Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

The Grid 2: Blueprint for a New Computing Infrastructure

Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

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General data protection regulation

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

This paper investigates a critical access control issue in the Internet of Things (IoT). In particular, we propose a smart contract-based framework, which consists of multiple access control contracts (ACCs), one judge contract (JC), and one register contract (RC), to achieve distributed and trustworthy access control for IoT systems. Each ACC provides one access control method for a subject-object pair, and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject. The JC implements a misbehavior-judging method to facilitate the dynamic validation of the ACCs by receiving misbehavior reports from the ACCs, judging the misbehavior and returning the corresponding penalty. The RC registers the information of the access control and misbehavior-judging methods as well as their smart contracts, and also provides functions (e.g., register, update, and delete) to manage these methods. To demonstrate the application of the framework, we provide a case study in an IoT system with one desktop computer, one laptop and two Raspberry Pi single-board computers, where the ACCs, JC, and RC are implemented based on the Ethereum smart contract platform to achieve the access control.

Smart Contract-Based Access Control for the Internet of Things

To realize the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends toward the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy, and personal safety risks that arise beyond these subsystems; i.e., from the wide-scale, cross-platform openness that cloud services bring to IoT. In this paper, we focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyze the current state of cloud-supported IoT to make explicit the security considerations that require further work.

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Twenty Security Considerations for Cloud-Supported Internet of Things

Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions. Information Flow Control (IFC) is a well understood Mandatory Access Control methodology. The earliest IFC models targeted security in a centralised environment, but decentralised forms of IFC have been designed and implemented, often within academic research projects. As a result, there is potential for decentralised IFC to achieve better cloud security than is available today. In this paper we describe the properties of cloud computing—Platform-as-a-Service clouds in particular—and review a range of IFC models and implementations to identify opportunities for using IFC within a cloud computing context. Since IFC security is linked to the data that it protects, both tenants and providers of cloud services can agree on security policy, in a manner that does not require them to understand and rely on the particulars of the cloud software stack in order to effect enforcement.

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Information Flow Control for Secure Cloud Computing

Advanced Persistent Threats (APTs) are difficult to detect due to their "low-and-slow" attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.

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UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats.

Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system's behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.

https://dl.acm.org/doi/pdf/10.1145/3127479.3129249

Practical whole-system provenance capture

A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government, consisting of different departments, provides services to its citizens through a common platform. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows;a crucial issue once data has left its owner’s control by cloud-hosted applications andwithin cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners’ data flow policy with regard to protection and sharing, aswell as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency and offers system-wide visibility over data flows. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification ofpolicy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware.

Thomas Pasquier

Papers

Twenty Security Considerations for Cloud-Supported Internet of Things

Information Flow Control for Secure Cloud Computing

UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats.

Practical whole-system provenance capture

Camflow: Managed Data-Sharing for Cloud Services