• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.95-101
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• 2019

Entering the era of the 4^th Industrial Revolution and the Internet of Things, various services are growing rapidly, and various researches are actively underway. Among them, research on abnormal behaviors on various devices that are being used in the IoT is being conducted. In a hyper-connected society, the damage caused by one wrong device can have a serious impact on the various connected systems. In this paper, We propose a technique to cope with the problem that the threats caused by various abnormal behaviors such as anti-debugging scheme, anomalous process detection method and back door detection method on how to increase the safety of the device and how to use the device and service safely in such IoT environment.

• Convergence Security Journal
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• v.14 no.7
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• pp.17-22
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• 2014

Wireless Ad hoc Network is threatened from many types of attacks because of its open structure, dynamic topology and the absence of infrastructure. Attacks by malicious nodes inside the network destroy communication path and discard packet. The damage is quite large and detecting attacks are difficult. In this paper, we proposed attack detection technique using secure authentication infrastructure for efficient detection and prevention of internal attack nodes. Cluster structure is used in the proposed method so that each nodes act as a certificate authority and the public key is issued in cluster head through trust evaluation of nodes. Symmetric Key is shared for integrity of data between the nodes and the structure which adds authentication message to the RREQ packet is used. ns-2 simulator is used to evaluate performance of proposed method and excellent performance can be performed through the experiment.

• Environmental Mutagens and Carcinogens
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• v.22 no.2
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• pp.90-96
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• 2002

Benzene is a widespread human carcinogen, inducing leukemia and hematotoxicity. Exposure to benzene metabolites has been shown to cause genetic damage, including aneusomy and chromosome aberrations. Fluorescence in situ hybridization(FISH) procedure was used to determine if the benzene metabolite, 1, 2, 4-benzenetriol(BT), hydroquinone(HQ) and trans, trans-muconic acid(t,t-MA) induced specific chromosomal change in HL-60 cells. Treatment with BT, HQ and t,t-MA resulted in the induction of monosomy 8 and 21 in HL-60 cells in a dose-dependent manner. All of these metabolites also induced trisomy 8 and 21, but no correlation between frequencies of trisomy and concentration was found. Translocations between chromosome 8 and another unidentified chromosome ［t(8:\ulcorner)］, and between chromosome 21 and another unidentified chromosome ［t(8:21)］ were found. However, translocation between chromosome 8 and 21 ［t(8:21)］ was not found. Results indicate that the benzene metabolites, BT, HQ and t,t-MA, induce chromosome specific numerical and structural aberrations, and the fluorescence in situ hybridization (FISH) approach may be a useful and powerful technique for detection of aneuploidy.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.55-61
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• 2009

The wall thinning defect of nuclear power pipe is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid. This type of defect becomes the cause of damage or destruction of in carbon steel pipes. Therefore, it is very important to measure defect which is existed not only on the welding part but also on the whole field of pipe. This study use dual-beam Shearography, which can measure the out-of-plane deformation and the in-plane deformation by using another illuminated laser beam and simple image processing technique. And this study proposes Infrared thermography, which is a two-dimensional non-contact nondestructive evaluation that can detect internal defects from the thermal distribution by the inspection of infrared light radiated from the object surface. In this paper, defect of nuclear power pipe were, measured using dual-beam shearography and infrared thermography, quantitatively evaluated by the analysis of phase map and thermal image pattern.

• Smart Structures and Systems
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• v.19 no.4
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• pp.371-382
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• 2017

Concrete filled steel tubes are extensively applied in engineering structures due to their resistance to high tensile and compressive load and convenience in construction. But one major flaw, their vulnerability to environmental attack, can severely reduce the strength and life of these structures. Degradation due to corrosion of steel confining the concrete is one of the major durability problems faced by civil engineers to maintain these structures. The problem accelerates as inner surface of steel tube is in contact with concrete which serves as electrolyte. If it remains unnoticed, it further accelerates and can be catastrophic. This paper discusses a non-destructive degradation monitoring technique for early detection corrosion in steel tubes in CFST members. Due to corrosion, damage in the form of debonding and pitting occurs in steel sections. Guided ultrasonic waves have been used as a feasible and attractive solution for the detection and monitoring of corrosion damages in CFST sections. Guided waves have been utilized to monitor the effect of notch and debond defects in concrete filled steel tubes simulating pitting and delamination of steel tubes from surrounding concrete caused by corrosion. Pulse transmission has been used to monitor the healthy and simulated damaged specimens. A methodology is developed and successfully applied for the monitoring of concrete filled steel tubular sections undergoing accelerated chloride corrosion. The ultrasonic signals efficiently narrate the state of steel tube undergoing corrosion.

• Steel and Composite Structures
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• v.31 no.3
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• pp.233-242
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• 2019

Early detection of damage bonding state such as insufficient bonding strength and interface partial contact defect for the explosive clad pipe is crucial in order to avoid sudden failure and even catastrophic accidents. A generalized and efficient model of the explosive clad pipe can reveal the relationship between bonding state and vibration characteristics, and provide foundations and priory knowledge for bonding state detection by signal processing technique. In this paper, the slender explosive clad pipe is regarded as two parallel elastic beams continuously joined by an elastic layer, and the elastic layer is capable to describe the non-uniform bonding state. By taking the characteristic beam modal functions as the admissible functions, the Rayleigh-Ritz method is employed to derive the dynamic model which enables one to consider inhomogeneous system and any boundary conditions. Then, the proposed model is validated by both numerical results and experiment. Parametric studies are carried out to investigate the effects of bonding strength and the length of partial contact defect on the natural frequency and forced response of the explosive clad pipe. A potential method for identifying the bonding quality of the explosive clad pipe is also discussed in this paper.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• Composites Research
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• v.16 no.6
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• pp.33-40
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• 2003

The initial thermal degradation of polymer matrix composite is not observed easily. At the beginning of thermal degradation of polymer matrix composites, phase transformation such as chain scission, oxidation occur, and then micro delamination is produced in matrix and interface between matrix and fiber before blistering. Initial heat damage deteriorate mechanical properties of composites. We presented the detection method of the initial heat damage of composites conveniently using ultrasonic technique. Absorption coefficient and material velocity was measured with thermal degradation and degree of cure. The more thermal degradation was progressed, the more absorption coefficient was increased. When the cure temperature is more high, the absorption coefficient of cured composite is increased and material velocity is decreased. We concluded that cure temperature is more high, the defects such as void is increased and molecular structure cured at high temperature has cross-linking structure which is more absorb the ultrasonic waves.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4662-4679
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• 2015

Currently, the leakage of internal information has emerged as one of the most significant security concerns in enterprise computing environments. Especially, damage due to internal information leakage by insiders is more serious than that by outsiders because insiders have considerable knowledge of the system's identification and password (ID&P/W), the security system, and the main location of sensitive data. Therefore, many security companies are developing internal data leakage prevention techniques such as data leakage protection (DLP), digital right management (DRM), and system access control, etc. However, these techniques cannot effectively block the leakage of internal information by insiders who have a legitimate access authorization. The security system does not easily detect cases which a legitimate insider changes, deletes, and leaks data stored on the server. Therefore, we focused on the insider as the detection target to address this security weakness. In other words, we switched the detection target from objects (internal information) to subjects (insiders). We concentrated on biometrics signals change when an insider conducts abnormal behavior. When insiders attempt to leak internal information, they appear to display abnormal emotional conditions due to tension, agitation, and anxiety, etc. These conditions can be detected by the changes of biometrics signals such as pulse, temperature, and skin conductivity, etc. We carried out experiments in two ways in order to verify the effectiveness of the emotional recognition technology based on biometrics signals. We analyzed the possibility of internal information leakage detection using an emotional recognition technology based on biometrics signals through experiments.