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

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

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 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

New Insider Threat Detection Method Based On Recurrent Neural Networks

Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions

A new intelligent multilayer framework for insider threat detection

In several earlier studies, machine learning (ML) has been widely used for building insider threat detection systems. However, the selection of the most appropriate ML classification model for insider threats detection remains a challenge. Despite the prominence of ML in the domain of insider threat detection, none of the previous works have utilized ML techniques for building a hybrid solution that can take advantage of the misuse and anomaly insider threat detection. In this study, a new multilayer framework has been proposed for insider threat detection. The first layer of the framework is used for selecting the best insider threat detection classification model among many based on the multi-criteria decision making techniques. The selection procedure has been developed based on the integration of the entropy-VIKOR methods. For the second layer, a hybrid insider threat detection method has been proposed, where the Misuse Insider Threat Detection (MITD) model has been created using the random forest algorithm. Subsequently, using the K-Nearest Neighbors algorithm, an anomaly insider threat detection algorithm has been developed. The proposed multilayer framework for insider threat detection has been evaluated by using the CERT r4.2 dataset. Results of the experiment demonstrate that the validity of the results produced by the selection framework is proven by the validation procedure obtained from previous research. The proposed hybrid detection method is observed to exhibit an overall accuracy of 99% and a false positive rate of 0.29% for known insider threats, whereas it exhibits 97% accuracy and 2.88% false-positive rate for unknown insider threats