The recent evolution of the Internet of Things in the healthcare sector has attained substantial recognition from the government and industry. Healthcare data accumulated from diverse sources are stored by health service providers which is useful for patient diagnosis as well as for research for pivotal analysis. However, healthcare data contains sensitive information of an individual that needs to be protected. The sensitive health information of an individual contains multiple attributes and the correlation of this information may lead to a privacy breach. In more complex scenarios, such type of data is expected to be released periodically and dynamically, the privacy breach of individuals becomes imminent due to the presence of personal data. In this article, a fuzzy logic-based intelligent privacy-aware algorithm is proposed to protect the individual’s privacy with multiple sensitive attributes in a dynamic data release scenario. Our formal modeling and analysis show that the proposed approach offers a robust privacy guarantee while releasing health-related data. Furthermore, the empirical evaluation exhibits that the proposed model outperforms the state-of-the-art approaches in terms of information loss and query accuracy.

Cloud computing is now being utilized as a prospective alternative for catering storage service. Security issues of cloud storage are a potential deterrent in its widespread adoption. Privacy breach, malicious modification, and data loss are emerging cyber threats against cloud storage. Recently, a fog server based three-layer architecture has been presented for secure storage employing multiple clouds. The underlying techniques used are Hash-Solomon code and customized hash algorithm in order to attain the goal. However, it resulted in loss of smaller portion of data to cloud servers and failed to provide better modification detection and data recoverability. This paper proposes a novel fog-centric secure cloud storage scheme to protect data against unauthorized access, modification, and destruction. To prevent illegitimate access, the proposed scheme employs a new technique$Xor - Combination$to conceal data. Moreover,$Block - Management$outsources the outcomes of$Xor - Combination$to prevent malicious retrieval and to ensure better recoverability in case of data loss. Simultaneously, we propose a technique based on hash algorithm in order to facilitate modification detection with higher probability. We demonstrate robustness of the proposed scheme through security analysis. Experimental results validate performance supremacy of the proposed scheme compared to contemporary solutions in terms of data processing time.

Cloud computing has a shared set of resources, including physical servers, networks, storage, and user applications. Resource allocation is a critical issue for cloud computing, especially in Infrastructure-as-a-Service (IaaS). The decision-making process in the cloud computing network is non-trivial as it is handled by switches and routers. Moreover, the network concept drifts resulting from changing user demands are among the problems affecting cloud computing. The cloud data center needs agile and elastic network control functions with control of computing resources to ensure proper virtual machine (VM) operations, traffic performance, and energy conservation. Software-Defined Network (SDN) proffers new opportunities to blueprint resource management to handle cloud services allocation while dynamically updating traffic requirements of running VMs. The inclusion of an SDN for managing the infrastructure in a cloud data center better empowers cloud computing, making it easier to allocate resources. In this survey, we discuss and survey resource allocation in cloud computing based on SDN. It is noted that various related studies did not contain all the required requirements. This study is intended to enhance resource allocation mechanisms that involve both cloud computing and SDN domains. Consequently, we analyze resource allocation mechanisms utilized by various researchers; we categorize and evaluate them based on the measured parameters and the problems presented. This survey also contributes to a better understanding of the core of current research that will allow researchers to obtain further information about the possible cloud computing strategies relevant to IaaS resource allocation.

User activity logs can be a valuable source of information in cloud forensic investigations; hence, ensuring the reliability and integrity of such logs is crucial. Most existing solutions for secure logging are designed for conventional systems rather than the complexity of a cloud environment. In this paper, we propose the Cloud Log Assuring Soundness and Secrecy (CLASS) process as an alternative scheme for the securing of logs in a cloud environment. In CLASS, logs are encrypted using the individual user's public key so that only the user is able to decrypt the content. In order to prevent unauthorized modification of the log, we generate proof of past log (PPL) using Rabin's fingerprint and Bloom filter. Such an approach reduces verification time significantly. Findings from our experiments deploying CLASS in OpenStack demonstrate the utility of CLASS in a real-world context.

