The emergence of beyond 5G and 6G networks is set to revolutionise telecommunications, addressing the demands of emerging applications through advanced capabilities. At the core of this transformation lies next-generation intelligent service orchestration, which is essential for meeting future Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) such as ultra-low latency, efficient power consumption and resource utilisation. These capabilities require multi-objective, seamless end-to-end service delivery across complex, distributed environments. Achieving such delivery requires scalable and modular system design approaches that support dynamic service composition and adaptability. Cloud-native technologies, underpinned by microservices architectures, plays a pivotal role, but also will introduce challenges in orchestrating resources efficiently across heterogeneous domains. To address these challenges, this paper proposes a solution, Federated Intelligent multi-objective Service function chain Orchestration (FISO) that integrates multi-objective federated profiling to preserve privacy while ensuring efficient end-to-end service delivery. FISO integrates Federated Learning (FL) and Reinforcement Learning (RL). FL is used to collaboratively learn from distributed edge profiling clients without sharing raw data, while RL dynamically guides optimal decision making for resource allocation and Service Function Chain (SFC) placement based on feedback from the federated models. FISO predicts optimal computing and network resources for SFCs, enabling the selection of appropriate edge locations, efficient resource allocation, placement of SFCs, and lifecycle management. Experimental results demonstrated on a pragmatic testbed validate the effectiveness of FISO in efficiently placing requested SFCs within an administrative domain with multiple edge/cloud nodes, predicting optimal CPU, memory, and link capacity resources, and minimising end-to-end latency and energy consumption.

Federated Learning (FL) enables collaborative model training across edge devices while preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using cloud technologies like Kubernetes (K8s) can offer computational elasticity, yet may compromise FL privacy principles. K8s can jeopardise FL privacy by potentially allowing malicious FL clients to access other resources given its flat networking approach. This paper introduces the privacy-preserving K8s operator kubeFlower. It addresses privacy risks via isolation-by-design and differential privacy for data management. Isolation ensures secure resource sharing, while differential privacy safeguards individual data privacy. We introduce the Privacy Preserving Persistent Volume Claimer (P3-VC), which adds noise to data while managing a privacy budget. kubeFlower simplifies FL system management in K8s while ensuring privacy. We tested our approach on a network testbed composed of different geo-located cloud and edge nodes where FL clients are deployed. Our results demonstrate the approach’s efficacy in preserving privacy in K8s-based FL for cloud–edge environments.

Group Key Exchange (GKE) is an important tool to develop secure multi-user applications such as group text messages, ad-hoc networks, and so on. Most of the currently deployed GKE schemes are synchronous, i.e., they require all the participants to be online during their execution. However, with more battery-powered devices being used in such applications, the synchronicity requirement is challenging to fulfill. To fill the gaps, asynchronous GKE schemes have been introduced in the literature. Nevertheless, the currently available asynchronous and synchronous GKE schemes rely on Trusted Third Parties (TTPs) for key establishment and management. To this end, reliance on TTPs is a serious shortcoming since TTPs are well known to be the single point of failure. Furthermore, the existing GKE schemes require participants to perform all computations, which can degrade the performance of resource-constrained devices such as Internet of Things (IoT) devices. To solve these problems, in this paper, we propose an asynchronous GKE scheme that uses blockchain and smart contracts to store the security keys-related material and reduce the computational load of the participants. Furthermore, our proposed scheme provides Perfect Forward Secrecy (PFS) and Post-Compromised Security (PCS). Our implementation on Ethereum shows that the proposed scheme can scale to more than 100 participants when combined with a distributed storage system.

Rrecently, communication technologies have been advanced rapidly to guarantee large bandwidth with low latency and reduced overhead. In addition, many new technologies are in pursuit to ensure guaranteed communication and services such as 5th and 6th generation (5G/6G) services and technologies, future Internet architectures, edge computing techniques, edge cloud-based architectures, services virtualization, and so forth. The prime objective of these new technologies is to make user-produced information easily accessible to the users on the go. Hence, these technologies play a vital role in forms of wireless networks. However, in this Special Issue, we solely focus on the new solutions, services, communication techniques, and architectures to provide efficient information communication to the vehicular networks.

