• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.9
• /
• pp.1684-1697
• /
• 2011

We show that two protocols for RFID mutual authentication in pervasive computing environments, recently proposed by Kang et al, are vulnerable to several attacks. First, we show these protocols do not preserve the privacy of users' location. Once a tag is authenticated successfully, we show several scenarios where legitimate or illegitimate readers can trace the location of that tag without any further information about the tag's identifier or initial private key. Second, since the communication between readers and the database takes place over an insecure communication channel and in the plaintext form, we show scenarios where a compromised tag can gain access to confidential information that the tag is not supposed get access to. Finally, we show that these protocols are also vulnerable to the replay and denial-of-service attacks. While some of these attacks are due to simple flaws and can be easily fixed, others are more fundamental and are due to relaxing widely accepted assumptions in the literature. We examine this issue, apply countermeasures, and re-evaluate the protocols overhead after taking these countermeasures into account and compare them to other work in the literature.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.5153-5170
• /
• 2016

The benefit of the scalability and flexibility inherent in cloud computing motivates clients to upload data and computation to public cloud servers. Because data is placed on public clouds, which are very likely to reside outside of the trusted domain of clients, this strategy introduces concerns regarding the security of sensitive client data. Thus, to provide sufficient security for the data stored in the cloud, it is essential to encrypt sensitive data before the data are uploaded onto cloud servers. Although data encryption is considered the most effective solution for protecting sensitive data from unauthorized users, it imposes a significant amount of overhead during the query processing phase, due to the limitations of directly executing operations against encrypted data. Recently, substantial research work that addresses the execution of SQL queries against encrypted data has been conducted. However, there has been little research on top-k join query processing over encrypted data within the cloud computing environments. In this paper, we develop an efficient algorithm that processes a top-k join query against encrypted cloud data. The proposed top-k join processing algorithm is, at an early phase, able to prune unpromising data sets which are guaranteed not to produce top-k highest scores. The experiment results show that the proposed approach provides significant performance gains over the naive solution.

• Journal of Advanced Navigation Technology
• /
• v.17 no.3
• /
• pp.325-331
• /
• 2013

Social network services(SNS) has been used as a means of communication for user or express themselves user. Therefore, SNS has a variety of information. This information is useful to help the investigation can be used as evidence. In this paper, A study of mobile digital forensic procedure for crime investigation in social network service. Analysis of database file taken from the smartphone at social network service application for mobile digital forensic procedure. Therefore, we propose a procedure for the efficient investigation of social network service mobile digital forensic.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.369-376
• /
• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.127-136
• /
• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.5 no.1
• /
• pp.51-64
• /
• 1995

In distributed environments, the DB secure models have been being studied to include the multi-level mechanism which is effective to control access according to the level of the data value. These mechanisms have the problems. The first, it is impossible to maintain the global data which is protected in the multi-level mechanism. The second, the access and the relation of the data is not clear due to the access revocation between the local data and the global's. In this paper, we proposed the mechanism using shema. The mechanism doesn't have the access revocation, and provides the protection of the data and the control to the global data.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.4
• /
• pp.151-159
• /
• 2003

Internet as being generalized, intrusion detection system is needed to protect computer system from intrusions synthetically. We propose an intrusion detection method to identify and control the contradiction on self-explanation that happen at profiling process of anomaly detection methodology. Because many patterns can be created on profiling process with association method, we present effective application plan through clustering for rules. Finally, we propose similarity function to decide whether anomaly action or not for user pattern using clustered pattern database.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.3
• /
• pp.115-127
• /
• 2006

The purpose of this paper is evaluation of various normalization methods and fusion algorithms in addition to pattern classification algorithms for multi-biometric systems. Experiments are performed using various normalization functions, fusion algorithms and pattern classification algorithms based on Biometric Scores Set-Releasel(BSSR1) provided by NIST. The performance results are presented by Half Total Error Rate (WTER). This study gives base data for the study on performance enhancement of multiple-biometric system by showing performance results using single database and metrics.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.5
• /
• pp.133-138
• /
• 2010

We present a new multimodal biometric authentication method, which performs both feature-level fusion and decision-level fusion. After generating support vector machines for new features made by integrating face and voice features, the final decision for authentication is made by integrating decisions of face SVM classifier, voice SVM classifier and integrated features SVM clssifier. We justify our proposal by comparing our method with traditional one by experiments with XM2VTS multimodal database. The experiments show that our multilevel fusion algorithm gives higher recognition rate than the existing schemes.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.37-40
• /
• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.