• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.5
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• pp.885-896
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• 2014

The files in computer storages can be deleted due to unexpected failures or accidents. Some malicious users often delete data by himself for anti-forensics. If deleted files are associated with crimes or important documents in business, they should be recovered and the recovery tool is necessary. The recovery methods and tools for some filesystems such as NTFS, FAT, and EXT have been developed actively. However, there has not been any researches for recovering deleted files in XFS filesystem applied to NAS or CCTV. In addition, since the current related tools are based on the traditional signature detection methods, they have low recovery rates. Therefore, this paper suggests the recovery methods for deleted files based on metadata and signature detection in XFS filesystem, and verifies the results by conducting experiment in real environment.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1027-1041
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• 2015

Recently, there are rapidly increasing cases of APT (Advanced Persistent Threat) attacks such as Verizon(2010), Nonghyup(2011), SK Communications(2011), and 3.20 Cyber Terror(2013), which cause leak of confidential information and tremendous damage to valuable assets without being noticed. Several anomaly detection technologies were studied to defend the APT attacks, mostly focusing on detection of obvious anomalies based on known malicious codes' signature. However, they are limited in detecting APT attacks and suffering from high false-negative detection accuracy because APT attacks consistently use zero-day vulnerabilities and have long latent period. Detecting APT attacks requires long-term analysis of data from a diverse set of sources collected over the long time, real-time analysis of the ingested data, and correlation analysis of individual attacks. However, traditional security systems lack sophisticated analytic capabilities, compute power, and agility. In this paper, we propose a Fast Data based real-time abnormal behavior detection system to overcome the traditional systems' real-time processing and analysis limitation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.65-72
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• 2021

Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.599-610
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• 2021

Early detection of small concrete crack or reinforcement corrosion is necessary for Structural Health Monitoring (SHM). Global vibration based methods are advantageous over local methods because of simple equipment installation and cost efficiency. Among vibration based techniques, FRF based methods are preferred over modal based methods. In this study, a new coupled method using frequency response function (FRF) and proper orthogonal modes (POM) is proposed by using the dynamic characteristic of a damaged beam. For the numerical simulation, wave finite element (WFE), coupled with traditional finite element (FE) method is used for effectively incorporating the damage related information and faster computation. As reported in literature, hybrid combination of wave function based wave finite element method and shape function based finite element method can addresses the mid frequency modelling difficulty as it utilises the advantages of both the methods. It also reduces the dynamic matrix dimension. The algorithms are implemented on a three-dimensional reinforced concrete beam. Damage is modelled and studied for two scenarios, i.e., crack in concrete and rebar corrosion. Single and multiple damage locations with different damage length are also considered. The proposed methodology is found to be very sensitive to both single- and multiple- damage while being computationally efficient at the same time. It is observed that the detection of damage due to corrosion is more challenging than that of concrete crack. The similarity index obtained from the damage parameters shows that it can be a very effective indicator for appropriately indicating initiation of damage in concrete structure in the form of spread corrosion or invisible crack.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.667-677
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• 2012

Vulnerable web applications have been the primary method used by the attackers to spread their malware to a large number of victims. Such attacks commonly make use of malicious links to remotely execute a rather advanced malicious code. The attackers often deploy malwares that utilizes unknown vulnerabilities so-called "zero-day vulnerabilities." The existing computer vaccines are mostly signature-based and thus are effective only against known attack patterns, but not capable of detecting zero-days attacks. To mitigate such limitations of the current solutions, there have been a numerous works that takes a behavior-based approach to improve detection against unknown malwares. However, behavior-based solutions arbitrarily introduced a several limitations that made them unsuitable for real-life situations. This paper proposes an advanced web browser based malicious behavior detection system that solves the problems and limitations of the previous approaches.

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.19-21
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• 2001

Induction motors are a critical component of many industrial machines and are frequently integrated in commercial equipment. The many economical losses and the deterioration of system reliability might be caused by the failure of induction motors in industrial field. Based on the reliability and cost competitiveness of driving system(motors), the faults detection and diagnosis of system is considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses the motor's condition. The diagnostic system is constructed by using LabVIEW of National Instruments.