The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solution to enhance the computational capacity of vehicular networks; however, the computation requirements of these compute hungry applications will surpass the capabilities of such a solution. To address this challenge, the on-board resources of neighboring mobile vehicles can be utilized. However, such resource utilization requires an incentive mechanism to motivate privately owned neighboring vehicles to participate in sharing their resources. In this paper, we propose a contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources. The proposed approach enables the Road Side Units (RSUs) to provide appropriate rewards by offering a tailored contract to each resource sharing vehicle based on their contribution and unique characteristics. Moreover, we derive an optimal contract scheme for computational task offloading, taking into account the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme. The proposed scheme achieves up to 28% higher computing resource utilization, 17.2% lower energy consumption per computing resource utilization, and 17.1% lesser energy consumption per task completed when compared to the linear pricing incentive baseline.

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service’s characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%.

Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of vehicular edge or fog computing; however, the growing popularity and massive adoption of novel services make edge resources insufficient. This challenge can be addressed by utilizing the onboard computation resources of neighboring vehicles that are not resource-constrained along with the edge computing resources. To fill the gaps, in this paper, we propose to solve the problem of task offloading by jointly considering the communication and computation resources in a mobile vehicular network. We formulate a non-linear problem to minimize the energy consumption subject to the network resources. Further-more, we consider a practical vehicular environment by taking into account the dynamics of mobile vehicular networks. The formulated problem is solved via a deep reinforcement learning (DRL) based approach. Finally, numerical evaluations are performed that demonstrates the effectiveness of our proposed scheme.

Federated Learning (FL) is a promising technique for edge computing environments as it provides better data privacy protection. It enables each edge node in the system to send a central server a computed value, named gradient, rather than sending raw data. However, recent research results show that the FL is still vulnerable to an inference attack, which is an adversarial algorithm that is capable of identifying the data used to compute the gradient. One prevalent mitigation strategy is differential privacy which computes a gradient with noised data, but this causes another problem that is accuracy degradation. To effectively deal with this problem, this paper proposes a new digestive neural network (DNN) and integrates it into FL. The proposed scheme distorts raw data by DNN to make it unrecognizable then computes a gradient by a classification network. The gradients generated by edge nodes will be sent to the server to complete a trained model. The simulation results show that the proposed scheme has 9.31% higher classification accuracy and 19.25% lower attack accuracy on average than the differential private schemes.

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, which is difficult to analyze for detecting any potential abnormal behaviour and application outage. This phenomenon makes Machine Learning (ML) a natural choice to leverage and analyze the API traffic and obtain intelligent predictions. This paper proposes an ML-based technique to detect and classify API traffic based on specific features like bandwidth and number of requests per token. We employ a Support Vector Machine (SVM) as a binary classifier to classify the abnormal API traffic using its linear kernel. Due to the scarcity of the API dataset, we created a synthetic dataset inspired by the real-world API dataset. Then we used the Gaussian distribution outlier detection technique to create a training labeled dataset simulating real-world API logs data which we used to train the SVM classifier. Furthermore, to find a trade-off between accuracy and false positives, we aim at finding the optimal value of the error term (C) of the classifier. The proposed anomaly detection method can be used in a plug and play manner, and fits into the existing micro-service architecture with little adjustments in order to provide accurate results in a fast and reliable way. Our results demonstrate that the proposed method achieves an F1-score of 0.964 in detecting anomalies in API traffic with a 7.3% of false positives rate.

The healthcare industry deals with highly sensitive data which must be managed in a secure way. Electronic Health Records (EHRs) hold various kinds of personal and sensitive data which contain names, addresses, social security numbers, insurance numbers, and medical history. Such personal data is valuable to the patients, healthcare service providers, medical insurance companies, and research institutions. However, the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers. Hence, we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications. Blockchain is one of the promising solutions, aimed to provide transparency, security, and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems. Therefore, to solve the mentioned problems in healthcare applications, in this paper, we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications. We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications. Additionally, we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality, privacy and access control. The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security, regulation compliance, compatibility, flexibility, and scalability.

Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog and edge computing. Its learning process employs frequent communications as the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them and redistributes new weights to the devices. In FL, private data does not leave the individual local devices, and thus, rendered as a robust solution in terms of privacy preservation. However, the recently introduced membership inference attacks pose a critical threat to the impeccability of FL mechanisms. By eavesdropping only on the updates transferring to the center server, these attacks can recover the private data of a local device. A prevalent solution against such attacks is the differential privacy scheme that augments a sufficient amount of noise to each update to hinder the recovering process. However, it suffers from a significant sacrifice in the classification accuracy of the FL. To effectively alleviate the problem, this paper proposes a Digestive Neural Network (DNN), an independent neural network attached to the FL. The private data owned by each device will pass through the DNN and then train the FL. The DNN modifies the input data, which results in distorting updates, in a way to maximize the classification accuracy of FL while the accuracy of inference attacks is minimized. Our simulation result shows that the proposed DNN shows significant performance on both gradient sharing- and weight sharing-based FL mechanisms. For the gradient sharing, the DNN achieved higher classification accuracy by 16.17% while 9% lower attack accuracy than the existing differential privacy schemes. For the weight sharing FL scheme, the DNN achieved at most 46.68% lower attack success rate with 3% higher classification accuracy.

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized “things” with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as the elliptic curve cryptosystem (ECC). Nevertheless, the reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this article, we investigate applications of one of the postquantum signatures called hash-based signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with an emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for IoT security in the quantum era. We also investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.

Security and privacy will play a pivotal role in the commercialization of Vehicular Ad-hoc NETworks (VANETs). Traditionally, both cryptographic and non-cryptographic approaches have been used by researchers to address security and privacy issues and achieve secure Intelligent Transportation System (ITS) applications. However, some security goals such as trust and reputation, are still hard to achieve through conventional cryptographic approaches. Trust is the degree of certainty with which the received information is accepted and acted upon. Historically trust has been computed for both the content generator and the content itself with former known as entity trust and the latter known as data trust. Both entity and content trust are equally important to support trustworthy communication in VANET. We review, analyze, and compare some of the recently proposed trust establishment and management mechanisms (from 2014 to 2019) in vehicular networks. Furthermore, we also discuss the weaknesses and inadequacies of existing trust establishment and management approaches when deployed in a VANET environment. Finally, we discuss some future challenges that will need to be addressed for trustworthy communications in vehicular networks.

5G is poised to support new emerging service types that help in the realization of futuristic applications. These services include enhanced Mobile BroadBand (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine-Type Communication (mMTC). Even though the new services offer a variety of new use-cases to be implemented, it is still a challenge to guarantee the Quality of Service (QoS) they demand. Moreover, as considerable amount of computational resources are introduced in the evolved Radio Access Network (RAN) following the Mobile Edge Computing (MEC) concept, computational resource allocation optimization along with radio allocation becomes essential. In this paper, we examine the characteristics of the new 5G services and propose a joint computational and radio resource allocation framework that analyzes the QoS performance of each 5G service individually. The framework is developed based on per-service load characterization. Therefore, a computational load distribution algorithm is developed that balances the workloads subject to user association constraint. Further, radio resource allocation performs load-based eMBB-mMTC slicing and uRLLC puncturing. The simulation results show that the proposed solution reduces the packet loss ratio by up to 15% and increases the user data rate by up to 7% for 4G-like services. Furthermore, the effect of resource granularity in radio allocation has been identified as crucial factor for effective allocation of services with small data loads. Finally, the problem of small granularity has been solved by adapting the allocation interval.

Recently, cloud computing has emerged as a primary enabling technology to provide compute, storage, platform, and analytics services to end-users and organizations based on pay-as-you-use. In essence, cloud provides agility, availability, scalability, and resiliency. However, increased number of users leads to issues such as scheduling of requests, demands, and work-load efficiency over the available cloud resources. Similarly, since the inception of cloud computing, task scheduling is reckoned as an essential ingredient in the commercial value of this technology. Task scheduling is considered as an NP-hard problem in cloud computing and different solutions exist in the literature to address this issue. In this paper, we investigate and empirically compare some of the recent state-of-the-art scheduling mechanisms in cloud computing with respect to Makespan (the time difference between the start and finish of a sequence of jobs or tasks) and throughput (number of tasks successfully executed per unit time (Makespan)). We then extend the comparison by evaluating the considered approaches with respect to Average Resource Utilization Ratio (ARUR). We also recommend and identify factors that can improve resource utilization and maximize revenue-generation for cloud service providers.

