Threat Analysis For Cyber Physical System

Cyber physical systems are the systems that have an interaction between the computers and the real-world; it has been widely used in many different areas and played a major role in our daily lives, Smart Grid, healthcare, aircrafts, and emergency management are the most areas where CPS applied. However the cyber physical systems currently one of the important hackers‟ target that have a lot of incidents because of the high impacts of these systems, many works have been conducted in CPS but still there are a lack of theories and tools that organizations and researchers can use to understand the natural of the new threats and the impacts that each threat can cause to the physical systems, in this project we will investigate the current threats on CPSs, develop matrices to calculate these threats, and conduct analysis on the collected data using quantitative approach

Skew detection and correction of mushaf Al-Quran script using hough transform

Document skew detection and correction is mainly one of base preprocessing steps in the document analysis. Correction of the skewed scanned images is critical because it has a direct impact on image quality. In this paper, the authors proposed a method for skew detection and correction for Mushaf Al-Quran image pages based on Hough transform method. The technique uses Hough transform lines detection for calculating the skew angulation. It works for different version of Mushaf AlQuran image pages which has skewed text zones. Moreover, it can detect and correct the skew angle in the range between 20 degrees. Experiment conducted on different Mushaf Al-Quran image pages shows the accuracy of the method

Impacto de la sociedad sin dinero en efectivo en el crecimiento económico de Malasia

For every economy, money is a critical medium of exchange. Money emerges as an intermediary and store of values to improve a barter system. Cashless finance endures utilization generally digital money or plastic without money or currency in papery appearance. The moderate of regulating economic activity endure possibly the massive motivator toward advance the digital economy. This research scrutinizes the progress of embrace cashless payment on economic development and growth of Malaysia. Due to rapid growth in the digital economy, electronic money transactions in Malaysia have increased significantly from in the last fifteen years. There is a positive trend in electronic money usage. The increases caused by the awareness of the society and the government that encourages in using electronic money. At the same time, the velocity of paper money in Malaysia tends to decrease during the same period. While there is a negative trend in the velocity of money, the money supply (M1) has increased in order to maintain an economic growth of the country.Esta investigación examina el progreso de la adopción de pagos sin efectivo en el desarrollo económico y el crecimiento de Malasia. Debido al rápido crecimiento de la economía digital, las transacciones de dinero electrónico en Malasia han aumentado significativamente en los últimos quince años. Hay una tendencia positiva en el uso del dinero electrónico. Los aumentos causados ​​por la conciencia de la sociedad y el gobierno que fomenta el uso del dinero electrónico. Al mismo tiempo, la velocidad del papel moneda en Malasia tiende a disminuir durante el mismo período. Si bien hay una tendencia negativa en la velocidad del dinero, la oferta de dinero (M1) ha aumentado para mantener el crecimiento económico del país

An Insider Threat Categorization Framework for Automated Manufacturing Execution System

Insider threats become one of the most dangerous threats in the cyber world as compared to outsider as the insiders have knowledge of assets. In addition, the threats itself considered in-visible and no one can predict what, when and how exactly the threat launched. Based on conducting literature, threat in Automated Manufacturing Execution Systems (AMESs) can be divided into three principle factors. Moreover, there is no standard framework to be referring which exist nowadays to categorize such factors in order to identify insider threats possible features. Therefore, from the conducted literature a standard theoretical categorization of insider threats framework for AMESs has been proposed. Hence, three principle factors, i.e. Human, Systems and Machine have considered as major categorization of insider threats. Consequently, the possible features for each factor identified based on previous researcher recommendations. Therefore, via identifying possible features and categorize it into principle factors or groups, a standard framework could be derived. These frameworks will contribute more benefit specifically in the manufacturing field as a reference to mitigate an insider threat.   Keywords—automated manufacturing execution systems insider threats, factors and features, insider threat categorization framework

