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Forti, Stefano.  2022.  Keynote: The fog is rising, in sustainable smart cities. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). :469–471.
With their variety of application verticals, smart cities represent a killer scenario for Cloud-IoT computing, e.g. fog computing. Such applications require a management capable of satisfying all their requirements through suitable service placements, and of balancing among QoS-assurance, operational costs, deployment security and, last but not least, energy consumption and carbon emissions. This keynote discusses these aspects over a motivating use case and points to some open challenges.
Garcia, Carla E., Camana, Mario R., Koo, Insoo.  2022.  DNN aided PSO based-scheme for a Secure Energy Efficiency Maximization in a cooperative NOMA system with a non-linear EH. 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN). :155–160.
Physical layer security is an emerging security area to tackle wireless security communications issues and complement conventional encryption-based techniques. Thus, we propose a novel scheme based on swarm intelligence optimization technique and a deep neural network (DNN) for maximizing the secrecy energy efficiency (SEE) in a cooperative relaying underlay cognitive radio- and non-orthogonal multiple access (NOMA) system with a non-linear energy harvesting user which is exposed to multiple eavesdroppers. Satisfactorily, simulation results show that the proposed particle swarm optimization (PSO)-DNN framework achieves close performance to that of the optimal solutions, with a meaningful reduction in computation complexity.
Hkiri, Amal, Karmani, Mouna, Machhout, Mohsen.  2022.  The Routing Protocol for low power and lossy networks (RPL) under Attack: Simulation and Analysis. 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET). :143-148.

Routing protocol for low power and lossy networks (RPL) is the underlying routing protocol of 6LoWPAN, a core communication standard for the Internet of Things. In terms of quality of service (QoS), device management, and energy efficiency, RPL beats competing wireless sensor and ad hoc routing protocols. However, several attacks could threaten the network due to the problem of unauthenticated or unencrypted control frames, centralized root controllers, compromised or unauthenticated devices. Thus, in this paper, we aim to investigate the effect of topology and Resources attacks on RPL.s efficiency. The Hello Flooding attack, Increase Number attack and Decrease Rank attack are the three forms of Resources attacks and Topology attacks respectively chosen to work on. The simulations were done to understand the impact of the three different attacks on RPL performances metrics including End-to-End Delay (E2ED), throughput, Packet Delivery Ratio (PDR) and average power consumption. The findings show that the three attacks increased the E2ED, decreased the PDR and the network throughput, and degrades the network’, which further raises the power consumption of the network nodes.

Khodayer Al-Dulaimi, Omer Mohammed, Hassan Al-Dulaimi, Mohammed Khodayer, Khodayer Al-Dulaimi, Aymen Mohammed.  2022.  Analysis of Low Power Wireless Technologies used in the Internet of Things (IoT). 2022 2nd International Conference on Computing and Machine Intelligence (ICMI). :1-6.

The Internet of Things (IoT) is a novel paradigm that enables the development of a slew of Services for the future of technology advancements. When it comes to IoT applications, the cyber and physical worlds can be seamlessly integrated, but they are essentially limitless. However, despite the great efforts of standardization bodies, coalitions, companies, researchers, and others, there are still a slew of issues to overcome in order to fully realize the IoT's promise. These concerns should be examined from a variety of perspectives, including enabling technology, applications, business models, and social and environmental consequences. The focus of this paper is on open concerns and challenges from a technological standpoint. We will study the differences in technical such Sigfox, NB-IoT, LoRa, and 6LowPAN, and discuss their advantages and disadvantage for each technology compared with other technologies. Demonstrate that each technology has a position in the internet of things market. Each technology has different advantages and disadvantages it depends on the quality of services, latency, and battery life as a mention. The first will be analysis IoT technologies. SigFox technology offers a long-range, low-power, low-throughput communications network that is remarkably resistant to environmental interference, enabling information to be used efficiently in a wide variety of applications. We analyze how NB-IoT technology will benefit higher-value-added services markets for IoT devices that are willing to pay for exceptionally low latency and high service quality. The LoRa technology will be used as a low-cost device, as it has a very long-range (high coverage).

Buzura, Sorin, Dadarlat, Vasile, Peculea, Adrian, Bertrand, Hugo, Chevalier, Raphaël.  2022.  Simulation Framework for 6LoWPAN Networks Using Mininet-WiFi. 2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). :1-5.

