Machine learning-based EDoS attack detection technique using execution trace analysis

One of the most important benefits of using cloud computing is the benefit of on-demand services. Accordingly, the method of payment in the cloud environment is pay per use. This feature results in a new kind of DDOS attack called Economic Denial of Sustainability (EDoS), in which the customer pays extra to the cloud provider as a result of the attack. Similar to other DDoS attacks, EDoS attacks are divided into different types, such as (1) bandwidth-consuming attacks, (2) attacks that target specific applications, and 3) connection-layer exhaustion attacks. In this work, we propose a novel framework to detect different types of EDoS attacks by designing a profile that learns from and classifies the normal and abnormal behaviors. In this framework, the extra demanding resources are only allocated to VMs that are detected to be in a normal situation and therefore prevent the cloud environment from attack and resource misuse propagation

Lightweight Scheme for Smart Home Environments using Offloading Technique

Internet of Things (IoT) as an emerging technology has been transforming the different aspects of our world from simple preprogrammed coffee machine to smart farming. Due to the human nature to simplify and ease of living, human are becoming dependent on these automated IoT devices and smart environments like smart phones, wearable devices, smart home and etc. In order to provide better QoS, these devices needs to work together and share data among them, also to the service providers and the cloud. Since these devices are resource constrained, IoT technology heavily depends on the cloud for processing, analytics and storage. But these data coming from the devices contains lot of personal identity information (PII). Almost all the time, the users of these devices are unaware of these information that is being transmitted or they do not possess the control over the data that they are being sent to the service provider, as well as to the cloud. Even the cloud services and service providers are secured but they are always curious. There are lot of security measures implemented for end to end communication but IoT lacks the mechanism for securing the data that the devices are generating along with access control. In this article we are proposing an approach for the security, privacy and access control of user data using Attribute Based Encryption (ABE) in smart home as the case study

Dynamic Matching and Weaving Semantics in \lambda -Calculus

In this chapter, we present a denotational semantics for aspect matching and weaving in lambda-calculus. The proposed semantics is based on the so-called Continuation-Passing Style (CPS) since this style of semantics provides a precise, accurate, and elegant description of aspect-oriented mechanisms. We first formalize semantics for a core language based on lambda-calculus. Afterwards, we extend the semantics by considering flow-based pointcuts, such as control flow and data flow that are important from a security perspective

Aspect-oriented security hardening of UML design models

© Springer International Publishing Switzerland 2015. This book comprehensively presents a novel approach to the systematic security hardening of software design models expressed in the standard UML language. It combines model-driven engineering and the aspect-oriented paradigm to integrate security practices into the early phases of the software development process. To this end, a UML profile has been developed for the specification of security hardening aspects on UML diagrams. In addition, a weaving framework, with the underlying theoretical foundations, has been designed for the systematic injection of security aspects into UML models. The work is organized as follows: chapter 1 presents an introduction to software security, model-driven engineering, UML and aspect-oriented technologies. Chapters 2 and 3 provide an overview of UML language and the main concepts of aspect-oriented modeling (AOM) respectively. Chapter 4 explores the area of model-driven architecture with a focus on model transformations. The main approaches that are adopted in the literature for security specification and hardening are presented in chapter 5. After these more general presentations, chapter 6 introduces the AOM profile for security aspects specification. Afterwards, chapter 7 details the design and the implementation of the security weaving framework, including several real-life case studies to illustrate its applicability. Chapter 8 elaborates an operational semantics for the matching/weaving processes in activity diagrams, while chapters 9 and 10 present a denotational semantics for aspect matching and weaving in executable models following a continuation-passing style. Finally, a summary and evaluation of the work presented are provided in chapter 11. The book will benefit researchers in academia and industry as well as students interested in learning about recent research advances in the field of software security engineering

Static Matching and Weaving Semantics in Activity Diagrams

In this chapter, we present formal specifications for aspect matching and weaving in UML activity diagrams. We formalize both types of adaptations, i.e., add adaptations and remove adaptations. For the join point model, we consider not only executable nodes, i.e., action nodes, but also various control nodes. In addition, we derive algorithms for matching and weaving based on the semantic rules. Finally, we prove the correctness and the completeness of these algorithms with respect to the proposed semantics

Dynamic Matching and Weaving Semantics in Executable UML

In this chapter, we elaborate a denotational semantics for aspect matching and weaving in Executable UML (xUML). More precisely, we specify xUML models using the standard Action Language for Foundational UML (Alf). As we did in the previous chapter, we start by formalizing the matching and the weaving processes for basic pointcuts. Then, we elaborate the semantics for the dataflow pointcut, which is relevant from a security perspective

Security Aspect Specification

In this chapter, we present the AOM profile proposed for the specification of security aspects on UML design models. The proposed profile covers the main UML diagrams that are used in software design, i.e., class diagrams, state machine diagrams, sequence diagrams, and activity diagrams. In addition, it covers most common AOP adaptations, i.e., adding new elements before, after, or around specific points, and removing existing elements. Moreover, we present a high-level and user-friendly pointcut language proposed to designate the locations where aspect adaptations should be injected into base models

Model-Based Security

In this chapter, we present the background related to security at the modeling level. We start by investigating security specification approaches for UML design: (1) using UML artifacts, (2) extending UML meta-language, and (3) creating a new meta-language. Afterwards, we evaluate the usability of these approaches for security specification according to a set of defined criteria. Finally, we overview the main design mechanisms that are adopted for security hardening at the modeling level. These are security design patterns, mechanism-directed meta-languages, and aspect-oriented modeling

Security Aspect Weaving

In this chapter, we present the design and implementation of the proposed security weaving framework. We start by providing a high-level overview that summarizes the main steps and the technologies that are followed to implement the weaving framework. Afterwards, we present the details of each weaving step. The proposed weaver is implemented as a model-to-model (M2M) transformation using the OMG standard Query/View/Transformation (QVT) language. In addition, it covers all the diagrams that are supported by our approach, i.e., class diagrams, state machine diagrams, activity diagrams, and sequence diagrams. For each diagram, we provide algorithms that implement its corresponding weaving adaptations. Moreover, we present the transformation rules that implement each aspect adaptation rule

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution