• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.59-66
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• 2023

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.193-206
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• 2024

In tandem with the advancements in the digital era, the significance of digital data has escalated, necessitating an increased focus on digital forensics investigations. However, the process of collecting and analyzing digital evidence faces significant challenges, such as the unidentifiability of damaged files due to issues like media corruption and anti-forensic techniques. Moreover, the technological limitations of existing tools hinder the recovery of damaged files, posing difficulties in the evidence collection process. This paper aims to propose solutions for the recovery of corrupted MS Office files commonly used in digital data creation. To achieve this, we analyze the structure of MS Office files in the OOXML format and present a novel approach to overcome the limitations of current recovery tools. Through these efforts, we aim to contribute to enhancing the quality of evidence collection in the field of digital forensics by efficiently recovering and identifying damaged data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.167-177
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• 2024

This paper proposes optimization techniques for SeaSign, an isogeny-based digital signature algorithm. SeaSign combines class group actions of CSIDH with the Fiat-Shamir with abort. While CSIDH-based algorithms have regained attention due to polynomial time attacks for SIDH-based algorithms, SeaSiogn has not undergone significat optimization because of its inefficiency. In this paper, an efficient signing method for SeaSign is proposed. The proposed signing method is simple yet powerful, achived by repositioning the rejection sampling within the algorithm. Additionally, this paper presnts parameters that can provide optimal performance for the proposed algorithm. As a result, by using the original parameters of SeaSign, the proposed method is three times faster than the original SeaSign. Additonally, combining the newly suggested parameters with the signing method proposed in this paper yields a performance that is 290 times faster than the original SeaSign and 7.47 times faster than the method proposed by Decru et al.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.515-525
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• 2024

Dark patterns refer to intentionally deceptive design techniques used by online service providers to hide necessary information, preventing users from taking desired actions or luring them into unintended behaviors. In this study, we analyzed the prevalence of dark patterns such as banners, advertorials, pop-ups, and video ads, and their impact on users across the top 200 news websites worldwide. The research revealed that there is a minimal correlation between banner ads and user bounce rates or unique visitors. Consequently, the main screen moving banner and headline news screen moving banner were most frequently observed in South America, while the headline news screen fixed banner was most commonly observed in Asia. All other categories were predominantly observed in Europe, making European websites the most diverse and abundant in various dark patterns.

• Convergence Security Journal
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• v.23 no.5
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• pp.45-55
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• 2023

With the widespread use of digital devices, anonymous communication technologies such as the dark web and deep web are becoming increasingly popular for criminal activity. Because these technologies leave little local data on the device, they are difficult to track using conventional crime investigation techniques. The United States and the United Kingdom have enacted laws and developed systems to address this issue, but South Korea has not yet taken any significant steps. This paper proposes a new blockchain-based crime investigation method that uses physical memory data analysis to track the behavior of anonymous network users. The proposed method minimizes infringement of basic rights by only collecting physical memory data from the device of the suspected user and storing the tracking information on a blockchain, which is tamper-proof and transparent. The paper evaluates the effectiveness of the proposed method using a simulation environment and finds that it can track the behavior of dark website users with a residual rate of 77.2%.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.15-24
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• 2024

One of the most significant issues in combinatorial optimization is the classical NP-complete conundrum known as the 0/1 Knapsack Problem. This study delves deeply into the investigation of practical solutions, emphasizing two classic algorithmic paradigms, brute force, and dynamic programming, along with the metaheuristic and nature-inspired family algorithm known as the Genetic Algorithm (GA). The research begins with a thorough analysis of the dynamic programming technique, utilizing its ability to handle overlapping subproblems and an ideal substructure. We evaluate the benefits of dynamic programming in the context of the 0/1 Knapsack Problem by carefully dissecting its nuances in contrast to GA. Simultaneously, the study examines the brute force algorithm, a simple yet comprehensive method compared to Branch & Bound. This strategy entails investigating every potential combination, offering a starting point for comparison with more advanced techniques. The paper explores the computational complexity of the brute force approach, highlighting its limitations and usefulness in resolving the 0/1 Knapsack Problem in contrast to the set above of algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.143-156
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• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.172-180
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• 2024

Since the beginning of 2015, corporate social responsibility (CSR) models have been changing in connection with the trend towards the transition of joint value creation of corporate activities and consideration of stakeholders' interests. The purpose of the academic paper lies in empirically studying the current practice of social responsibility of transnational corporations (TNCs). The research methodology has combined the method of qualitative analysis, the method of cases of agricultural holdings in emerging markets within the framework of resource theory, institutional theory and stakeholders' theory. The results show that the practice of CSR is integrated into the strategy of sustainable development of TNCs, which determine the methods, techniques and forms of communication, as well as areas of stakeholders' responsibility. The internal practice of CSR is aimed at developing norms and standards of moral behaviour with stakeholders in order to maximize economic and social goals. Economic goals are focused not only on making a profit, but also on minimizing costs due to the potential risks of corruption, fraud, conflict of interest. The system of corporate social responsibility of modern TNCs is clearly regulated by internal documents that define the list of interested parties and stakeholders, their areas of responsibility, greatly simplifying the processes of cooperation and responsibility. As a result, corporations form their own internal institutional environment. Ethical norms help to avoid the risks of opportunistic behaviour of personnel, conflicts of interest, cases of bribery, corruption, and fraud. The theoretical value of the research lies in supplementing the theory of CSR in the context of the importance of a complex, systematic approach to integrating the theory of resources, institutional theory, theory of stakeholders in the development of strategies for sustainable development of TNCs, the practice of corporate governance and social responsibility.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.