The Internet of Things (IoT) continuously grows as applications require connectivity and sensor networks are being deployed in multiple application domains. With the increased applicability demand, the need for testing and development frameworks also increases. This paper presents a novel simulation framework for testing IPv6 over Low Power Wireless Personal Networks (6LoWPAN) networks using the Mininet-WiFi simulator. The goal of the simulation framework is to allow easier automation testing of large-scale networks and to also allow easy configuration. This framework is a starting point for many development scenarios targeting traffic management, Quality of Service (QoS) or security network features. A basic smart city simulation is presented which demonstrates the working principles of the framework.

Jain, Arpit, Jat, Dharm Singh.  2020.  An Edge Computing Paradigm for Time-Sensitive Applications. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :798—803.
Edge computing (EC) is a new developing computing technology where data are collected, and analysed nearer to the edge or sources of the data. Cloud to the edge, intelligent applications and analytics are part of the IoT applications and technology. Edge computing technology aims to bring cloud computing features near to edge devices. For time-sensitive applications in cloud computing, architecture massive volume of data is generated at the edge and stored and analysed in the cloud. Cloud infrastructure is a composition of data centres and large-scale networks, which provides reliable services to users. Traditional cloud computing is inefficient due to delay in response, network delay and congestion as simultaneous transactions to the cloud, which is a centralised system. This paper presents a literature review on cloud-based edge computing technologies for delay-sensitive applications and suggests a conceptual model of edge computing architecture. Further, the paper also presents the implementation of QoS support edge computing paradigm in Python for further research to improve the latency and throughput for time-sensitive applications.
Choudhary, Swapna, Dorle, Sanjay.  2021.  Empirical investigation of VANET-based security models from a statistical perspective. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA). :1—8.
Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.
Massey, Keith, Moazen, Nadia, Halabi, Talal.  2021.  Optimizing the Allocation of Secure Fog Resources based on QoS Requirements. 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :143—148.
Fog computing plays a critical role in the provisioning of computing tasks in the context of Internet of Things (IoT) services. However, the security of IoT services against breaches and attacks relies heavily on the security of fog resources, which must be properly implemented and managed. Increasing security investments and integrating the security aspect into the core processes and operations of fog computing including resource management will increase IoT service protection as well as the trustworthiness of fog service providers. However, this requires careful modeling of the security requirements of IoT services as well as theoretical and experimental evaluation of the tradeoff between security and performance in fog infrastructures. To this end, this paper explores a new model for fog resource allocation according to security and Quality of Service (QoS). The problem is modeled as a multi-objective linear optimization problem and solved using conventional, off-the-shelf optimizers by applying the preemptive method. Specifically, two objective functions were defined: one representing the satisfaction of the security design requirements of IoT services and another that models the communication delay among the different virtual machines belonging to the same service request, which might be deployed on different intermediary fog nodes. The simulation results show that the optimization is efficient and achieves the required level of scalability in fog computing. Moreover, a tradeoff needs to be pondered between the two criteria during the resource allocation process.
Nougnanke, Kokouvi Benoit, Labit, Yann, Bruyere, Marc, Ferlin, Simone, Aïvodji, Ulrich.  2021.  Learning-based Incast Performance Inference in Software-Defined Data Centers. 2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). :118–125.
Incast traffic is a many-to-one communication pattern used in many applications, including distributed storage, web-search with partition/aggregation design pattern, and MapReduce, commonly in data centers. It is generally composed of short-lived flows that may be queued behind large flows' packets in congested switches where performance degradation is observed. Smart buffering at the switch level is sensed to mitigate this issue by automatically and dynamically adapting to traffic conditions changes in the highly dynamic data center environment. But for this dynamic and smart buffer management to become effectively beneficial for all the traffic, and especially for incast the most critical one, incast performance models that provide insights on how various factors affect it are needed. The literature lacks these types of models. The existing ones are analytical models, which are either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, we propose a machine-learning-based incast performance inference. With this prediction capability, smart buffering scheme or other QoS optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance. Since applying machine learning to networks managed in a distributed fashion is hard, the prediction mechanism will be deployed on an SDN control plane. We could then take advantage of SDN's centralized global view, its telemetry capabilities, and its management flexibility.
Mamushiane, Lusani, Shozi, Themba.  2021.  A QoS-based Evaluation of SDN Controllers: ONOS and OpenDayLight. 2021 IST-Africa Conference (IST-Africa). :1–10.
