• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• Annual Conference of KIPS
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• 2024.10a
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• pp.60-63
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• 2024

For many decades, memory corruption attacks have posed a significant threat to computer systems, particularly those written in unsafe programming languages such as C/C++. In response, a 'safe' programming language, Rust, was recently developed to prevent memory bugs by using compile-time and runtime checks. Rust's security and efficiency has lead its adoption from multiple popular applications such as Firefox and Tor. Due to the large code base and complexity of legacy software, the adoption generally takes a form of a gradual deployment, where security-critical portion of the program is replaced with Rust, resulting in a mixed-language application. Unfortunately, such adoption strategy introduced a new attack vector that propagates the vulnerabilities residing in the unsafe languages to Rust, undermining the security guarantees provided by Rust. In this paper, we shed light on strategies designed to defend against attacks that target multi-lingual applications to compromise the security of Rust. We study underlying rationale of various defense mechanisms and design decisions taken to improve their performance and effectiveness. Furthermore, we explore the limitations of existing defenses and argue that additional methods are necessary for Rust to fully benefit from its security promises in multi-language environments.

• International Journal of Computer Science & Network Security
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• v.24 no.10
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• pp.115-124
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• 2024

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contain a malicious and any illegal activity happened in network environments. To accomplish this we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifier are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.259-268
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• 2014

In this paper, we suggest a method of extracting circuit parameters of the insulating transformer using S-parameter measurement, especially in high frequency range. At 60 Hz, conventionally, no load test and short circuit test are used to extract the circuit parameters. In this paper S-parameters measured from VNA(Vector Network Analyzer) were used to extract the transformer parameters using data fitting method (optimization). The S-parameters from the equivalent circuit using the extracted parameters showed good agreement with those from measurement. Furthermore, the transformer secondary voltages from the equivalent circuit model also coincide quite exactly to the measured secondary voltages in sinusoidal forms. Finally we assert that the proposed method to extract the parameters for the insulating transformer using S-parameter is valid especially in high frequency.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.295-300
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• 2006

The integral imaging system is an auto-stereoscopic display that allows users to see 3D images without wearing special glasses. In the integral imaging system, the 3D object information is taken from several view points and stored as elemental images. Then, users can see a 3D reconstructed image by the elemental images displayed through a lens array. The elemental images can be created by computer graphics, which is referred to the computer-generated integral imaging. The process of creating the elemental images is called image mapping. There are some image mapping methods proposed in the past, such as PRR(Point Retracing Rendering), MVR(Multi-Viewpoint Rendering) and PGR(Parallel Group Rendering). However, they have problems with heavy rendering computations or performance barrier as the number of elemental lenses in the lens array increases. Thus, it is difficult to use them in real-time graphics applications, such as virtual reality or real-time, interactive games. In this paper, we propose a new image mapping method named VVR(Viewpoint Vector Rendering) that improves real-time rendering performance. This paper describes the concept of VVR first and the performance comparison of image mapping process with previous methods. Then, it discusses possible directions for the future improvements.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.148-153
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• 2007

This paper deals with implementing a steganography based multi-modal biometric system. For this purpose, we construct a multi-biometrics system based on the face and iris recognition. Here, the feature vector of iris pattern is hidden in the face image. The recognition system is designed by the fuzzy-based Linear Discriminant Analysis(LDA), which is an expanded approach of the LDA method combined by the theory of fuzzy sets. Furthermore, we present a watermarking method that can embed iris information into face images. Finally, we show the advantages of the proposed watermarking scheme by computing the ROC curves and make some comparisons recognition rates of watermarked face images with those of original ones. From various experiments, we found that our proposed scheme could be used for establishing efficient and secure multi-modal biometric systems.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.819-822
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• 2004

To transmit MPEG-2 video on an erroneous channel, a number of error control techniques He needed. Especially, error concealment techniques which can be implemented on receivers independent of transmitters are essential to obtain good video quality. In this paper, a motion adaptive boundary matching algorithm (MA-BMA) is presented for temporal error concealment. Before carrying out BMA, we perform error concealmmt by a motion vector prediction using neighboring motion vectors. If the candidate of error concealment is rot satisfied, search range and reliable boundary pixels are selected by the motion activity or motion vectors ane a damaged macroblock is concealed by applying the MA-BMA. This error concealment technique reduces the complexity and maintains PSNR gain of 0.3 0.7dB compared to the conventional BMA.