The use of bot malware and botnets as a tool to facilitate other malicious cyber activities (e.g. distributed denial of service attacks , dissemination of malware and spam , and click fraud). However, detection of botnets , particularly peer-to-peer (P2P) botnets, is challenging. Hence, in this paper we propose a sophisticated traffic reduction mechanism, integrated with a reinforcement learning technique . We then evaluate the proposed approach using real-world network traffic, and achieve a detection rate of 98.3%. The approach also achieves a relatively low false positive rate (i.e. 0.012%).

Nowadays, trust and reputation models are used to build a wide range of trust-based security mechanisms and trust-based service management applications on the Internet of Things (IoT). Considering trust as a single unit can result in missing important and significant factors. We split trust into its building-blocks, then we sort and assign weight to these building-blocks (trust metrics) on the basis of its priorities for the transaction context of a particular goal. To perform these processes, we consider trust as a multi-criteria decision-making problem, where a set of trust worthiness metrics represent the decision criteria. We introduce Entropy-based fuzzy analytic hierarchy process (EFAHP) as a trust model for selecting a trustworthy service provider, since the sense of decision making regarding multi-metrics trust is structural. EFAHP gives 1) fuzziness, which fits the vagueness, uncertainty, and subjectivity of trust attributes; 2) AHP, which is a systematic way for making decisions in complex multi-criteria decision making; and 3) entropy concept, which is utilized to calculate the aggregate weights for each service provider. We present a numerical illustration in trust-based Service Oriented Architecture in the IoT (SOA-IoT) to demonstrate the service provider selection using the EFAHP Model in assessing and aggregating the trust scores.

High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination by considering the traffic segment status when choosing the next intersection. RTISAR presents a new formula for assessing segment status based on connectivity, density, load segment, and cumulative distance toward the destination. A verity period mechanism is proposed to denote the projected period when a network failure is likely to occur in a particular segment. This mechanism can be calculated for each collector packet to minimize the frequency of RTISAR execution and to control the generation of collector packets. As a result, this mechanism minimizes the communication overhead generated during the segment status computation process. Simulations are performed to evaluate RTISAR, and the results are compared with those of intersection-based connectivity aware routing and traffic flow-oriented routing. The evaluation results provided evidence that RTISAR outperforms in terms of packet delivery ratio, packet delivery delay, and communication overhead.

Wireless sensor networks (WSNs) have captivated substantial attention from both industrial and academic research in the last few years. The major factor behind the research efforts in that field is their vast range of applications which include surveillance systems, military operations, health care, environment event monitoring, and human safety. However, sensor nodes are low potential and energy constrained devices; therefore, energy‐efficient routing protocol is the foremost concern. In this paper, an energy‐efficient routing protocol for wireless sensor networks is proposed. Our protocol consists of a routing algorithm for the transmission of data, cluster head selection algorithm, and a scheme for the formation of clusters. On the basis of energy analysis of the existing routing protocols, a multistage data transmission mechanism is proposed. An efficient cluster head selection algorithm is adopted and unnecessary frequency of reclustering is exterminated. Static clustering is used for efficient selection of cluster heads. The performance and energy efficiency of our proposed routing protocol are assessed by the comparison of the existing routing protocols on a simulation platform. On the basis of simulation results, it is observed that our proposed routing protocol (EE‐MRP) has performed well in terms of overall network lifetime, throughput, and energy efficiency.

The Vehicular Ad-hoc Networks (VANET) raises as an emerging technology for smart transport as observed in the recent decade. However, there are some hurdles affect the applications of VANET, the advancement of VANET needed to cope up with the requirement of smart transport in smart cities. The routing is the important factor for having effective communication between smart vehicles, which need to be addressed smartly. Many factors affect the communication between the vehicles such as topology fragmentation which results in the phenomenon of Local-Maximum-Problem that lead to packet delivery failure. The traditional routing schemes failed to address the problem of the trade-off between Packet-Delivery-Ratio and the high cost, in term of End-to-End-Delay, of packet recovery from the failures that occurs frequently because of Local-Maximum-Problem. Therefore, this article works out to analyses the performance of existing position-based routing protocols for Inter-vehicle ad-hoc network and introduced Intersection-based Distance and Traffic-Aware Routing (IDTAR) protocol. IDTAR aims to provide optimal performance in environments of smart transport in smart cities. The experiments evaluate IDTAR against famous position-based routing protocols such as GyTAR, A-STAR-SR and GSR considering several densities of vehicles. Simulation result shows that IDTAR protocol has a fewer End-to-End-Delay and high packet delivery ratio in smart cities circumstances. This concludes that IDTAR can be adaptive for smart transport in smart cities communication with some consideration in terms of its security, compatibility and reliability.