SDN marks a paradigm shift towards an externalized and logically centralized controller, unlike the legacy networks where control and data planes are tightly coupled. The controller has a comprehensive view of the network, offering flexibility to enforce new traffic engineering policies and easing automation. In SDN, a high performance controller is required for efficient traffic management. In this paper, we conduct a performance evaluation of two distributed SDN controllers, namely ONOS and OpenDayLight. Specifically, we use the Mininet emulation environment to emulate different topologies and the D-ITG traffic generator to evaluate aforementioned controllers based on metrics such as delay, jitter and packet loss. The experimental results show that ONOS provides a significantly higher latency, jitter and low packet loss than OpenDayLight in all topologies. We attribute the poor performance of OpenDayLight to its excessive CPU utilization and propose the use of Hyper-threading to improve its performance. This work provides practitioners in the telecoms industry with guidelines towards making informed controller selection decisions
LaMar, Suzanna, Gosselin, Jordan J, Caceres, Ivan, Kapple, Sarah, Jayasumana, Anura.  2021.  Congestion Aware Intent-Based Routing using Graph Neural Networks for Improved Quality of Experience in Heterogeneous Networks. MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM). :477—481.
Making use of spectrally diverse communications links to re-route traffic in response to dynamic environments to manage network bottlenecks has become essential in order to guarantee message delivery across heterogeneous networks. We propose an innovative, proactive Congestion Aware Intent-Based Routing (CONAIR) architecture that can select among available communication link resources based on quality of service (QoS) metrics to support continuous information exchange between networked participants. The CONAIR architecture utilizes a Network Controller (NC) and artificial intelligence (AI) to re-route traffic based on traffic priority, fundamental to increasing end user quality of experience (QoE) and mission effectiveness. The CONAIR architecture provides network behavior prediction, and can mitigate congestion prior to its occurrence unlike traditional static routing techniques, e.g. Open Shortest Path First (OSPF), which are prone to congestion due to infrequent routing table updates. Modeling and simulation (M&S) was performed on a multi-hop network in order to characterize the resiliency and scalability benefits of CONAIR over OSPF routing-based frameworks. Results demonstrate that for varying traffic profiles, packet loss and end-to-end latency is minimized.
Iashvili, Giorgi, Iavich, Maksim, Bocu, Razvan, Odarchenko, Roman, Gnatyuk, Sergiy.  2021.  Intrusion Detection System for 5G with a Focus on DOS/DDOS Attacks. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). 2:861–864.
The industry of telecommunications is being transformed towards 5G technology, because it has to deal with the emerging and existing use cases. Because, 5G wireless networks need rather large data rates and much higher coverage of the dense base station deployment with the bigger capacity, much better Quality of Service - QoS, and the need very low latency [1–3]. The provision of the needed services which are envisioned by 5G technologies need the new service models of deployment, networking architectures, processing technologies and storage to be defined. These technologies will cause the new problems for the cybersecurity of 5G systems and the security of their functionality. The developers and researchers working in this field make their best to secure 5G systems. The researchers showed that 5G systems have the security challenges. The researchers found the vulnerabilities in 5G systems which allow attackers to integrate malicious code into the system and make the different types of the illegitimate actions. MNmap, Battery drain attacks and MiTM can be successfully implemented on 5G. The paper makes the analysis of the existing cyber security problems in 5G technology. Based on the analysis, we suggest the novel Intrusion Detection System - IDS by means of the machine-learning algorithms. In the related papers the scientists offer to use NSL-KDD in order to train IDS. In our paper we offer to train IDS using the big datasets of DOS/DDOS attacks, besides of training using NSL-KDD. The research also offers the methodology of integration of the offered intrusion detection systems into an standard architecture of 5G. The paper also offers the pseudo code of the designed system.
Li, Fulin, Ji, Huifang, Zhou, Hongwei, Zhang, Chang.  2021.  A Dynamic and Secure Migration Method of Cryptographic Service Virtual Machine for Cloud Environment. 2021 7th International Conference on Computer and Communications (ICCC). :583–588.
In order to improve the continuity of cryptographic services and ensure the quality of services in the cloud environment, a dynamic migration framework of cryptographic service virtual machines based on the network shared storage system is proposed. Based on the study of the security threats in the migration process, a dynamic migration attack model is established, and the security requirement of dynamic migration is analyzed. It designs and implements the dynamic security migration management software, which includes a dynamic migration security enhancement module based on the Libvirt API, role-based access control policy, and transmission channel protection module. A cryptographic service virtual machine migration environment is built, and the designed management software and security mechanism are verified and tested. The experimental results show that the method proposed in the paper can effectively improve the security of cryptographic service virtual machine migration.
Halabi, Talal.  2021.  Adaptive Security Risk Mitigation in Edge Computing: Randomized Defense Meets Prospect Theory. 2021 IEEE/ACM Symposium on Edge Computing (SEC). :432–437.

