Special issue on entropy-based applied cryptography and enhanced security for ubiquitous computing

Entropy is a basic and important concept in information theory. It is also often used as a measure of the unpredictability of a cryptographic key in cryptography research areas. Ubiquitous computing (Ubi-comp) has emerged rapidly as an exciting new paradigm. In this special issue, we mainly selected and discussed papers related with ore theories based on the graph theory to solve computational problems on cryptography and security, practical technologies; applications and services for Ubi-comp including secure encryption techniques, identity and authentication; credential cloning attacks and countermeasures; switching generator with resistance against the algebraic and side channel attacks; entropy-based network anomaly detection; applied cryptography using chaos function, information hiding and watermark, secret sharing, message authentication, detection and modeling of cyber attacks with Petri Nets, and quantum flows for secret key distribution, etc

Fast-Sec: an approach to secure Big Data processing in the cloud

Group Security is an important concern in computer systems, which is especially remarkable when the system has to handle large amounts of data and some different users accessing this data with different accessing permissions. This work proposes an innovative approach for providing a security infrastructure support to Big Data Analytic in Cloud-based systems named Fast-sec. Fast-Sec handles systems with large volumes of data from heterogeneous sources, in which users may access the system by different platforms, consuming or providing data. The security infrastructure proposed in Fast-Sec provides an authentication mechanism for users, and data access control adapted to high demands from cloud-based Big Data environment. The reported results show the adequacy of the proposed safety infrastructure to the cloud-based systems processing Big Data. © 2017 Informa UK Limited, trading as Taylor & Franci

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

The twofold role of Cloud Computing in Digital Forensics: target of investigations and helping hand to evidence analysis

This PhD thesis discusses the impact of Cloud Computing infrastructures on Digital Forensics in the twofold role of target of investigations and as a helping hand to investigators. The Cloud offers a cheap and almost limitless computing power and storage space for data which can be leveraged to commit either new or old crimes and host related traces. Conversely, the Cloud can help forensic examiners to find clues better and earlier than traditional analysis applications, thanks to its dramatically improved evidence processing capabilities. In both cases, a new arsenal of software tools needs to be made available. The development of this novel weaponry and its technical and legal implications from the point of view of repeatability of technical assessments is discussed throughout the following pages and constitutes the unprecedented contribution of this wor

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Secure Schemes for Semi-Trusted Environment