Software-defined networking (SDN) is new network architecture that emerges as to implement network virtualization (NV) with vast features, especially when it is applied it in multi-tenant scenarios. The rapid growth of wireless network applications and services, let to adopt NVs into software-defined wireless network (SDWN). This is because wireless networks require specific features that can be hindered of implementing NVs such as updated location information, dynamic channel configuration, and rapid client re-association. This paper presents state-of-the-art NV methods for SDWN with the aim of highlighting issues and challenges of applying NVs techniques of SDN into SDWN. We discuss three SDN techniques that facilitate NV in the cloud, namely proxy-based virtualization, layer two prefixes-based virtualizations and programing language-based virtualization. Moreover, the paper points out the possibility of providing effective VNs in the SDWN architecture. We also taxonomies the SDWN proposed virtualization methods based on hypervisor controller in the different networks. Finally, the potential requirements and challenges and open issues of SDWN NVs are also identified and presented as the future directions in SDWN research.

Abstract Cloud computing represents the most recent enterprise trend in information technology and refers to the virtualization of computing resources that are available on demand. Cloud computing saves cost and time for businesses. Moreover, this computing process reflects a radical technological revolution in how companies develop, deploy, and manage enterprise applications over the Internet. Virtualized cloud computing mainly offers cloud‐computing delivery models such as software as a service, platform as a service, and infrastructure as a service. Security and privacy are presently considered critical factors in the adaptation of any cloud‐service delivery model. Cloud computing leverages several technologies; in the process, this model can inherit potential security threats. Thus far, security issues in cloud computing have rarely been addressed at the service delivery level. Key security concerns include Web application security, network security, data security, integration, vulnerabilities in the virtualized environment, and physical security. The aim of this research is to comprehensively present the security threats with respect to their cloud service deliver models. This study also determines how service delivery models differ from existing enterprise applications, classify these models, and investigate the inherent security challenges. This study primarily focuses on the security concerns of each layer of the cloud‐service delivery model, as well as on existing solutions and approaches. In addition, countermeasures to potential security threats are also presented for each cloud model. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregation process. In this paper, we have highlighted the gains of the existing schemes for node clustering‐based data aggregation along with a detailed discussion on their advantages and issues that may degrade the performance. Also, the boundary issues in each type of clustering technique have been analyzed. Simulation results reveal that the efficacy and validity of these clustering‐based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in various other environmental conditions. Copyright © 2016 John Wiley & Sons, Ltd.

Technological advancement of various video and image processing tools has made tempering of digital video easy and faster. This review paper focuses on passive techniques that are employed for detecting forgeries in a digital video. Passive forgery detection techniques are methods used for detecting the authenticity of a video without depending on pre-embedded information. The techniques exploit the use of statistical or mathematical properties that are distorted as a result of video tempering for forgery detection. Passive video forgery detection approach has a great prospect in multimedia security, information security and pattern recognition. In this paper, we divide passive techniques for video forensics into three categories; Statistical correlation of video features, frame-based for detecting statistical anomalies, and the inconsistency features of different digital equipment. The discussion also covers the trends, limitations and idea for improvements of passive forgery detection techniques.

In grid computing, the realization of an enviable fault tolerance ability is linked with the proper utilization of resources and scheduling of jobs. The literature offers two solutions to these two challenging tasks, viz. checkpointing and replication. A checkpointing strategy is being proposed that uses the median of failure intervals of the resources in deciding the checkpoint intervals for the given jobs. The strategy shows improved system throughput, job losses and job execution times while eliminating unnecessary checkpoints.