• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3384-3400
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• 2021

With the advent of the data era, people pay much more attention to data corruption. Aiming at the problem that the majority of existing schemes do not support corruption detection of ciphertext data stored in cloud environment, this paper proposes a data corruption detection scheme based on ciphertexts in cloud environment (DCDC). The scheme is based on the anomaly detection method of Gaussian model. Combined with related statistics knowledge and cryptography knowledge, the encrypted detection index for data corruption and corruption detection threshold for each type of data are constructed in the scheme according to the data labels; moreover, the detection token for data corruption is generated for the data to be detected according to the data labels, and the corruption detection of ciphertext data in cloud storage is realized through corresponding tokens. Security analysis shows that the algorithms in the scheme are semantically secure. Efficiency analysis and simulation results reveal that the scheme shows low computational cost and good application prospect.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.149-156
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• 2023

OOXML-based MS-Office digital files are extensively utilized by businesses and organizations worldwide. However, OOXML-based MS-Office digital files are vulnerable to forgery and corruption attack by including hidden suspicious information, which can lead to activating malware or shell code being hidden in the file. Such malicious code can cause a computer system to malfunction or become infected with ransomware. To prevent such attacks, it is necessary to analyze and detect the corruption of OOXML-based MS-Office files. In this paper, we examine the weaknesses of the existing OOXML-based MS-Office file structure and analyzes how concealment and forgery are performed on MS-Office digital files. As a result, we propose a system to detect hidden data effectively and proactively respond to ransomware attacks exploiting MS-Office security vulnerabilities. Proposed system is designed to provide reliable and efficient detection of hidden data in OOXML-based MS-Office files, which can help organizations protect against potential security threats.

• ETRI Journal
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• v.31 no.4
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• pp.451-453
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• 2009

This letter proposes the use of vowel sound detection for voice activity detection. Vowels have distinctive spectral peaks. These are likely to remain higher than their surroundings even after severe corruption. Therefore, by developing a method of detecting the spectral peaks of vowel sounds in corrupted signals, voice activity can be detected as well even in low signal-to-noise ratio (SNR) conditions. Experimental results indicate that the proposed algorithm performs reliably under various noise and low SNR conditions. This method is suitable for mobile environments where the characteristics of noise may not be known in advance.

• Structural Engineering and Mechanics
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• v.55 no.6
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• pp.1223-1239
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• 2015

A method for structural damage identification based on Chaotic Artificial Bee Colony (CABC) algorithm is presented. ABC is a heuristic algorithm with simple structure, ease of implementation, good robustness but with slow convergence rate. To overcome the shortcoming, the tournament selection mechanism is chosen instead of the roulette mechanism and chaotic search mechanism is also introduced. Residuals of natural frequencies and modal assurance criteria (MAC) are used to establish the objective function, ABC and CABC are utilized to solve the optimization problem. Two numerical examples are studied to investigate the efficiency and correctness of the proposed method. The simulation results show that the CABC algorithm can identify the local damage better compared with ABC and other evolutionary algorithms, even with noise corruption.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1383-1391
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• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.649-664
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• 2011

Cooperative spectrum sensing (CSS) with decision fusion is considered as a key technology for tackling the challenges caused by fading/shadowing effects and noise uncertainty in spectrum sensing in cognitive radio. However, most existing solutions assume an error-free decision transmission, which is obviously not the case in realistic scenarios. This paper extends the general decision-fusion-based CSS scheme by considering the fading/shadowing effects and noise corruption in the common control channels. With this more practical model, the fusion centre first estimates the local decisions using a binary minimum error probability detector, and then combines them to get the final result. Theoretical analysis and simulation of this CSS scheme are performed over typical channels, which suggest some performance deterioration compared with the pure case that assumes an error-free decision transmission. Furthermore, the fusion strategy optimization in the proposed cooperation model is also investigated using the Bayesian criteria. The numerical results show that the total error rate of noisy CSS is higher than that of the pure case, and the optimal values of fusion parameter in the counting rule under both cases decrease as the local detection threshold increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2655-2663
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• 2011

Several algorithms have been developed to detect AF which rely either on the form of P waves or the based on the time frequency domain analysis of RR variability. However, locating the P wave fiducial point is very difficult because of the low amplitude of the P wave and the corruption by noise. Also, the time frequency domain analysis of RR variability has disadvantage to get the details of irregular RR interval rhythm. In this study, we describe an atrial fibrillation detection algorithm through non-linear analysis of irregular RR interval rhythm based on the variability, randomness and complexity. We employ a new statistical techniques root mean squares of successive differences(RMSSD), turning points ratio(TPR) and sample entropy(SpEn). The detection algorithm was tested using the optimal threshold on two databases, namely the MIT-BIH Atrial Fibrillation Database and the Arrhythmia Database. We have achieved a high sensitivity(Se:94.5%), specificity(Sp:96.2%) and Se(89.8%), Sp(89.62%) respectively.

• Journal of IKEEE
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• v.28 no.1
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• pp.116-122
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• 2024

AMBA AHB-Lite bus is widely used in on-chip bus protocol for low-power and cost-effective SoC. However, it lacks built-in error detection and correction for end-to-end data integrity. This can lead to data corruption and system instability, particularly in harsh environments like automotive applications. To mitigate this problem, this paper proposes the application of SEC-DED (Single Error Correction-Double Error Detection) to AMBA AHB-Lite bus. It aims not only to detect errors in real-time but also to correct them, thereby enhancing end-to-end data integrity. Simulation results demonstrate real-time error detection and correction when errors occur, which bolsters end-to-end data integrity of automotive on-chip bus.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1069-1078
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• 2012

It is known that memory has been frequently a target threatening the computer system's security while attacks on the system utilizing the memory's weakness are actually increasing. Accordingly, various memory protection mechanisms have been studied on OS while new attack techniques bypassing the protection systems have been developed. Especially, buffer overflow attacks have been developed as attacks of Return to Library or Return-Oriented Programing and recently, a technique bypassing the countermeasure against Return-Oriented Programming proposed. Therefore, this paper is intended to suggest a detection mechanism at binary level by analyzing the procedure and features of Jump-Oriented Programming. In addition, we have implemented the proposed detection mechanism and experimented it may efficiently detect Jump-Oriented Programming attack.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.75-84
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• 2022

The efficiency of the regional fiscal policy implementation is based on the achievement of target criteria in the formation and distribution of own financial resources of local budgets, reducing their deficit and reducing dependence on transfers. It is also relevant to compare the development of financial autonomy of regions in the course of decentralisation of fiscal relations. The study consists in the cluster analysis of the effectiveness of fiscal policy implementation in the context of 24 regions and the capital city of Kyiv (except for temporarily occupied territories) under conditions of fiscal decentralisation. Clustering of the regions of Ukraine by 18 indicators of fiscal policy implementation efficiency was carried out using Ward's minimum variance method and k-means clustering algorithm. As a result, the regions of Ukraine are grouped into 5 homogeneous clusters. For each cluster measures were developed to increase own revenues and minimize dependence on official transfers to increase the level of financial autonomy of the regions. It has been proved that clustering algorithms are an effective tool in assessing the effectiveness of fiscal policy implementation at the regional level and stimulating further expansion of financial decentralisation of regions.