Edge computing supports the deployment of ubiquitous, smart services by providing computing and storage closer to terminal devices. However, ensuring the full security and privacy of computations performed at the edge is challenging due to resource limitation. This paper responds to this challenge and proposes an adaptive approach to defense randomization among the edge data centers via a stochastic game, whose solution corresponds to the optimal security deployment at the network's edge. Moreover, security risk is evaluated subjectively based on Prospect Theory to reflect realistic scenarios where the attacker and the edge system do not similarly perceive the status of the infrastructure. The results show that a non-deterministic defense policy yields better security compared to a static defense strategy.

Bhagavan, Srini, Gharibi, Mohamed, Rao, Praveen.  2021.  FedSmarteum: Secure Federated Matrix Factorization Using Smart Contracts for Multi-Cloud Supply Chain. 2021 IEEE International Conference on Big Data (Big Data). :4054–4063.
With increased awareness comes unprecedented expectations. We live in a digital, cloud era wherein the underlying information architectures are expected to be elastic, secure, resilient, and handle petabyte scaling. The expectation of epic proportions from the next generation of the data frameworks is to not only do all of the above but also build it on a foundation of trust and explainability across multi-organization business networks. From cloud providers to automobile industries or even vaccine manufacturers, components are often sourced by a complex, not full digitized thread of disjoint suppliers. Building Machine Learning and AI-based order fulfillment and predictive models, remediating issues, is a challenge for multi-organization supply chain automation. We posit that Federated Learning in conjunction with blockchain and smart contracts are technologies primed to tackle data privacy and centralization challenges. In this paper, motivated by challenges in the industry, we propose a decentralized distributed system in conjunction with a recommendation system model (Matrix Factorization) that is trained using Federated Learning on an Ethereum blockchain network. We leverage smart contracts that allow decentralized serverless aggregation to update local-ized items vectors. Furthermore, we utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the network while maintaining their privacy. Based on our results, we argue that training a model over a serverless Blockchain network using smart contracts will provide the same accuracy as in a centralized model while maintaining our serverless model privacy and reducing the overhead communication to a central server. Finally, we assert such a system that provides transparency, audit-ready and deep insights into supply chain operations for enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS).
Al-Eidi, Shorouq, Darwish, Omar, Chen, Yuanzhu, Husari, Ghaith.  2021.  SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning. IEEE Access. 9:177–191.
With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve.
Conference Name: IEEE Access
Alqarni, Hussain, Alnahari, Wael, Quasim, Mohammad Tabrez.  2021.  Internet of Things (IoT) Security Requirements: Issues Related to Sensors. 2021 National Computing Colleges Conference (NCCC). :1–6.
The last couple of years have seen IoT-enabled sensors continuing to experience massive growth. Sensors have enhanced the possibility of large-scale IoT deployments in grid systems, vehicles, homes, and so forth. A network that incorporates different embedded systems has the underlying capability of transmitting information and receiving instructions through distributed sensor networks. Sensors are especially essential in gathering different pieces of information that relate to different IoT devices. However, security has become a critical concern for sensor networks that are enabled by the IoT. This is partly because of their design limitations like limited memory, weak processing capability, weak processing ability, and exposure to entities that are malicious. Even more, some ad hoc wireless sensor networks that are enabled by IoT are to some extent also prone to frequent changes in topology. This dynamic aspect tends to aggravate the security issues that are associated with sensors, thus enhancing the need to find a lasting solution. This paper sheds light on the IoT security requirements with special attention to issues related to sensors.
Cui, Jie, Kong, Lingbiao, Zhong, Hong, Sun, Xiuwen, Gu, Chengjie, Ma, Jianfeng.  2021.  Scalable QoS-Aware Multicast for SVC Streams in Software-Defined Networks. 2021 IEEE Symposium on Computers and Communications (ISCC). :1—7.
Because network nodes are transparent in media streaming applications, traditional networks cannot utilize the scalability feature of Scalable video coding (SVC). Compared with the traditional network, SDN supports various flows in a more fine-grained and scalable manner via the OpenFlow protocol, making QoS requirements easier and more feasible. In previous studies, a Ternary Content-Addressable Memory (TCAM) space in the switch has not been considered. This paper proposes a scalable QoS-aware multicast scheme for SVC streams, and formulates the scalable QoS-aware multicast routing problem as a nonlinear programming model. Then, we design heuristic algorithms that reduce the TCAM space consumption and construct the multicast tree for SVC layers according to video streaming requests. To alleviate video quality degradation, a dynamic layered multicast routing algorithm is proposed. Our experimental results demonstrate the performance of this method in terms of the packet loss ratio, scalability, the average satisfaction, and system utility.
Nath, Shubha Brata, Addya, Sourav Kanti, Chakraborty, Sandip, Ghosh, Soumya K.  2021.  Container-based Service State Management in Cloud Computing. 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM). :487—493.
