead to problems such as denial of service, elevation of privilege. When a vulnerability occurs in software, the relevant information is documented, but often the cause of the vulnerability or the source code is not disclosed. In this case, analysis at the binary level is necessary to detect the vulnerability. This paper aims to detect the Time-Of-Check Time-Of-Use (TOCTOU) Race Condition vulnerability of UNIX kernel-based File System at the binary level. So far, various detection techniques of static/dynamic analysis techniques have been studied for the vulnerability. Existing vulnerability detection tools using static analysis detect through source code analysis, and there are currently few studies conducted at the binary level. In this paper, we propose a method for detecting TOCTOU Race Condition in File System based on Control Flow Graph and Call Graph through Binary Analysis Platform (BAP), a binary static analysis tool.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.137-152
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• 2018

It is a measure to overcome limitations that occur in the activity of detecting and blocking abnormal information leakage activity by collecting the activity log generated by the security solution to detect the leakage of existing financial information and analyzing it by pattern analysis. First, it monitors real-time execution programs in PC that are used as information leakage path (read from the outside, save to the outside, transfer to the outside, etc.) in the PC. Second, it determines whether it is a normal controlled exception control circumvention by interacting with the related security control process at the time the program is executed. Finally, we propose a risk management model that can control the risk of financial information leakage through the process procedure created on the basis of scenario.

• 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.

• Smart Media Journal
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• v.8 no.1
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• pp.19-26
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• 2019

At the opening ceremony of 2018 Winter Olympics in PyeongChang, an unknown cyber-attack occurred. The malicious code used in the attack is based on in-memory malware, which differs from other malicious code in its concealed location and is spreading rapidly to be found in more than 140 banks, telecommunications and government agencies. In-memory malware accounts for more than 15% of all malicious codes, and it does not store its own information in a non-volatile storage device such as a disk but resides in a RAM, a volatile storage device and penetrates into well-known processes (explorer.exe, iexplore.exe, javaw.exe). Such characteristics make it difficult to analyze it. The most recently released in-memory malicious code bypasses the endpoint protection and detection tools and hides from the user recognition. In this paper, we propose a method to efficiently extract the payload by unpacking injection through IDA Pro debugger for Dorkbot and Erger, which are in-memory malicious codes.

• Information Systems Review
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• v.22 no.4
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• pp.135-161
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• 2020

With the growth of blockchain and cryptocurrency-related markets, cryptocurrency exchanges are growing as a new industry. However, as the legal and regulatory definitions of cryptocurrencies are still in progress, unlike existing industrial groups, they are not under the supervision of regulatory agencies. As a result, users (i.e., cryptocurrency investors) have suffered two types of damage that could occur from hacking and other accidents on the exchanges. One type of the damage is the loss of assets caused by the extortion of personal information or account and the other is the damage from users who might be involved in external frauds. Both are analyzed in comparison with existing operators whose functions are like the exchanges. The results of this study show that membership (KYC: Know Your Client), log-in, and additional authentication in transactions are on the similar level to those of the operators while the fraud detection system (FDS) and anti-money laundering (AML) of fiat currencies and cryptocurrencies need rapid improvement.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.71-83
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• 2017

In recent years, by utilizing the greatest strengths of process mining, the various research activities have been actively progressed to use auditing work of business organization. On the other hand, there is insufficient research on systematic and efficient analysis of massive data generated under big data environment using process mining, and proactive monitoring of risk management from audit side, which is one of important management activities of corporate organization. In this study, we intend to realize Hadoop-based internal audit integrated real-time monitoring system in order to detect the abnormal symptoms in prevent accidents in advance. Through the integrated real-time monitoring system for purchasing audit, we intend to realize strengthen the delivery management of purchasing materials ordered, reduce cost of purchase, manage competitive companies, prevent fraud, comply with regulations, and adhere to internal control accounting system. As a result, we can provide information that can be immediately executed due to enhanced purchase audit integrated real-time monitoring by analyzing data efficiently using process mining via Hadoop-based systems. From an integrated viewpoint, it is possible to manage the business status, by processing a large amount of work at a high speed faster than the continuous monitoring, the effectiveness of the quality improvement of the purchase audit and the innovation of the purchase process appears.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.178-192
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• 2006

The buffer overflow attack is the single most dominant and lethal form of security exploits as evidenced by recent worm outbreaks such as Code Red and SQL Stammer. In this paper, we propose microarchitectural techniques that can detect and recover from such malicious code attacks. The idea is that the buffer overflow attacks usually exhibit abnormal behaviors in the system. This kind of unusual signs can be easily detected by checking the safety of memory references at runtime, avoiding the potential data or control corruptions made by such attacks. Both the hardware cost and the performance penalty of enforcing the safety guards are negligible. In addition, we propose a more aggressive technique called corruption recovery buffer (CRB), which can further increase the level of security. Combined with the safety guards, the CRB can be used to save suspicious writes made by an attack and can restore the original architecture state before the attack. By performing detailed execution-driven simulations on the programs selected from SPEC CPU2000 benchmark, we evaluate the effectiveness of the proposed microarchitectural techniques. Experimental data shows that enforcing a single safety guard can reduce the number of system failures substantially by protecting the stack against return address corruptions made by the attacks. Furthermore, a small 1KB CRB can nullify additional data corruptions made by stack smashing attacks with only less than 2% performance penalty.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.369-375
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• 2016

