Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems

In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system

Privacy Preserving Attribute Based Encryption for Multiple Cloud Collaborative Environment

In a Multiple Cloud Collaborative Environment (MCCE), cloud users and cloud providers interact with each other via a brokering service to request and provision cloud services. The brokering service considers several pieces of data to broker the best deal between users and providers which can subsequently risks the privacy and security of MCCE. In this paper, we propose a Privacy Preserving Attribute-Based Encryption(PPABE) scheme which protects MCCE from a compromised broker. The proposed encryption scheme preserves the privacy by employing data access policy over sets of attributes. The identifying attributes are anonymoized using pseudonyms. The data access policy is further anonymized so as it remain unknown to unauthorized parties. The PP-ABE achieves unlinkability between different data items which flows through the collaborative cloud environment and preserves the privacy of cloud users and cloud providers

A Lightweight Attribute-based Security Scheme for Fog-Enabled Cyber Physical Systems

In this paper, a lightweight attribute-based security scheme based on elliptic curve cryptography (ECC) is proposed for fog-enabled cyber physical systems (Fog-CPS). A novel aspect of the proposed scheme is that the communication between Fog-CPS entities is secure even when the certification authority (CA) is compromised. This is achieved by dividing the attributes into two sets, namely, secret and shared, and subsequently generating two key pairs, referred to as the partial and final key pairs, for each entity of the Fog-CPS system. Unlike existing attribute-based encryption (ABE) and identity-based encryption schemes, in the proposed scheme, each entity calculates the final public key of the communicating CPS devices without the need of generating and transmitting digital certificates. Moreover, the proposed security scheme considers an efficient and secure key pair update approach in which the calculation overhead is limited to one group element. To show the effectiveness of the proposed scheme, we have calculated and compared the memory and processing complexity with other bilinear and elliptic curve schemes. We have also implemented our scheme in a Raspberry Pi (3B+ model) for CPS simulations. The proposed scheme guarantees the confidentiality, integrity, privacy, and authenticity in Fog-CPS systems

A Secure Integrated Framework for Fog-Assisted Internet of Things Systems

Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices

Fractional flow reserve vs. angiography in guiding management to optimize outcomes in non-ST-segment elevation myocardial infarction: the British Heart Foundation FAMOUS-NSTEMI randomized trial

Aim: We assessed the management and outcomes of non-ST segment elevation myocardial infarction (NSTEMI) patients randomly assigned to fractional flow reserve (FFR)-guided management or angiography-guided standard care. Methods and results: We conducted a prospective, multicentre, parallel group, 1 : 1 randomized, controlled trial in 350 NSTEMI patients with ≥ coronary stenosis ≥30% of the lumen diameter assessed visually (threshold for FFR measurement) (NCT01764334). Enrolment took place in six UK hospitals from October 2011 to May 2013. Fractional flow reserve was disclosed to the operator in the FFR-guided group (n = 176). Fractional flow reserve was measured but not disclosed in the angiography-guided group (n = 174). Fractional flow reserve ≤0.80 was an indication for revascularization by percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG). The median (IQR) time from the index episode of myocardial ischaemia to angiography was 3 (2, 5) days. For the primary outcome, the proportion of patients treated initially by medical therapy was higher in the FFR-guided group than in the angiography-guided group [40 (22.7%) vs. 23 (13.2%), difference 95% (95% CI: 1.4%, 17.7%), P = 0.022]. Fractional flow reserve disclosure resulted in a change in treatment between medical therapy, PCI or CABG in 38 (21.6%) patients. At 12 months, revascularization remained lower in the FFR-guided group [79.0 vs. 86.8%, difference 7.8% (−0.2%, 15.8%), P = 0.054]. There were no statistically significant differences in health outcomes and quality of life between the groups. Conclusion: In NSTEMI patients, angiography-guided management was associated with higher rates of coronary revascularization compared with FFR-guided management. A larger trial is necessary to assess health outcomes and cost-effectiveness

Factors associated with low birthweight in term pregnancies: A matched case-control study from rural Pakistan