Location-based encryption enhances security through integration of location data which is based on Global Positioning System (GPS) coordinates into encryption and decryption processes. It allows data to be decrypted only at specific location(s) or within a specific area. However, this approach strictly relies on self-checked location data which can be easily bypassed. In this paper, we present an analysis of the security of the existing location-based key exchange methods together with our proposed improvements. Furthermore, we propose a novel method based on the existence of a Trusted Third Party (TTP). A TTP is an entity trusted by both sides and the location tracking is entrusted to a TTP rather than a client. We also demonstrate a working proof-of-concept for the proposed approach.

Named Data Networking (NDN) uses the content name to enable content sharing in a network using Interest and Data messages. In essence, NDN supports communication through multiple interfaces, therefore, it is imperative to think of the interface that better meets the communication requirements of the application. The current interface ranking is based on single static metric such as minimum number of hops, maximum satisfaction rate, or minimum network delay. However, this ranking may adversely affect the network performance. To fill the gap, in this paper, we propose a new multi-metric robust interface ranking scheme that combines multiple metrics with different objective functions. Furthermore, we also introduce different forwarding modes to handle the forwarding decision according to the available ranked interfaces. Extensive simulation experiments demonstrate that the proposed scheme selects the best and suitable forwarding interface to deliver content.

Group Key Agreement (GKA) mechanisms play a crucial role in realizing various applications in different networks, such as sensor networks and the Internet of Things (IoT). To be suitable for IoT, a GKA must satisfy several critical requirements. First, a GKA must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices to establish ad hoc networks securely, and usually, these devices are resource-constrained. Second, the GKA must be able to distribute session keys successfully, even with offline devices. Third, a GKA must reduce the burden of heavy cryptographic computations for IoT devices. Based on these observations, we propose a new GKA scheme that satisfies all the requirements above. The proposed scheme leverages smart contracts to alleviate the computational and storage overheads on IoT devices induced by cryptographic functions. It also brings the advantage of asynchronism such that offline devices will be able to compute the group key once they are online.

Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and F-score, respectively.

The past few years have witnessed unprecedented advancements in the Distributed Ledger Technology (DLT) and blockchain - a form of DLT. DLT has clearly expanded the applications landscape in various sectors of our lives ranging from banking to business, finance, industry, education, and so on. On the other hand, security plays a crucial part in the successful realization of such applications and services. To this end, cryptography is the primary mean to protect the applications, networks, infrastructure, and services from cyber-threats. However, the existing Public Key Infrastructure (PKI) is based on central Certificate Authority (CA) that can become a bottleneck and may affect the efficiency of the cryptographic protocols because of the overhead incurred by the verification of cryptographic signatures and certificates. Recently, blockchain has also been leveraged to aid PKI without the need for a central authority. In this spirit, in this paper, we develop and implement a blockchain-based PKI using open-source Hyperledger Sawtooth. The proposed blockchain-based approach helps to address the problems of the existing PKI such as compromised and misbehaving CAs.

Named Data Networking (NDN) is a promising candidate for future internet architecture. It is one of the implementations of the Information-Centric Networking (ICN) architectures where the focus is on the data rather than the owner of the data. While the data security is assured by definition, these networks are susceptible of various Denial of Service (DoS) attacks, mainly Interest Flooding Attacks (IFA). IFAs overwhelm an NDN router with a huge amount of interests (Data requests). Various solutions have been proposed in the literature to mitigate IFAs; however; these solutions do not make a difference between intentional and unintentional misbehavior due to the network congestion. In this paper, we propose a novel congestion-aware IFA detection and mitigation solution. We performed extensive simulations and the results clearly depict the efficiency of our proposal in detecting truly occurring IFA attacks.

