The Apartheid System in the Israeli and South African Experiences

The apartheid system prevailed in South Africa in 1948. The natives of the land were segregated into ethnic groups in certain locations known as the Bantustan. According to apartheid laws, each ethic group, whether white or black in skin, is only liable to inhabit a certain region. However, the white have the right of mobilization while the Black are imprisoned in their selected areas. Such discrimination alludes to the one which is established by Israel to discriminate between the Palestinian Muslims and Christians in the West Bank, Gaza Strip and other occupied lands. Such racial discrimination is deeply rooted in the Jewish rationale. Sephardim and Ashkenazim, i.e. western or eastern Jews serve as a good example on this issue. The Western Jews look down upon their Eastern peers as being inferior by all means. The Zionist movement whose members are originally from the west have instigated this inequality and prejudice against their Eastern peers

Integrating Blockchain with Fog and Edge Computing for Micropayment Systems

Fog computing eliminates the need for a centralized cloud data center as it allows computation to be done by nodes closer to the edge. This helps in improving scalability, latency, and throughput compared to traditional cloud environment. Furthermore, with massive increase of IoT devices in the coming future, current solutions that consider centralized cloud computing may not be suitable. Blockchain has developed as a powerful technology enabling unlimited application and opportunities during the last decade. As both blockchain and fog computing technologies operate on a decentralized framework for operations, their integration can help in driving many technologies forward and provide tremendous advantage in terms of security and cost. Recently, micropayments are adopted into a large number of applications. However, individually processing micropayments will result in higher transaction fees where in some cases transaction fee can exceed the payment value. Due to this reason, traditional cryptocurrency blockchain like Bitcoins is inappropriate for micropayment transactions. As such, using fog computing for micropayment can improve the latency and scalability. On the other side, the increased speed and connection density offered by 5G technology will enable real-time processing of data as well as automated transaction processing between connected devices. The 5G technology will enable the smart devices to make micropayments by processing data more efficiently. This will have far-reaching impact on business financial management. The 6G networks will exhibit more heterogeneity than 5G enabling different types of devices to communicate in an efficient way. This will enhance the micropayment networks where different types of IoT devices will be able to connect and hence process payments and transactions in a more secure way. Integrating this intelligence with big data in blockchain and fog computing will change the traditional business models and support the creation of efficient and fast micropayment systems.This chapter explains the benefits of integrating modern technologies (fog computing, blockchain, 6G, and IoT) to solve the problem of micropayment systems. This is achieved by utilizing the capabilities of each technology (e.g., edge computing, blockchain, evolution of 5G to 6G) to bring intelligence from centralized computing facilities to edge/fog devices allowing for more envisioned applications such as micropayment systems where reliable, cheap, high speed, secure, and reduced latency transaction processing can be achieved. The chapter also highlights the various relationships among these technologies and surveys the most relevant work in order to analyze how the use of these disruptive technologies could potentially improve the micropayment systems functionality. Furthermore, various forms of integration of these technologies and associated applications are discussed, and solutions/challenges are outlined. The chapter also briefly discusses a generic solution to the problem of micropayments by integrating fog computing capabilities, blockchain, and edge computing to provide a practical payment setup that allows customers to issue micropayments in a convenient, fast, and secure manner

On the formal verification of group key security protocols

The correctness of group key security protocols in communication systems remains a great challenge because of dynamic characteristics of group key construction as we deal with an open number of group members. Therefore, verification approaches for two parties protocols cannot be applied on group key protocols. Security properties that are well defined in normal two-party protocols have different meanings and different interpretations in group key distribution protocols, and so they require a more precise definition before we look at how to verify them. An example of such properties is secrecy, which has more complex variations in group key context: forward secrecy, backward secrecy, and key independence. In this thesis, we present a combination of three different theorem-proving methods to verify security properties for group-oriented protocols. We target regular group secrecy, forward secrecy, backward secrecy, and collusion properties for group key protocols. In the first method, rank theorems for forward properties are established based on a set of generic formal specification requirements for group key management and distribution protocols. Rank theorems imply the validity of the security property to be proved, and are deducted from a set of rank functions we define over the protocol. Rank theorems can only reason about absence of attacks in group key protocols. In the second method, a sound and complete inference system is provided to detect attacks in group key management protocols. The inference system provides an elegant and natural proof strategy for such protocols compared to existing approaches. It complements rank theorems by providing a method to reason about the existence of attacks in group key protocols. However, these two methods are based on interactive higher-order logic theorem proving, and therefore require expensive user interactions. Therefore, in the third method, an automation sense is added to the above techniques by using an event-B first-order theorem proving system to provide invariant checking for group key secrecy property and forward secrecy property. This is not a straightforward task, and should be based on a correct semantical link between group key protocols and event-B models. However, in this method, the number of protocol participants that can be considered is limited, it is also applicable on a single protocol event. Finally, it cannot model backward secrecy and key independence. We applied each of the developed methods on a different group protocol from the literature illustrating the features of each approach

Domain Restriction Based Formal Model for Firewall Configurations

Firewalls are widely adopted for protecting private networks by filtering out undesired network traffic in and out of secured networks. Therefore, they play an important role in the security of communication systems. The verification of firewalls is a great challenge because of the dynamic characteristics of their operation, their configuration is highly error prone, and finally, they are considered the first defense to secure networks against attacks and unauthorized access. In this paper, we present a formal model for firewalls rulebase using domain restriction method, and based on this model, a novel algorithm for detecting and identifying conflicts in firewalls rulebase. The algorithm is based on calculating the conflict set of firewall configurations using the domain restriction. The domain restriction method is implemented using Event-B formal techniques, where we model fire-wall configuration rules, and then use invariant checking to verify the consistency of firewall configurations