Adaptive Deep Learning Detection Model for Multi-Foggy Images

The fog has different features and effects within every single environment. Detection whether there is fog in the image is considered a challenge and giving the type of fog has a substantial enlightening effect on image defogging. Foggy scenes have different types such as scenes based on fog density level and scenes based on fog type. Machine learning techniques have a significant contribution to the detection of foggy scenes. However, most of the existing detection models are based on traditional machine learning models, and only a few studies have adopted deep learning models. Furthermore, most of the existing machines learning detection models are based on fog density-level scenes. However, to the best of our knowledge, there is no such detection model based on multi-fog type scenes have presented yet. Therefore, the main goal of our study is to propose an adaptive deep learning model for the detection of multi-fog types of images. Moreover, due to the lack of a publicly available dataset for inhomogeneous, homogenous, dark, and sky foggy scenes, a dataset for multi-fog scenes is presented in this study (https://github.com/Karrar-H-Abdulkareem/Multi-Fog-Dataset). Experiments were conducted in three stages. First, the data collection phase is based on eight resources to obtain the multi-fog scene dataset. Second, a classification experiment is conducted based on the ResNet-50 deep learning model to obtain detection results. Third, evaluation phase where the performance of the ResNet-50 detection model has been compared against three different models. Experimental results show that the proposed model has presented a stable classification performance for different foggy images with a 96% score for each of Classification Accuracy Rate (CAR), Recall, Precision, F1-Score which has specific theoretical and practical significance. Our proposed model is suitable as a pre-processing step and might be considered in different real-time applications

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing

Diacritic Segmentation Technique For Arabic Handwritten Using Region-Based

Arabic is a broadly utilized alphabetic composition framework on the planet, and it has 28 essential letters. The letters in order was first used to compose messages in Arabic, most prominently the Qur'an the holy book of Islam. However, Arabic language has diacritics in the word or letters which are not something extra or discretionary to the language, rather they are a vital piece of it. By changing some diacritics may change both the syntax and semantics of a word by turning a word into another. However, the current researches address the foreground image and consider the diacritics as noises or secondary images. Thus, it is not suitable for Arabic handwritten. The diacritics will be removed from the image and this will lead to losing some good features. Furthermore, to extract the diacritics, the region-based segmentation technique is used. The image will be measured based on the region properties by first finding the connected component in binary image, and then we will determine the best area range measurement in that region for each image. The proposed technique region based has been tested in nine different images with different handwritten style, and successfully extracted secondary foreground images (diacritics) for each imag

Skew Detection And Correction Of Mushaf Al-Quran Script Using Hough Transform

Document skew detection and correction is mainly one of base preprocessing steps in the document analysis. Correction of the skewed scanned images is critical because it has a direct impact on image quality. In this paper, the authors proposed a method for skew detection and correction for Mushaf Al-Quran image pages based on Hough transform method. The technique uses Hough transform lines detection for calculating the skew angulation. It works for different version of Mushaf Al-Quran image pages which has skewed text zones. Moreover, it can detect and correct the skew angle in the range between 20 degrees. Experiment conducted on different Mushaf Al-Quran image pages shows the accuracy of the method

Skew Correction For Mushaf Al-Quran: A Review

Skew correction has been studied a lot recently. However, the content of skew correction in these studies is considered less for Arabic scripts compared to other languages. Different scripts of Arabic language are used by people. Mushaf A-Quran is the book of Allah swt and used by many people around the world. Therefore, skew correction of the pages in Mushaf Al-Quran need to be studied carefully. However, during the process of scanning the pages of Mushaf Al-Quran and due to some other factors, skewed images are produced which will affect the holiness of the Mushaf AlQuran. However, a major difficulty is the process of detecting the skew and correcting it within the page. Therefore, this paper aims to view the most used skew correction techniques for different scripts as cited in the literature. The findings can be used as a basis for researchers who are interested in image processing, image analysis, and computer visio

Cyber-Security Incidents: A Review Cases In Cyber-Physical Systems

Cyber-Physical Systems refer to systems that have an interaction between computers, communication channels and physical devices to solve a real-world problem. Towards industry 4.0 revolution, Cyber-Physical Systems currently become one of the main targets of hackers and any damage to them lead to high losses to a nation. According to valid resources, several cases reported involved security breaches on Cyber-Physical Systems. Understanding fundamental and theoretical concept of security in the digital world was discussed worldwide. Yet, security cases in regard to the cyber-physical system are still remaining less explored. In addition, limited tools were introduced to overcome security problems in Cyber-Physical System. To improve understanding and introduce a lot more security solutions for the cyber-physical system, the study on this matter is highly on demand. In this paper, we investigate the current threats on Cyber-Physical Systems and propose a classification and matrix for these threats, and conduct a simple statistical analysis of the collected data using a quantitative approach. We confirmed four components i.e., (the type of attack, impact, intention and incident categories) main contributor to threat taxonomy of Cyber-Physical System