Low birthweight (LBW) remains a significant public health problem in Pakistan and further understanding of factors associated with LBW is required. We conducted a hospital-based matched case control study to identify risk factors associated with LBW in a rural district of Pakistan. We found that illiteracy (AOR: 2.68; 95% CI: 1.59 - 4.38), nulliparity (AOR: 1.82; 95% CI: 1.26-2.44), having a previous miscarriage/abortion (AOR: 1.22; 95% CI: 1.06-2.35), having \u3c 2 antenatal care (ANC) visits during last pregnancy (AOR: 2.43; 95% CI: 1.34-2.88), seeking ANC in third trimester (AOR: 3.62; 95% CI : 2.14-5.03), non-use of iron folic acid during last pregnancy (AOR: 2.72; 95% CI: 1.75-3.17), having hypertension during last pregnancy (AOR: 1.42; 95% CI: 1.13-2.20), being anemic (AOR: 2.67; 95% CI: 1.65-5.24) and having postpartum weight o

Reinforcing the role of the conventional C-arm - a novel method for simplified distal interlocking

<p>Abstract</p> <p>Background</p> <p>The common practice for insertion of distal locking screws of intramedullary nails is a freehand technique under fluoroscopic control. The process is technically demanding, time-consuming and afflicted to considerable radiation exposure of the patient and the surgical personnel. A new concept is introduced utilizing information from within conventional radiographic images to help accurately guide the surgeon to place the interlocking bolt into the interlocking hole. The newly developed technique was compared to conventional freehand in an operating room (OR) like setting on human cadaveric lower legs in terms of operating time and radiation exposure.</p> <p>Methods</p> <p>The proposed concept (guided freehand), generally based on the freehand gold standard, additionally guides the surgeon by means of visible landmarks projected into the C-arm image. A computer program plans the correct drilling trajectory by processing the lens-shaped hole projections of the interlocking holes from a single image. Holes can be drilled by visually aligning the drill to the planned trajectory. Besides a conventional C-arm, no additional tracking or navigation equipment is required.</p> <p>Ten fresh frozen human below-knee specimens were instrumented with an Expert Tibial Nail (Synthes GmbH, Switzerland). The implants were distally locked by performing the newly proposed technique as well as the conventional freehand technique on each specimen. An orthopedic resident surgeon inserted four distal screws per procedure. Operating time, number of images and radiation time were recorded and statistically compared between interlocking techniques using non-parametric tests.</p> <p>Results</p> <p>A 58% reduction in number of taken images per screw was found for the guided freehand technique (7.4 ± 3.4) (mean ± SD) compared to the freehand technique (17.6 ± 10.3) (<it>p </it>< 0.001). Total radiation time (all 4 screws) was 55% lower for the guided freehand technique compared to conventional freehand (<it>p </it>= 0.001). Operating time per screw (from first shot to screw tightened) was on average 22% reduced by guided freehand (<it>p </it>= 0.018).</p> <p>Conclusions</p> <p>In an experimental setting, the newly developed guided freehand technique for distal interlocking has proven to markedly reduce radiation exposure when compared to the conventional freehand technique. The method utilizes established clinical workflows and does not require cost intensive add-on devices or extensive training. The underlying principle carries potential to assist implant positioning in numerous other applications within orthopedics and trauma from screw insertions to placement of plates, nails or prostheses.</p

A novel framework for intelligent surveillance system based on abnormal human activity detection in academic environments

Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system thatcan perform robustly in an academic environment has become an urgent need. In this paper, we propose a novel framework for an automatic real-time video-based surveillance system which can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment. To develop our system, we have divided the work into three phases: preprocessing phase, abnormal human activity detection phase, and content-based image retrieval phase. For motion object detection, we used the temporal-differencing algorithm and then located the motions region using the Gaussian function.Furthermore, the shape model based on OMEGA equation was used as a filter for the detected objects (i.e.,human and non-human). For object activities analysis, we evaluated and analyzed the human activities of the detected objects. We classified the human activities into two groups:normal activities and abnormal activities based on the support vector machine. The machine then provides an automatic warning in case of abnormal human activities. It also embeds a method to retrieve the detected object from the database for object recognition and identification using content-based image retrieval.Finally,a software-based simulation using MATLAB was performed and the results of the conducted experiments showed an excellent surveillance system that can simultaneously perform the tracking, semantic scene learning, and abnormality detection in an academic environment with no human intervention