• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1027-1037
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• 2019

Conventional cyber-attack detection solutions are generally based on signature-based or malicious behavior analysis so that have had difficulty in detecting unknown method-based attacks. Since the various information occurring all the time reflects the state of the system, by modeling it in a steady state and detecting an abnormal state, an unknown attack can be detected. Since a variety of system information occurs in a string form, word embedding, ie, techniques for converting strings into vectors preserving their order and semantics, can be used for modeling and detection. Novelty Detection, which is a technique for detecting a small number of abnormal data in a plurality of normal data, can be performed in order to detect an abnormal condition. This paper proposes a method to detect system anomaly by cyber attack using embedding and novelty detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.6
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• pp.79-89
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• 2004

Fingerprint identification consists of user enrollment phase storing user's fingerprint in a database and user identification phase making a candidate list for a given fingerprint. straightforward approach to perform the user identification phase is to scan the entire database sequentially, and takes times for large-scale databases. In this paper, we develop a hardware design which can perform the user identification phase in real-time. Our design employs parallel processing techniques and has been implemented on a PCI-based platform containing an FPGA and SDRAMs. Based on the performance evaluation, our hardware implementation can provide a scalability and perform the fingerprint identification in real-time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.13-22
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• 2005

To improve the anomaly IDS using system calls, this study focuses on Neural Networks Learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm with fuzzy membership function. The back-propagation neural networks and Neuro-Fuzzy technique are applied for anomaly intrusion detection of system calls using Sendmail Data of UNM to demonstrate its aspect of he complexity of time, space and MDL performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.11 no.5
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• pp.75-84
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• 2001

Existent public key certificates provide authentication information through some information on user\`s public key. However, an attribute certificate which stores and manage user\`s attribute information, provides various privilege information such as position, privilege and role. In recent, international organizations establishes standards on attribute certificate, and the researches and developments on attribute certificate have been widely made. In addition it may be expected to be used many real application area requiring for authorization information as well as authentication information. Therefore, this paper considers background and standardization trends of attribute certificate and describes the profile and related techniques of attribute certificate currently established by IETF. In addition, it introduces and access control system using attribute certificate and specifies applications of attribute certificate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.3-12
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• 2006

The spread of mobile devices, PDAs and sensors has enabled the construction of ubiquitous computing environments, transforming regular physical spaces into 'smart space' augmented with intelligence and enhanced with services. However, unless privacy concerns are taken into account early in the design process of various ubiquitous devices(e.g. mobile devices, PDAs, sensors, etc.). we will end up crating ubiquitous surveillance infrastructure. Also, it may inappropriate to use public key techniques for computational constrained devices in ubiquitous computing environment. In this paper, we propose efficient user authentication and ky agreement protocol not only to preserve anonymity for protecting personal privacy but also to be suitable for computational constrained devices in ubiquitous computing environments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.53-62
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• 2010

In VANETs (Vehicular Ad hoc NETwork), conditional anonymity must be provided to protect privacy of vehicles while enabling authorities to identify misbehaving vehicles. To this end, previous systems provide a mechanism to revoke the anonymity of individual messages. In VANET, if we can trace the movement path of vehicles, it can be useful in determining the liability of vehicles in car accidents and crime investigations. Although route tracing can be provided using previous message revocation techniques, they violate privacy of other vehicles. In this paper, we provide a route tracing technique that protects privacy of vehicles that are not targeted. The proposed method can be employed independently of the authentication mechanism used and includes a mechanism to prevent authorities from abusing this new function.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer