In the recent context of the emergence of smart cities, the massive amount of data generated by connected objects has led to unprecedented demands in terms of data transfer. The various constraints linked to their number, their characteristics, and their transmission are even greater and dim the effectiveness, in their regard, of traditional data planning schemes. As a result, the need to minimize the delivery time of urgent packets while reducing the average data delay, the difficulty in choosing and combining the appropriate criteria for classifying and prioritizing data, and the loss of packets are of continuing concern. In this paper, we propose an adaptive scheduling model based on multilevel priority packet classification, preemptive packet queuing with dynamic and adaptive reordering, contingency migration of packets in critical situations, and adaptive criticality-based selection of packet next-hop. We introduce two new parameters for scheduling decisions: the ratio of per-level deadlines reflecting the evolution of a packet in the network and the migration coefficient based on the experience of same-characteristic packets. Performance evaluation shows that the proposed model effectively prevents data loss and prioritizes the transfer of emergency data over a hierarchical wireless sensor network. Moreover, it guarantees the shortest delays for urgent data with an improvement of 31% and promotes fairness toward less urgent ones. The lowest delivery rate observed with the proposed method is 99.9%.

Mobile banking applications make users' daily lives easier by allowing them to access banking services, such as balance inquiries and bill payments, anytime and anywhere. Since these applications manage very sensitive financial data, special attention must be paid to data security. Several works in the literature assess the security of mobile banking applications. However, we observe the lack of a widely adopted framework among researchers for assessing the security of mobile banking applications. In this paper, we propose a framework consisting of twenty-six criteria for assessing the security of Android mobile banking applications. These criteria are divided into five categories: mobile device security, data in transit, data storage, cryptographic misuse, and others. Subsequently, we evaluate the proposed framework based on predefined requirements. These requirements are no redundancy, no ambiguity, and comprehensiveness. As a case study, we assess the security of the Android mobile banking applications of seven major Canadian banks. The results show that data in transit is adequately protected by these applications.

Bio-cryptosystems often save the biometric template for authentication and generally employ randomly generated keys to encrypt and sign data. This method raises privacy protection concerns. Furthermore, for a system secured by the usage of a cryptographic key, losing the key often has disastrous consequences. To overcome the privacy issues, and allow the secure recovery of lost keys, we design a Key Derivation Function to extract a key from biometric data: a new method – based on clustering algorithms – detects consistent and discriminative features from biometric characteristics to create a code. Then, HMAC-SHA256 (as specified by the National Institute of Standards and Technology) generates a standard key from the code. To reproduce the code at future times (that also serves for authentication), the Key Derivation Function stores helper data with the guarantee of privacy. Indeed, with a private face dataset, the probability of generating the code with only the helper data is less than 2−300, and less than 2−246 for a subset of the YouTube Face database. Moreover, on the private database and the tested users from the YouTube Face database, our system has a false acceptance rate of 0%. It corrects up to 40.3% of noise levels on the private database and has good management of the inter-user variability.

Blockchain is a new paradigm to realize payment without a single Trusted-Third-Party. The technology exploits cryptography to secure all the transactions that are available to participating nodes for validation via distributed consensus algorithms. Blockchain-based architectures for mobile payment face challenges like transaction privacy and performance issues. We analyze these architectures and assess the privacy issues and the performance in the real world. Then, we propose a payment scheme to guarantee the privacy of the transactions with discussions on tricks to improve the performance. We study the feasibility of implementation of the proposed scheme, both on public and consortium Blockchains. The payment scheme ensures that a participating node has access to only a part of the meaningful data of a transaction. The expected performance with Hyperledger Blockchain (with less than 16 participating nodes) is more than 1000 TPS. We indicate updates to decrease the duration of a transaction from 15 S to less than 1 S with public Ethereum Blockchain.

We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion.We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology.To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory.We solve the STP problem with a Deep Neural Networks (DNN) in a multitask learning setting augmented by input from CV. Results show that our approach, namely MTL-CV, with an average Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122).Compared to single task learning with Artificial Neural Network (ANN), ANN had a lower performance, 0.113 for RMSE, than MTL-CV.MTL-CV learned historical similarities between segments, in contrast to using direct historical trends in the measure, because trends may not exist in the measure but do in the similarities.

