• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.267-274
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• 2021

Medical care practices include gathering a wide range of student data that are with manic episodes and depression which would assist the specialist with diagnosing a health condition of the students correctly. In this way, the instructors of the specific students will also identify those students and take care of them well. The data which we collected from the students could be straightforward indications seen by them. The artificial intelligence has been utilized with Naive Baye's classification, Random forest classification algorithm, SVM algorithm to characterize the datasets which we gathered to check whether the student is influenced by Bipolar illness or not. Performance analysis of the disease data for the algorithms used is calculated and compared. Also, a sequential deep learning model is builded using Keras. The consequences of the simulations show the efficacy of the grouping techniques on a dataset, just as the nature and complexity of the dataset utilized.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.775-785
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• 2023

As processor technology advances, it has accelerated ISA extensions and increased the complexity of micro-architectures, leading to a continued rise in the importance of processor validation techniques. Recently, various fuzzing techniques have been introduced to discover undocumented instructions, and this study highlights the shortcomings of existing undocumented instruction fuzzing techniques and presents our observation on error cases in the latest processors from Intel and AMD. In particular, we analyzes the causes of false positives resulting from the fuzzer incorrectly judging CPU instruction length and proposes the length determination technique based on instruction execution information to improve accuracy.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.81-88
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• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• Convergence Security Journal
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• v.24 no.3
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• pp.23-31
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• 2024

The anonymity and decentralized nature of cryptocurrencies make them highly susceptible to criminal exploitation, requiring the development of effective tracking techniques. By analyzing various open source intelligence(OSINT), such as public data, social media, and online forums, open source intelligence can provide useful information for identifying criminals and tracking the flow of cryptocurrency funds. In this study, we present a comprehensive proposal for the utilization of open source intelligence. We will discuss the current status and trends of cryptocurrency and related crimes, and introduce the concept and methodology of open source intelligence. The paper then focuses on five methods and seven frameworks of open source intelligence for tracking and analyzing cryptocurrency-related crimes, and presents techniques for the integrated application of open source intelligence methods and frameworks.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.145-152
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• 2024

Agile project management is an ongoing method to project completion that actually took place all across length of the project's life cycle. Because loop allows you to adapt as you go rather than maintaining a linear route, iterative methods are mainly applied in software development to ensure velocity and agility. Many pitfalls abound in agile software development adaptations that organizations fail to sidestep. New competitive challenges, fluctuating market dynamics, technological disruption, and the ever-changing demands of empowered customers confront organizations around the world. Organizations are all under tremendous pressure to adapt to change and deliver good products and services to customers more swiftly. Research measured at the challenges that could be encountered and offered advice for how agile development might flourish as it becomes a component of a company's family. Our paper gives a comprehensive review of the most significant obstacles that companies encounter while adopting agile techniques. Adaptability. The agile approach encompasses a variety of techniques, which each have different usage in various sectors. Because certain other standards existing today clash with agile methodology, the adaptation of any of the agile techniques in work environments posed a problem. In this paper, we will cover some of the challenges that firms face in adopting the agile software development life cycle.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.827-839
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• 2012

A cell phone is very close to the user and therefore should be considered in digital forensic investigation. Recently, the proportion of smartphone owners is increasing dramatically. Unlike the feature phone, users can utilize various mobile application in smartphone because it has high-performance operating system (e.g., Android, iOS). As acquisition and analysis of user data in smartphone are more important in digital forensic purposes, smartphone forensics has been studied actively. There are two way to do smartphone forensics. The first way is to extract user's data using the backup and debugging function of smartphones. The second way is to get root permission, and acquire the image of flash memory. And then, it is possible to reconstruct the filesystem, such as YAFFS, EXT, RFS, HFS+ and analyze it. However, this methods are not suitable to recovery and analyze deleted data from smartphones. This paper introduces analysis techniques for fragmented flash memory pages in smartphones. Especially, this paper demonstrates analysis techniques on the image that reconstruction of filesystem is impossible because the spare area of flash memory pages does not exist and the pages in unallocated area of filesystem.

• Journal of the Korean housing association
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• v.13 no.6
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• pp.121-131
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• 2002

The rebuilding method for old apartment housing blocks has merits of providing new buildings and larger private living spaces. However, it causes many serious urban problems, such as shortage of infrastructure capacity, traffic congestion, reduction of building life, and deterioration of open space quality. Nowadays, remodeling is accepted as a way of overcoming such negative effects of the rebuilding method. This study focuses on the various techniques of remodeling. The results of this research are as follows; First, old apartment blocks provide poor level of service in general, so they have problems of bad accessibility, deterioration of facilities, and degraded landscape. In many cases, there is a hindrance from walking freely and security problem due to illegal privatization of public spaces. Second, various remodeling techniques are required to meet residents' different needs. The residents of apartment housing value private space above public space, and show low level of willingness to pay cost for remodeling. Third, based on these findings, some remodeling techniques are suggested - integration of a space to another, expansion of spaces, connection of spaces, reuses of roofs and walls, relocation and renovation of paths between buildings, and so on.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2892-2913
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• 2016

Network virtualization promises to play a dominant role in shaping the future Internet by overcoming the Internet ossification problem. However, due to the injecting of additional virtualization layers into the network architecture, several new security risks are introduced by the network virtualization. Although traditional protection mechanisms can help in virtualized environment, they are not guaranteed to be successful and may incur high security overheads. By performing the virtual network (VN) embedding in a security-aware way, the risks exposed to both the virtual and substrate networks can be minimized, and the additional techniques adopted to enhance the security of the networks can be reduced. Unfortunately, existing embedding algorithms largely ignore the widespread security risks, making their applicability in a realistic environment rather doubtful. In this paper, we attempt to address the security risks by integrating the security factors into the VN embedding. We first abstract the security requirements and the protection mechanisms as numerical concept of security demands and security levels, and the corresponding security constraints are introduced into the VN embedding. Based on the abstraction, we develop three security-risky modes to model various levels of risky conditions in the virtualized environment, aiming at enabling a more flexible VN embedding. Then, we present a mixed integer linear programming formulation for the VN embedding problem in different security-risky modes. Moreover, we design three heuristic embedding algorithms to solve this problem, which are all based on the same proposed node-ranking approach to quantify the embedding potential of each substrate node and adopt the k-shortest path algorithm to map virtual links. Simulation results demonstrate the effectiveness and efficiency of our algorithms.