Development of Distributed Ledger Technology (DLT)-based applications requires an appropriate platform that meets the application requirements. However, due to the abundance of such platforms such as Ethereum, NEM, IOTA, and OpenChain, and the differences among them in terms of scalability, throughput, and features, it is not easy to select a platform for a given use-case. Selection of the right DLT platform is pivotal for the performance of applications and thus-forth directly affects consumer satisfaction. Therefore, the aforementioned factors must be taken into account to decide on a particular platform. To fill this gap, in this paper, we conduct a comparative analysis of different DLT platforms. The choice of platform is based on their popularity and current market share as well as the evolving trends and approaches. In essence, we choose Ethereum, EOS, Hyperledger Sawtooth and NEO. We compare these platforms from both development and performance perspectives. The comparison revealed that Sawtooth provides a huge customization capability that affects the performance and EOS maintains a stable throughput under varying network scales and loads.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

The unprecedented advancements in mobile phone technology on one hand offer plethora of applications to consumers, but on the other hand cause serious risk to users' privacy. Among other modules, camera is one of the most pervasive modules in smart phone used for taking pictures and videos. Furthermore, many applications use camera as an image capturing device that is used for different purposes such as entertainment or identification and authentication (e.g. biometric face authentication). In this paper, we aim at Android camera module and try to find vulnerabilities that could be exploited for camera spoofing. Particularly we aim at different techniques such as modifying the requesting application and creating virtual device at kernel level to use it as a camera. Our experiments revealed that it is still possible to spoof Android camera. Furthermore, based on our findings, we also suggest recommendations to avoid such exploit in Android applications.

Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of k binary networks vs. a single k-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.

This paper introduces the concept of a new service for social Internet of Vehicles (IoV)-based clouds called incentives-based vehicle witnesses as a service (IVWaaS), which employs vehicles moving on the road as the witnesses to designated events. Specifically, we focus on two key enablers, a new secure and privacy preserving service framework as well as a new incentive mechanism to promote the wide adoption of the aforementioned social service. In IVWaaS, when confronted any events, the vehicles in the vicinity with mounted cameras collaborate with other roadside cameras to take pictures of the site of interest around them, and send the pictures to the cloud infrastructure anonymously so that the privacy of the vehicles can be preserved. To stimulate active participation from the users, we also introduce a new privacy-aware incentives mechanism called privacy-aware proportionate receipt collection, in which the contributors are credited according to their contribution to the service and can claim their incentives in a privacy-aware fashion. Service providers can also use the stored pictures as “on-demand picture service.” Other law enforcement agencies can obtain the stored pictorial information and use it as forensics in the investigations.

Developing means of secure and trustful communications for Vehicular Social Networks (VSN) is essential to enable active information sharing among vehicles. Reputation-based trust management system is a popular security mechanism used in vehicular networks and proven to be robust in many other applications. In this paper, we propose a similarity-based bootstrapping method using analytic hierarchy process (AHP) for trust management in VSN. Then we introduce a reputation mechanism that takes into account, the user behaviors as well as historic features such as the total distance driven. Our simulation results show that the proposed system is robust against high density of malicious nodes (up to 60%). Furthermore, we also prove the accuracy of the proposed system in different conditions.

Summary The mobile cloud is a highly heterogenous and constantly evolving network of numerous portable devices utilizing the powerful back‐end cloud infrastructure to overcome their severe deficiency in computing resource and offer various services such as data sharing. Inherently, in mobile cloud, the risk of user privacy invasion by the cloud operator is high. The conditional proxy re‐encryption (CPRE) is a useful concept for secure group data sharing via cloud while preserving the privacy of the shared data from any unintended third parties including the cloud operator. Unfortunately, the state‐of‐art CPRE is not particularly designed for mobile cloud environment and therefore imposes heavy burdens to the weak mobile cloud clients. This paper introduces a new CPRE scheme, namely the CPRE for mobile cloud, which utilizes the back‐end cloud to the extreme extent so that the overhead of terminals is drastically reduced. Specifically, our scheme outsources a significant amount of computation overhead caused by the following functions at terminals: (a) re‐encryption key generation, (b) condition value change, and (c) decryption, to the cloud. The proposed scheme also allows users to verify the correctness of outsourced computation under refereed delegation of computation model. Our simulation results show CPRE for mobile cloud that outperforms its existing alternatives. Copyright © 2016 John Wiley & Sons, Ltd.