Distributed denial of service attacks are cyber-attacks that target the availability of servers. As a result, legitimate users no longer have access to the service. This can have a negative impact on an organization, such as lack of reputation and economic losses. Therefore, it is important to design defense mechanisms against these attacks. There are systems for detecting distributed denial of service attacks in the literature, which still have various shortcomings. Some of these systems detect the presence of attack traffic without identifying the attack packets or flows. Others use static thresholds and therefore cannot adapt to changes in legitimate traffic. In this paper, we propose an online system that aims to detect flooding attacks in a short timeframe and a client–server environment. The proposed detection system consists of five modules, namely features extraction and connections construction, suspicious activity detection, attack connections detection, alert generation and threshold update. The suspicious activity detection module calculates the normalized Shannon entropy by considering the source Internet Protocol address as a random variable. Suspicious activity is detected when the computed entropy is below a threshold. The threshold calculation is based on Chebyshev's theorem. We propose a dynamic threshold algorithm to track changes in legitimate traffic. We evaluate the proposed system through simulations and using a publicly available dataset. Compared to other similar works, the proposed detection system has a better performance in terms of detection rate, false positive rate, precision and overall accuracy.

In cloud computing, a fundamental management problem with the Infrastructure as a Service (IaaS) model lies in the efficient embedding of computational and networking resources onto distributed virtualized infrastructures owned by independent cloud providers (CPs). In such a context, this issue usually referred to as the Virtual Network Embedding (VNE) problem, adds more complexity since the entire embedding process requires two mayor phases of operation: the multicloud virtual network requests (VNRs) splitting, followed by the intracloud VNR segments mapping. This paper focuses on the splitting phase problem, by proposing a VNRs splitting strategy formalized as an Integer Linear Program (ILP) model, with the objective of improving the performance and QoS of resulting mapped VNR segments, while minimizing the resource provisioning expenditures. As the VNE is classified as an NP-hard problem, a hybrid metaheuristic approach based on the Ant Colony Optimization (ACO) combined with the Tabu Search (TS) as local search operator, is proposed in order to find good feasible solutions in reasonable time. The simulation results show the efficiency of the proposed approach, which generates, in a highly reduced computing time, solution costs very close to the exact solution, with an average cost gap ranging from 0 percent to a maximum of 3.42 percent.

Mobile devices are becoming an essential part of many users’ lives. Users exchange sometimes very sensitive data with remote servers. This raises a security problem in terms of the confidentiality and integrity of these data, and users’ privacy. Mutual authentication protocols allow a user and a server to confirm each other’s legitimacy and share a session key to encrypt subsequent communications. Several protocols have been proposed to achieve this goal. However, these have certain weaknesses, such as impersonation, lack of anonymity, the use of additional hardware, and the synchronization problem associated with the use of timestamps. In this paper, we propose a mutual authentication protocol based on elliptic curve cryptography for mobile client – server environments, which addresses the above problems. This protocol is intended to be lightweight as it is designed for resource constrained mobile devices. Moreover, we present a formal and informal analysis of the security of the proposed protocol. This latter has security attributes, such as session key security, perfect forward secrecy, user anonymity, resistance to impersonation, replay and insider attacks. Performance evaluation shows that we outperform similar protocols. Therefore, the proposed protocol is secure, efficient and suitable for mobile environments.

— In multi-tenancy cloud environments, physical resources are transparently shared by multiple Virtual Machines (VMs) belonging to multiple users. Implementing an efficient access control mechanism in such environments can prevent unauthorized access to the Cloud resources. In this paper, we propose an access control mechanism that provides scalable and secure access control to the Cloud in the context of multi-tenancy cloud environments. Such a mechanism will prevent malicious tenants from generating and sending unauthorized traffic to the Cloud network.

This paper addresses the problem of computational and networking resources embedding across multiple independent cloud providers (CPs). We focus on the splitting phase problem by proposing a virtual network requests (VNRs) splitting strategy, which aims at improving the performance and the quality of service (QoS) of resulting mapped VNR segments. We formalise our splitting strategy as a mathematical maximisation problem with constraints by using an integer linear program (ILP). Since the VNRs splitting process is classified as an NP-hard problem, we propose a metaheuristic approach based on the Tabu search (TS), in order to find good feasible solutions in polynomial solving time. The simulations results obtained show the efficiency of the proposed algorithm, in comparison with the exact method and an other baseline approach. Solution costs are on average close to the upper bounds, with an average gap ranging from 0% to a maximum of 2.97%, performed in a highly reduced computing time.