Big data analytics and internet of things for personalised healthcare: opportunities and challenges

With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future

DASS-CARE 2.0: Blockchain-Based Healthcare Framework for Collaborative Diagnosis in CIoMT Ecosystem

Due to current Covid-19 pandemic, several countries enforce lock-down to prevent pandemic outspread. Hence, the mode of delivery of health services shall change as physical visits are not allowed. As such, the need of tele-medicine service and remote diagnosis have become a necessity. To provide reliable, safe, secure, and sustainable tele-medicine consultancy services, the supporting IT infrastructure need to be transformed. Therefore, it is necessary to use new generation of information technologies such as loT, Blockchain, and cloud computing to transform the traditional medical systems to smart healthcare systems. In this paper, we propose a proof of concept (PoC) of an ameliorated version of our DASS-CARE framework that supports decentralized, accessible, scalable, and secure access to healthcare services based on Internet of Medical things (IoMT) and Artificial Intelligence (AI). In this paper, we propose DASS-CARE 2.0 that offers more medical services including: (a) the real time health monitoring, (b) the collaborative and secure access to medical records, (c) the storage of medical history diagnosis and prescriptions, and (d)the patient\u27s discharge and bills\u27 payments. The paper concludes with future changes to the framework that can furnish further services

Statistical analysis of factors associated with recent traffic accidents dataset: a practical study

In this paper, we propose a logistic model to fit accidents dataset of 10,000 road crash incidents for the Emirate of Abu Dhabi published in 2020. After cleaning up the dataset, we use descriptive and inferential statistical tools to study the attributes of each variable. Then, we identify the main independent variables that can be incorporated in a general logistic regression model which also includes the interactions between them. Our analysis using the significance level of (alpha = 0.05) found that there is a reduced logistic regression model that can fit the data in which the ‘location of accident’ can be represented using ‘type of accident’ and the ‘age’ of people involved in the accidents. Moreover, the results show that the interaction terms are not significant to be included in the model. Furthermore, the study shows that the odds for accidents by young age group (less than 40 years old) in external streets is 27% higher than the odds for internal streets, and that the odds for sequential type accidents in external streets is 13% higher than the odds for internal streets

Rank Functions Based Inference System for Group Key Management Protocols Verification

Design and veri¯cation of cryptographic protocols has been under investigation for quite sometime. However, most of the attention has been paid for two parties protocols. In group key management and distribution protocols, keys are computed dynamically through cooperation of all protocol participants. Therefore regular approaches for two parties protocols veri¯cation cannot be applied on group key protocols. In this paper, we present a framework for formally verifying of group key management and distribution protocols based on the concept of rank functions. We de¯ne a class of rank functions that satisfy speci¯c requirements and prove the soundness of these rank functions. Based on the set of sound rank functions, we provide a sound and complete inference system to detect attacks in group key management protocols. The inference system provides an elegant and natural proof strategy for such protocols compared to existing approaches. The above formalizations and rank theorems were implemented using the PVS theorem prover. We illustrate our approach by applying the inference system on a generic Di±e-Hellman group protocol and prove it in PVS

Probabilistic analysis of security attacks in cloud environment using hidden Markov models

© 2020 John Wiley & Sons, Ltd. The rapidly growing cloud computing paradigm provides a cost-effective platform for storing, sharing, and delivering data and computation through internet connectivity. However, one of the biggest barriers for massive cloud adoption is the growing cybersecurity threats/risks that influence its confidence and feasibility. Existing threat models for clouds may not be able to capture complex attacks. For example, an attacker may combine multiple security vulnerabilities into an intelligent, persistent, and sequence of attack behaviors that will continuously act to compromise the target on clouds. Hence, new models for detection of complex and diversified network attacks are needed. In this article, we introduce an effective threat modeling approach that has the ability to predict and detect the probability of occurrence of various security threats and attacks within the cloud environment using hidden Markov models (HMMs). The HMM is a powerful statistical analysis technique and is used to create a probability matrix based on the sensitivity of the data and possible system components that can be attacked. In addition, the HMM is used to provide supplemental information to discover a trend attack pattern from the implicit (or hidden) raw data. The proposed model is trained to identify anomalous sequences or threats so that accurate and up-to-date information on risk exposure of cloud-hosted services are properly detected. The proposed model would act as an underlying framework and a guiding tool for cloud systems security experts and administrators to secure processes and services over the cloud. The performance evaluation shows the effectiveness of the proposed approach to find attack probability and the number of correctly detected attacks in the presence of multiple attack scenarios

Event-B based invariant checking of secrecy in group key protocols

Abstract—The correctness of group key protocols in commu-nication systems remains a great challenge because of dynamic characteristics of group key construction as we deal with an open number of group members. In this paper, we propose a solution to model group key protocols and to verify their required properties, in particular secrecy property, using the event-B method. Event-B deals with tools allowing invariant checking, and can be used to verify group key secrecy property. We define a well-formed formal link between the group protocol model and the event-B counterpart model. Our approach is applied on a tree-based group Diffie-Hellman protocol that dynamically outputs group keys using the logical structure of a balanced binary tree. I