• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.5
• /
• pp.911-917
• /
• 2021

As software gets an increasing amount of patches, lots of software bugs are increasingly caused by such software patches, collectively known as regression bugs. To proactively detect the regressions bugs, both industry and academia are actively searching for a way to augment fuzz testing, one of the most popular automatic bug detection techniques. In this paper, we investigate the status quo of the studies on augmenting fuzz testing for regression bug detection and, based on the limitations of current proposals, provide an outlook of the relevant research.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.230-238
• /
• 2021

A sensor network is made up of many sensors deployed in different areas to be monitored. They communicate with each other through a wireless medium. The routing of collected data in the wireless network consumes most of the energy of the network. In the literature, several routing approaches have been proposed to conserve the energy at the sensor level and overcome the challenges inherent in its limitations. In this paper, we propose a new low-energy routing protocol for power grids sensors based on an unsupervised clustering approach. Our protocol equitably harnesses the energy of the selected cluster-head nodes and conserves the energy dissipated when routing the captured data at the Base Station (BS). The simulation results show that our protocol reduces the energy dissipation and prolongs the network lifetime.

• International Journal of Advanced Culture Technology
• /
• v.8 no.4
• /
• pp.119-128
• /
• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.2
• /
• pp.61-72
• /
• 2007

With the increasing growth of Internet and wireless IP networks, Multimedia systems need to be envisaged as information resources where users can access anywhere and anytime. However, efficient services in these multimedia systems are open and challenging research problem due to user mobility, limited resources in wireless devices and expensive radio bandwidth. To implement multimedia services over heterogeneous network, the IP header compression scheme can be used for saving bandwidth. In this paper, we present an efficient solution for header compression, which is modified form of ECRTP. It shows an architectural framework adopting modified ECRTP when IP tunneling network using GRE over IPSec is implemented. We have conducted simulations in order to analyze the effects of different header compression techniques while delivering real-time services to the wireless access network through secured IP Network. The impacts on performance have been investigated through a series of experiments.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.221-228
• /
• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.165-171
• /
• 2021

Vehicular Ad hoc Network (VANET) is an extension paradigm of moving vehicles to communicate with wireless transmission devices within a certain geographical limit without any fixed infrastructure. The vehicles have most important participation in this model is usually positioned quite dimly within the certain radio range. Fuzzy based multi-hop broadcast protocol is better than conventional message dissemination techniques in high-mobility VANETs, is proposed in this research work. Generally, in a transmission range the existing number of nodes is obstacle for rebroadcasting that can be improved by reducing number of intermediate forwarding points. The proposed protocol stresses on transmission of emergency message projection by utilization subset of surrounding nodes with consideration of three metrics: inter-vehicle distance, node density and signal strength. The proposed protocol is fuzzy MHB. The method assessment is accomplished in OMNeT++, SUMO and MATLAB environment to prove the efficiency of it.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.209-213
• /
• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.214-222
• /
• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• International Journal of Computer Science & Network Security
• /
• v.21 no.2
• /
• pp.188-192
• /
• 2021

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.43-47
• /
• 2021

In the course of this article, the concept of "innovation" was considered and analyzed, which is considered not only as a subject, something new, but also as a process. The process of introducing something new into life, and in our case, into the educational process. Innovative educational technologies are varied and plentiful. In this article, the most commonly used. Among them: the use of ICT, game techniques, the portfolio method, personality-oriented, information support of the learning process, educational and health-saving technologies, and others.