—Internet of Things (IoT) is a key technological enabler to create smart environments and provide various benefits. In the context of a smart city, a huge number of IoT applications are being developed for emergency management operation and city traffic congestion management. These applications require fast system reaction to get the valuable data and make appropriate decisions. Therefore, it is essential to design and develop a service model that ensures an appropriate level of Quality of Service (QoS) for such applications. In this paper, we take advantage of Software-Defined Networking (SDN) technology integrated into the IoT system to propose a new QoS routing model for core transport SDN. In the model, the application QoS preferences and network elements status are directly considered in the resource allocation process aiming to satisfy the application expectation while maximizing network performance. We modeled a status-aware and Service-Level-Agreement-aware (SLA-aware) routing mechanism and implemented multi-path and load-balancing approaches in the model to enhance the network throughput and increase system availability.

We developed a distributed data mining system to elaborate on a decision concerning the cause of urban traffic congestion via emerging connected vehicle (CV) technology. We observe this complex phenomena through the interactions between vehicles exchanging messages via vehicle to vehicle communication. Results are based on real-time simulation generated scenarios extended from the real-world traffic travel and activity patterns simulation cologne scenario. We evaluate a voting procedure that provides deeper insights using cooperation between vehicles, belief functions (BF) aimed at improving representation of information and a data association technique (DAT) aimed at data mining and extracting the association rules from the exchanged messages. Methods are tested and compared using a microscopic urban mobility simulator, SUMO, and a network simulator, ns-2, for the simulation of communication between CVs. Compared with the back-propagation algorithm (BP) extensively used in the past literature, our performance evaluation shows that the proposed methods enhance the estimation of the cause of congestion by 48 for the proposed VP, 58 for the BF, 71 for the DAT, and 70 for $\beta $ -DAT. The methods also enhance detection time from 7.09 to 10.3, and $\beta $ -DAT outperforms BP by approximately 1.25 less false alarms triggered by the network, which can be significant in the context of real-time decision making. We show that a market penetration rate between 63 and 75 is enough to ensure satisfactory performance.

With the rise of the Internet of Things (IoT) technology, the number of IoT devices/sensors has increased significantly. It is anticipated that large-scale sensor-based systems will prevail in our societies, calling for novel methodologies to design and operate those new systems. To support the computational demand of real-time delay-sensitive applications of largely distributed IoT devices/sensors, the Cloud is migrating to the edge of the network where resources such as routers, switches, and gateways are being virtualized. The open structural design of IoT architecture and the extensive usage of the paradigm itself cause to encounter conventional security issues for the existing networking technologies. Moreover, cooperation generates challenges as new security challenges can disrupt the systems' regular functionalities and operations. Furthermore, the commercialization of the IoT has led to several public security concerns including threats of cyber-attacks, privacy issues, and organized crimes. In this paper, we aim to provide guidelines for researchers and practitioners interested in understanding IoT security issues. More specifically, an extensive description of security threats and challenges across the different layers of the architecture of IoT systems is presented. Also, the light will be shed on the solutions and countermeasures proposed in the literature to address such security issues. Finally, an emerging security challenge which has yet to be explained in-depth in previous studies is introduced. (C) 2019 Elsevier B.V. All rights reserved.

An Intrusion Detection System (IDS) is a key cybersecurity tool for network administrators as it identifies malicious traffic and cyberattacks. With the recent successes of machine learning techniques such as deep learning, more and more IDS are now using machine learning algorithms to detect attacks faster. However, these systems lack robustness when facing previously unseen types of attacks. With the increasing number of new attacks, especially against Internet of Things devices, having a robust IDS able to spot unusual and new attacks becomes necessary. This work explores the possibility of leveraging generative adversarial models to improve the robustness of machine learning based IDS. More specifically, we propose a new method named SIGMA, that leverages adversarial examples to strengthen IDS against new types of attacks. Using Generative Adversarial Networks (GAN) and metaheuristics, SIGMA %Our method consists in generates adversarial examples, iteratively, and uses it to retrain a machine learning-based IDS, until a convergence of the detection rate (i.e. until the detection system is not improving anymore). A round of improvement consists of a generative phase, in which we use GANs and metaheuristics to generate instances ; an evaluation phase in which we calculate the detection rate of those newly generated attacks ; and a training phase, in which we train the IDS with those attacks. We have evaluated the SIGMA method for four standard machine learning classification algorithms acting as IDS, with a combination of GAN and a hybrid local-search and genetic algorithm, to generate new datasets of attacks. Our results show that SIGMA can successfully generate adversarial attacks against different machine learning based IDS. Also, using SIGMA, we can improve the performance of an IDS to up to 100\% after as little as two rounds of improvement.

