Background: Cognitive Muscular TherapyTM (CMT) is an integrated behavioural intervention developed for knee osteoarthritis. CMT teaches patients to reconceptualise the condition, integrates muscle biofeedback and aims to reduce muscle overactivity, both in response to pain and during daily activities. This nested qualitative study explored patient and physiotherapist perspectives and experiences of CMT.Methods: Five physiotherapists were trained to follow a well-defined protocol and then delivered CMT to at least two patients with knee osteoarthritis. Each patient received seven individual clinical sessions and was provided with access to online learning materials incorporating animated videos. Semi-structured interviews took place after delivery/completion of the intervention and data were analysed at the patient and physiotherapist level.Results: Five physiotherapists and five patients were interviewed. All described a process of changing beliefs throughout their engagement with CMT. A framework with three phases was developed to organise the data according to how osteoarthritis was conceptualised and how this changed throughout their interactions with CMT. Firstly, was an identification of pain beliefs to be challenged and recognition of how current beliefs can misalign with daily experiences. Secondly was a process of challenging and changing beliefs, validated through new experiences. Finally, there was an embedding of changed beliefs into self-management to continue with activities. Conclusion:This study identified a range of psychological changes which occur during exposure to CMT. These changes enabled patients to reconceptualise their condition, develop a new understanding of their body, understand psychological processes, and make sense of their knee pain.

Attribute-Based Encryption (ABE) could be an effective cryptographic tool for the secure management of Internet-of-Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero, and concludes that adopting ABE in the IoT is indeed feasible.Accepted for publication --IEEE Micro Special Issue on Internet of Things (2016) Preprint versionAttribute-Based Encryption and IoT. In recent years, several security protocols adopted Attribute-Based Encryption (ABE) as a building block in different distributed environments [3], such as IoT [4], cloud services [5], and medical systems [6]. ABE is a public key scheme where both encryption and decryption are based on high-level data access policies. Considering the aforementioned requirements in distributed and heterogeneous IoT scenarios, ABE provides more efficient access control mechanism compared to conventional cryptographic algorithms [3], [6], [7]: (i) allows fine-grained access control based on recipients' attributes; (ii) scales independent from the number of authorized users; (iii) is resilient against collusion attacks; (iv) does not require key sharing or key management algorithms between the participating parties (data owner does not need to identify the destination client). Besides its noteworthy advantages, a proper key revocation algorithm is still a challenging issue in ABE (beyond the scope of this paper), and an ongoing research effort [3]. More relevant to our work, ABE suffers from high computational overhead [6], [8]. However, the literature is still missing a proper assessment of ABE efficiency on resource-constrained devices, widely used in the IoT domain.In order to shine a light on the feasibility of ABE in IoT, we perform a comprehensive analysis of the cost of ABE operations on resource-constrained devices. In particular, along the same line of our previous work [7], which investigated the feasibility of ABE on smartphone devices, in this paper we implement the original Key-Policy Attribute-Based Encryption (KP-ABE) [9] and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) [10] on widely used IoT-enabling devices. Our work focuses on the evaluation of encryption and decryption (hereinafter called cryptographic operations ) on four boards: Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. Due to space limitation, we only report the results for CP-ABE. However, we noticed that the KP-ABE experiments have a very similar quantitative behavior to CP-ABE results. Supported by our observations from thorough experimental results, we provide evidence of the feasibility of adopting ABE on resource-constrained devices. Moreover, we present a smart healthcare use case application to evaluate feasibility of using ABE in real world IoT scenarios.

IEEE/ACM Transactions on Networking\ud 29 November 2016\ud LineSwitch: Tackling Control Plane Saturation Attacks in Software-Defined Networking ( Articles not published yet, but available online Article in press About articles in press (opens in a new window) )\ud Ambrosin, M., \ud Conti, M., \ud De Gaspari, F., \ud Poovendran, R.\ud Abstract\ud \ud Software defined networking (SDN) is a new networking paradigm that in recent years has revolutionized network architectures. At its core, SDN separates the data plane, which provides data forwarding functionalities, and the control plane, which implements the network control logic. The separation of these two components provides a virtually centralized point of control in the network, and at the same time abstracts the complexity of the underlying physical infrastructure. Unfortunately, while promising, the SDN approach also introduces new attacks and vulnerabilities. Indeed, previous research shows that, under certain traffic conditions, the required communication between the control and data plane can result in a bottleneck. An attacker can exploit this limitation to mount a new, network-wide, type of denial of service attack, known as the control plane saturation attack. This paper presents LineSwitch, an efficient and effective data plane solution to tackle the control plane saturation attack. LineSwitch employs probabilistic proxying and blacklisting of network traffic to prevent the attack from reaching the control plane, and thus preserve network functionality. We implemented LineSwitch as an extension of the reference SDN implementation, OpenFlow, and run a thorough set of experiments under different traffic and attack scenarios. We compared LineSwitch to the state of the art, and we show that it provides at the same time, the same level of protection against the control plane saturation attack, and a reduced time overhead by up to 30%

Attribute-Based Encryption (ABE) is a powerful cryptographic tool that allows fine-grained access control over data. Due to its features, ABE has been adopted in several applications, such as encrypted storage or access control systems.Recently, researchers argued about the non acceptable performance of ABE when implemented on mobile devices. Indeed, the non feasibility of ABE on mobile devices would hinder the deployment of novel protocols and services-that could instead exploit the full potential of such devices. However, we believe the conclusion of non usability was driven by a not-very efficient implementation.In this paper, we want to shine a light on this concern by studying the feasibility of applying ABE on smartphone devices. In particular, we implemented AndrABEn, an ABE library for Android operating system. Our library is written in the C language and implements two main ABE schemes: Ciphertext-Policy Attribute-Based Encryption, and Key-Policy Attribute-Based Encryption. We also run a thorough set of experimental evaluation for AndrABEn, and compare it with the current state-of-the-art (considering the same experimental setting). The results confirm the possibility to effectively use ABE on smartphone devices, requiring an acceptable amount of resources in terms of computations and energy consumption. Since the current state-of-the-art claims the non feasibility of ABE on mobile devices, we believe that our study (together with the AndrABEn library that we made available online) is a key result that will pave the way for researchers and developers to design and implement novel protocols and applications for mobile devices.

Remote attestation protocols are widely used to detect device configuration (e.g., software and/or data) compromise in Internet of Things (IoT) scenarios. Unfortunately, the performances of such protocols are unsatisfactory when dealing with thousands of smart devices. Recently, researchers are focusing on addressing this limitation. The approach is to run attestation in a collective way, with the goal of reducing computation and communication. Despite these advances, current solutions for attestation are still unsatisfactory because of their complex management and strict assumptions concerning the topology (e.g., being time invariant or maintaining a fixed topology). In this paper, we propose PADS, a secure, efficient, and practical protocol for attesting potentially large networks of smart devices with unstructured or dynamic topologies. PADS builds upon the recent concept of non-interactive attestation, by reducing the collective attestation problem into a minimum consensus one. We compare PADS with a state-of-the art collective attestation protocol and validate it by using realistic simulations that show practicality and efficiency. The results confirm the suitability of PADS for low-end devices, and highly unstructured networks. 5