Most companies are willing to spend money on security systems such as DRM, Mail filtering, DLP, USB blocking, etc., for data leakage prevention. However, in many cases, it is difficult that legal team take action for data case because usually the company recognized that after the employee had left. Therefore perceiving one's resignation before the action and building up adequate response process are very important. Throughout analyzing DRM log which records every single file's changes related with user's behavior, the company can predict one's resignation and prevent data leakage before those happen. This study suggests how to prevent for the damage from leaked confidential information throughout building the DRM monitoring process which can predict employee's resignation.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.238-240
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• 2006

네트워크상에서 발생하는 다양한 형태의 대량의 데이터를 정확하고 효율적으로 분석하기 위해 설계되고 있는 마이닝 시스템들은 목표지향적으로 훈련데이터들을 어떻게 구축하여 다룰 것인지에 대한 문제보다는 대부분 얼마나 많은 데이터 마이닝 기법을 지원하고 이를 적용할 수 있는지 등의 기법에 초점을 두고 있다. 따라서, 점점 더 에이전트화, 분산화, 자동화 및 은닉화 되는 최근의 보안공격기법을 정확하게 탐지하기 위한 방법은 미흡한 실정이다. 본 연구에서는 유비쿼터스 환경 내에서 발생 가능한 문제 중 복잡하고 지능화된 침입패턴의 탐지를 위해 데이터 마이닝 기법과 결함허용방법을 이용하는 개선된 학습알고리즘과 후처리 방법에 의한 RTPID(Refinement Training and Post-processing for Intrusion Detection)시스템을 제안한다. 본 논문에서의 RTPID 시스템은 active learning과 post-processing을 이용하여, 네트워크 내에서 발생 가능한 침입형태들을 정확하고 효율적으로 다루어 분석하고 있다. 이는 기법에만 초점을 맞춘 기존의 데이터마이닝 분석을 개선하고 있으며, 특히 제안된 분석 프로세스를 진행하는 동안 능동학습방법의 장점을 수용하여 학습효과는 높이며 비용을 감소시킬 수 있는 자가학습방법(self learning)방법의 효과를 기대할 수 있다. 이는 관리자의 개입을 최소화하는 학습방법이면서 동시에 False Positive와 False Negative 의 오류를 매우 효율적으로 개선하는 방법으로 기대된다. 본 논문의 제안방법은 분석도구나 시스템에 의존하지 않기 때문에, 유사한 문제를 안고 있는 여러 분야의 네트웍 환경에 적용될 수 있다.더욱 높은성능을 가짐을 알 수 있다.의 각 노드의 전력이 위험할 때 에러 패킷을 발생하는 기법을 추가하였다. NS-2 시뮬레이터를 이용하여 실험을 한 결과, 제안한 기법이 AOMDV에 비해 경로 탐색 횟수가 최대 36.57% 까지 감소되었음을 알 수 있었다.의 작용보다 더 강력함을 시사하고 있다.TEX>로 최고값을 나타내었으며 그 후 감소하여 담금 10일에는 $1.61{\sim}2.34%$였다. 시험구간에는 KKR, SKR이 비교적 높은 값을 나타내었다. 무기질 함량은 발효기간이 경과할수록 증하였고 Ca는 $2.95{\sim}36.76$, Cu는 $0.01{\sim}0.14$, Fe는 $0.71{\sim}3.23$, K는 $110.89{\sim}517.33$, Mg는 $34.78{\sim}122.40$, Mn은 $0.56{\sim}5.98$, Na는 $0.19{\sim}14.36$, Zn은 $0.90{\sim}5.71ppm$을 나타내었으며, 시험구별로 보면 WNR, BNR구가 Na만 제외한 다른 무기성분 함량이 가장 높았다.O to reduce I/O cost by reusing data already present in the memory of other nodes. Finally, chunking and on-line compression mechanisms are included in both models. We demonstrate that we can obtain significantly high-performanc

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.217-225
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• 2014

Analysis of social media such as Twitter can yield interesting perspectives to understanding human behavior, detecting hot issues, identifying influential people, or discovering a group and community. However, it is difficult to gather the data relevant to specific topics due to the main characteristics of social media data; data is large, noisy, and dynamic. This paper proposes a new algorithm that dynamically selects the seed nodes to efficiently collect tweets relevant to topics. The algorithm utilizes attributes of users to evaluate the user influence, and dynamically selects the seed nodes during the collection process. We evaluate the proposed algorithm with real tweet data, and get satisfactory performance results.