WPANs are gaining in popularity as cheap, easy to deploy networks to collect real-time contextual information. Research in the area of WPANs has been focused mostly on energy efficiency with only a few papers on supporting video applications on these networks. In this work we will present a new method to improve the quality of video applications over WPANs. The proposed solution is based on a cross-layer architecture to collect information on the state of the network, and to dynamically modify the parameters of the H.264 coder at the source accordingly. We prove through extensive simulations on ns-2 that this adaptive implementation of the H.264 video coder improves the quality of the video at the receiver as compared to the regular static implementation.

In cloud computing, a fundamental management problem with the Infrastructure as a Service model lies in the efficient embedding of computing and networking resources onto distributed virtualized infrastructures. This issue, usually referred to as the Virtual Network Embedding (VNE) problem, has been well studied for a single Cloud Provider (CP). However, wide-area services delivery may require to embed heterogeneous resources over multiple CPs. This adds more complexity and scalability issues, since the Virtual Network Requests (VNRs) embedding process requires two phases of operation: the multicloud VNRs splitting, followed by the intracloud VNR segments mapping. This paper addresses the problem of VNE across multiple CPs by proposing a VNRs splitting strategy which aims at improving the performance and QoS of resulting VNR segments. An Integer Linear Program (ILP) is used to formalize the splitting phase as a maximization problem with constraints. Subsequently, in order to minimize the overall delay, a multi-objective intracloud resource mapping approach formalized as a Mixed-Integer Linear Program (MILP) is adopted. Simulations with the exact method show the efficiency of the proposed strategy based on several performance criteria. In particular, the acceptance rate and the delay are respectively improved by 15.1 and 18.5 percent, while preventing QoS violations.

In this paper, we address the problem of computational and networking virtual resources embedding across multiple Infrastructure-as-a-Service (IaaS) providers. This issue, usually referred to as the Virtual Network Embedding (VNE) problem, requires two phases of operation in such a context: the multicloud virtual network requests (VNRs) splitting, followed by the intracloud VNR segments mapping. This paper focuses on the splitting phase problem, by proposing a splitting strategy based on two optimization approaches, with the objective of improving the performance and the quality of service (QoS) of resulting mapped VNR segments. An Integer Linear Program (ILP) is used to formalize our splitting strategy as a mathematical minimization problem with constraints. The ILP model is first solved with the exact approach. Subsequently, a metaheuristic approach based on the Tabu Search (TS) is proposed in order to find optimal or near-optimal solutions in polynomial solving time. The simulation results obtained show the efficiency of the proposed VNRs splitting approaches according to several performance criteria. Solution costs of the heuristic are on average close to the exact solution, with an average cost gap ranging from 0% to a maximum of 2.05%, performed in a highly reduced computing time. In comparison with other baseline approaches, the acceptance rate and the delay are improved by approximately 15%, while preventing QoS violations.

Selecting the most stable partner on a vehicular ad hoc network (VANET) is a challenging task due to its unique mobility, driver behavior, and networking requirements. Although new partner selection protocols are introduced for mobile ad hoc networks and general mobility, there is a lack of a partner selection protocol that is suitable for VANET. This paper discusses the requirements of such a protocol and presents a handoff protocol that is tailored to VANET requirements. We leverage the use of partner-assisted handoff by introducing a vehicle link expiration time (VLET) metric that is designed to maximize intervehicular connections. The suitability of the proposed protocol is carefully studied and then compared to state-of-the-art protocols. A performance analysis comparing average connection time, connection stability, handoff delay, and success rate is carried out. The simulation results show that the proposed protocol enhances the handoff on VANET and can increase the average connection time among vehicles by 175%. This research advocates the importance of using road topology and traffic information for any future VANET handoff protocols and paves the way for future research on using the introduced VLET in other VANET applications.