In recent years, two distributed system technologies have emerged: Peer-to-Peer (P2P) and cloud computing. For the former, the computers at the edge of networks share their resources, i.e., computing power, data, and network bandwidth, and obtain resources from other peers in the same community. Although this technology enables efficiency, scalability, and availability at low cost of ownership and maintenance, peers defined as ``like each other'' are not wholly controlled by one another or by the same authority. In addition, resources and functionality in P2P systems depend on peer contribution, i.e., storing, computing, routing, etc. These specific aspects raise security concerns and attacks that many researchers try to address. Most solutions proposed by researchers rely on public-key certificates from an external Certificate Authority (CA) or a centralized Public Key Infrastructure (PKI). However, both CA and PKI are contradictory to fully decentralized P2P systems that are self-organizing and infrastructureless. To avoid this contradiction, this thesis concerns the provisioning of public-key certificates in P2P communities, which is a crucial foundation for securing P2P functionalities and applications. We create a framework, named the Self-Organizing and Self-Healing CA group (SOHCG), that can provide certificates without a centralized Trusted Third Party (TTP). In our framework, a CA group is initialized in a Content Addressable Network (CAN) by trusted bootstrap nodes and then grows to a mature state by itself. Based on our group management policies and predefined parameters, the membership in a CA group is dynamic and has a uniform distribution over the P2P community; the size of a CA group is kept to a level that balances performance and acceptable security. The muticast group over an underlying CA group is constructed to reduce communication and computation overhead from collaboration among CA members. To maintain the quality of the CA group, the honest majority of members is maintained by a Byzantine agreement algorithm, and all shares are refreshed gradually and continuously. Our CA framework has been designed to meet all design goals, being self-organizing, self-healing, scalable, resilient, and efficient. A security analysis shows that the framework enables key registration and certificate issue with resistance to external attacks, i.e., node impersonation, man-in-the-middle (MITM), Sybil, and a specific form of DoS, as well as internal attacks, i.e., CA functionality interference and CA group subversion. Cloud computing is the most recent evolution of distributed systems that enable shared resources like P2P systems. Unlike P2P systems, cloud entities are asymmetric in roles like client-server models, i.e., end-users collaborate with Cloud Service Providers (CSPs) through Web interfaces or Web portals. Cloud computing is a combination of technologies, e.g., SOA services, virtualization, grid computing, clustering, P2P overlay networks, management automation, and the Internet, etc. With these technologies, cloud computing can deliver services with specific properties: on-demand self-service, broad network access, resource pooling, rapid elasticity, measured services. However, theses core technologies have their own intrinsic vulnerabilities, so they induce specific attacks to cloud computing. Furthermore, since public clouds are a form of outsourcing, the security of users' resources must rely on CSPs' administration. This situation raises two crucial security concerns for users: locking data into a single CSP and losing control of resources. Providing inter-operations between Application Service Providers (ASPs) and untrusted cloud storage is a countermeasure that can protect users from lock-in with a vendor and losing control of their data. To meet the above challenge, this thesis proposed a new authorization scheme, named OAuth and ABE based authorization (AAuth), that is built on the OAuth standard and leverages Ciphertext-Policy Attribute Based Encryption (CP-ABE) and ElGamal-like masks to construct ABE-based tokens. The ABE-tokens can facilitate a user-centric approach, end-to-end encryption and end-to-end authorization in semi-trusted clouds. With these facilities, owners can take control of their data resting in semi-untrusted clouds and safely use services from unknown ASPs. To this end, our scheme divides the attribute universe into two disjointed sets: confined attributes defined by owners to limit the lifetime and scope of tokens and descriptive attributes defined by authority(s) to certify the characteristic of ASPs. Security analysis shows that AAuth maintains the same security level as the original CP-ABE scheme and protects users from exposing their credentials to ASP, as OAuth does. Moreover, AAuth can resist both external and internal attacks, including untrusted cloud storage. Since most cryptographic functions are delegated from owners to CSPs, AAuth gains computing power from clouds. In our extensive simulation, AAuth's greater overhead was balanced by greater security than OAuth's. Furthermore, our scheme works seamlessly with storage providers by retaining the providers' APIs in the usual way

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

AIRM: a new AI Recruiting Model for the Saudi Arabian labour market

One of the goals of Saudi Vision 2030 is to keep the unemployment rate at the lowest level to empower the economy. Prior research has shown that an increase in unemployment has a negative effect on a country’s Gross Domestic Product. This research aims to utilise cutting-edge technology such as Data Lake (DL), Machine Learning (ML) and Artificial Intelligence (AI) to assist the Saudi labour market bymatching job seekers with vacant positions. Currently, human experts carry out this process; however, this is time consuming and labour intensive. Moreover, in the Saudi labour market, this process does not use a cohesive data centre to monitor, integrate, or analyse labour market data, resulting in inefficiencies, such as bias and latency. These inefficiencies arise from a lack of technologies and, more importantly, from having an open labour market without a national labour market data centre. This research proposes a new AI Recruiting Model (AIRM) architecture that exploits DLs, ML and AI to rapidly and efficiently match job seekers to vacant positions in the Saudi labour market. A Minimum Viable Product (MVP) is employed to test the proposed AIRM architecture using a labour market dataset simulation corpus for training purposes; the architecture is further evaluated against three research-collaborative Human Resources (HR) professionals. As this research is data-driven in nature, it requires collaboration from domain experts. The first layer of the AIRM architecture uses balanced iterative reducing and clustering using hierarchies (BIRCH) as a clustering algorithm for the initial screening layer. The mapping layer uses sentence transformers with a robustly optimised BERTt pre-training approach (RoBERTa) as the base model, and ranking is carried out using the Facebook AI Similarity Search (FAISS). Finally, the preferences layer takes the user’s preferences as a list and sorts the results using the pre-trained cross-encoders model, considering the weight of the more important words. This new AIRM has yielded favourable outcomes: This research considered accepting an AIRM selection ratified by at least one HR expert to account for the subjective character of the selection process when exclusively handled by human HR experts. The research evaluated the AIRM using two metrics: accuracy and time. The AIRM had an overall matching accuracy of 84%, with at least one expert agreeing with the system’s output. Furthermore, it completed the task in 2.4 minutes, whereas human experts took more than six days on average. Overall, the AIRM outperforms humans in task execution, making it useful in pre-selecting a group of applicants and positions. The AIRM is not limited to government services. It can also help any commercial business that uses Big Data