Classifying hyperspectral data within high dimensionality is a challenging task. To cope with this issue, this study implements a semi‐supervised multi‐kernel class consistency regulariser graph‐based spatial–spectral feature learning framework. For feature learning process, establishing the neighbouring relationship between the distinct samples from the high‐dimensional space is the key to a favourable outcome for classification. The proposed method implements two kernels and a class consistency regulariser. The first kernel constructs simple edges where every single vertex represents one particular sample and the edge weight encodes the initial similarity between distinct samples. Later the obtained relation is fed into the second kernel to obtain the final features for classification where the semi‐supervised learning is conducted to estimate the grouping relations among different samples according to their similarity, class, and spatial information. To validate the performance of proposed framework, the authors conduct several experiments on three publically available hyperspectral datasets. The proposed work equates favourably with state‐of‐the‐art works with an overall classification accuracy of 98.54, 97.83, and 98.38% for Pavia University, Salinas‐A, and Indian Pines datasets, respectively.

Recently an online electric vehicle (OLEV) concept has been introduced, where vehicles are propelled by the wirelessly transmitted electrical power from the infrastructure installed under the road while moving. The absence of secure-and-fair billing is one of the main hurdles to widely adopt this promising technology. This paper introduces a new secure and privacy-aware fair billing framework for OLEV on the move through the charging plates installed under the road. We first propose two extreme lightweight mutual authentication mechanisms, a direct authentication and a hash chain-based authentication between vehicles and the charging plates that can be used for different vehicular speeds on the road. Second, we propose a secure and privacy-aware wireless power transfer on move for the vehicles with bidirectional auditability guarantee by leveraging game theoretic approach. Each charging plate transfers a fixed amount of energy to the vehicle and bills the vehicle in a privacy-aware way accordingly. Our protocol guarantees secure, privacy-aware, and fair billing mechanism for the OLEVs while receiving electric power from the infrastructure installed under the road. Moreover, our proposed framework can play a vital role in eliminating the security and privacy challenges in the deployment of power transfer technology to the OLEVs.

Recently a new concept of online electric vehicle (OLEV) has been introduced in South Korea, where vehicles are propelled through the transmitted energy from the infrastructure installed underneath the road. However, for billing and audit reasons only authentic vehicles with necessary credentials are allowed to charge their batteries and pay the designated amount to the service provider. Moreover, due to the massive budget requirements for such infrastructure, only designated road segments will offer the charging service. As a result, a tradeoff solution to the charging of electric vehicles is needed to both fulfill the charging requirements of the electric vehicles and reduce the upfront costs for the service providers. To obtain electric charge from the charging plates beneath the road, vehicles need to authenticate themselves beforehand for twofold purposes: to bill the vehicles accordingly and to let the revocation authorities revoke the vehicle in case of a dispute. In this paper, we use the core concept of the OLEV and introduce extreme lightweight privacy-aware authentication schemes for charging-on-the-move through the charging plates installed under the road. More precisely we propose two mutual authentication mechanisms between charging plates and the vehicles, a direct authentication and a hash chain-based authentication. In the direct authentication scheme, we leverage multiple pseudonyms for conditional privacy. Vehicles use different pseudonyms every time they use the charging-on-the-move service. Whereas in case of hash chain-based authentication mechanism, the vehicles mutually authenticate with charging plates through service provider. Our proposed authentication mechanisms preserve conditional privacy throughout the protocol and is computationally lightweight than the existing mechanisms.

Vehicular ad hoc network (VANET) is gaining significant popularity due to their role in improving traffic efficiency and safety. However, communication in VANET needs to be secure as well as authenticated. The vehicles in the VANET not only broadcast traffic messages known as beacons but also broadcast safety critical messages such as electronic emergency brake light (EEBL). Due to the openness of the network, a malicious vehicles can join the network and broadcast bogus messages that could result in accident. On one hand, a vehicle needs to be authenticated while on the other hand, its private data such as location and identity information must be prevented from misuse. In this paper, we propose an efficient pseudonymous authentication protocol with conditional privacy preservation to enhance the security of VANET. Most of the current protocols either utilize pseudonym based approaches with certificate revocation list (CRL) that causes significant communicational and storage overhead or group signature based approaches that are computationally expensive. Another inherent disadvantage is to have full trust on certification authorities, as these entities have complete user profiles. We present a new protocol that only requires honest-but-curious behavior from certification authority. We utilize a mechanism for providing a user with two levels of pseudonyms named as base pseudonym and short time pseudonyms to achieve conditional privacy. However, in case of revocation, there is no need to maintain the revocation list of pseudonyms. The inherent mechanism assures the receiver of the message about the authenticity of the pseudonym. In the end of the paper, we analyze our protocol by giving the communication cost as well as various attack scenarios to show that our approach is efficient and robust.

In the recent past, a new concept termed as VANET-based clouds evolved from traditional VANET incorporating both VANET and cloud computing technologies in order to provide vehicle drivers, passengers, and consumers with safe, reliable, and infotainment-rich services while driving on the roads. In this paper, we use a framework of VANET-based clouds proposed by Hussain et al. namely VuC (VANET using Clouds) and define another layer TIaaS (Traffic Information as a Service) atop the cloud computing stack. TIaaS layer provides vehicular nodes (more precisely subscribers) with fine-grained traffic information in a secure way. Additionally our proposed scheme provides security, privacy, and conditional anonymity which are of prime concern in VANET clouds.

Technological advancements in mobile technology and cloud computing open the door for another paradigm known as Mobile Cloud Computing (MCC). This integration of cloud computing and mobile technology gives numerous facilities to a mobile user, such as the ubiquitous availability of Location Based Services (LBS). The utilization of these LBS services require the knowledge of a user's location, hence threatens the privacy of a user. In this paper we take advantages of ultra-fast processing and reliability of cloud computing and aim to solve the privacy threat issue faced by a mobile user while getting LBS services. We propose a model that utilizes a cloud based server which helps in making of a cloaking region. We highlight that how our model utilizes an untrusted cloud based server and eliminates the use of a trusted anonymizer while providing LBS services to a user securely and anonymously.

Inspired by the dramatic evolution of VANE clouds, this paper proposes a new VANET-cloud service called VWaaS (Vehicle Witnesses as a Service) in which vehicles moving on the road serve as anonymous witnesses of designated events such as a terrorist attack or a deadly accident. When confronted the events, a group of vehicles with mounted cameras collaborate with roadside stationary cameras to take pictures of the site of interest (SoI) around them, and send the pictures to the cloud infrastructure anonymously. The pictures are sent to the cloud in a way that the privacy of the senders can be protected, and kept by the cloud for future investigation. However, for the case that the pictures are used as an evidence of court trial, we made the privacy protection to be conditional and thus can be revoked by authorized entity(s) if necessary.

The foreseen dream of reliable, safe, and comfortable driving experience is yet to become reality since automobile industries are testing their waters for VANET (Vehicular Ad Hoc NETwork) deployment. But nevertheless, security and privacy issues have been the root cause of hindrance in VANET deployment. Recently, VANET evolved to VANET-based clouds as a result of resources-rich high-end cars. Soon after, Hussain et al. defined different architectural frameworks for VANET-based clouds. In this paper, we aim at a specific framework namely VuC (VANET using Clouds) where VANET and CC (Cloud Computing) cooperate with each other in order to provide VANET users (more precisely subscribers) with services. We propose a lightweight privacy-aware revocation and route tracing mechanism for VuC. Beacons broadcasted by vehicles are stored in cloud infrastructure as cooperation from VANET and after processing, cloud provides VANET subscribers with services. Revocation authorities can revoke and trace the path taken by the target node for a specified timespan by exploiting the beacons stored in the cloud. Our proposed scheme is secure, preserves conditional privacy, and is computationally less expensive than the previously proposed schemes.

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.