In a cloud data center, the client requests are catered by placing the services in its servers. Such services are deployed through a sandboxing platform to ensure proper isolation among services from different users. Due to the lightweight nature, containers have become increasingly popular to support such sandboxing. However, for supporting effective and efficient data center resource usage with minimum resource footprints, improving the containers' consolidation ratio is significant for the cloud service providers. Towards this end, in this paper, we propose an exciting direction to significantly boost up the consolidation ratio of a data-center environment by effectively managing the containers' states. We observe that many cloud-based application services are event-triggered, so they remain inactive unless some external service request comes. We exploit the fact that the containers remain in an idle state when the underlying service is not active, and thus such idle containers can be checkpointed unless an external service request comes. However, the challenge here is to design an efficient mechanism such that an idle container can be resumed quickly to prevent the loss of the application's quality of service (QoS). We have implemented the system, and the evaluation is performed in Amazon Elastic Compute Cloud. The experimental results have shown that the proposed algorithm can manage the containers' states, ensuring the increase of consolidation ratio.
Zhang, ZhiShuo, Zhang, Wei, Qin, Zhiguang, Hu, Sunqiang, Qian, Zhicheng, Chen, Xiang.  2021.  A Secure Channel Established by the PF-CL-AKA Protocol with Two-Way ID-based Authentication in Advance for the 5G-based Wireless Mobile Network. 2021 IEEE Asia Conference on Information Engineering (ACIE). :11–15.
The 5G technology brings the substantial improvement on the quality of services (QoS), such as higher throughput, lower latency, more stable signal and more ultra-reliable data transmission, triggering a revolution for the wireless mobile network. But in a general traffic channel in the 5G-based wireless mobile network, an attacker can detect a message transmitted over a channel, or even worse, forge or tamper with the message. Building a secure channel over the two parties is a feasible solution to this uttermost data transmission security challenge in 5G-based wireless mobile network. However, how to authentication the identities of the both parties before establishing the secure channel to fully ensure the data confidentiality and integrity during the data transmission has still been a open issue. To establish a fully secure channel, in this paper, we propose a strongly secure pairing-free certificateless authenticated key agreement (PF-CL-AKA) protocol with two-way identity-based authentication before extracting the secure session key. Our protocol is provably secure in the Lippold model, which means our protocol is still secure as long as each party of the channel has at least one uncompromised partial private term. Finally, By the theoretical analysis and simulation experiments, we can observe that our scheme is practical for the real-world applications in the 5G-based wireless mobile network.
Madi, Nadim K. M., Madi, Mohammed.  2020.  Analysis of Downlink Scheduling to Bridge between Delay and Throughput in LTE Networks. 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). :243–247.
The steady growing trend of user demand in using various 4G mobile broadband applications obligates telecom operators to thoroughly plan a precise Quality of Service (QoS) contract with its subscribers. This directly reveals a challenge in figuring out a sophisticated behavior of radio resources (RBs) at the base station to effectively handle the oscillated loads to fulfill their QoS profiles. This paper elaborates on the above issue by analyzing the behavior of the downlink packet scheduling scheme and proposes a solution to bridge between the two major QoS indicators for Real-Time (RT) services, that are, throughput and delay. The proposed scheduling scheme emphasizes that a prior RBs planning indeed has an immense impact on the behavior of the deployed scheduling rule, particularly, when heterogeneous flows share the channel capacity. System-level simulations are performed to evaluate the proposed scheduling scheme in a comparative manner. The numerical results of throughput and delay assured that diverse QoS profiles can be satisfied in case of considering RBs planning.
Anisetti, Marco, Ardagna, Claudio A., Berto, Filippo, Damiani, Ernesto.  2021.  Security Certification Scheme for Content-centric Networks. 2021 IEEE International Conference on Services Computing (SCC). :203–212.
Content-centric networking is emerging as a credible alternative to host-centric networking, especially in scenarios of large-scale content distribution and where privacy requirements are crucial. Recently, research on content-centric networking has focused on security aspects and proposed solutions aimed to protect the network from attacks targeting the content delivery protocols. Content-centric networks are based on the strong assumption of being able to access genuine content from genuine nodes, which is however unrealistic and could open the door to disruptive attacks. Network node misbehavior, either due to poisoning attacks or malfunctioning, can act as a persistent threat that goes unnoticed and causes dangerous consequences. In this paper, we propose a novel certification methodology for content-centric networks that improves transparency and increases trustworthiness of the network and its nodes. The proposed approach builds on behavioral analysis and implements a continuous certification process that collects evidence from the network nodes and verifies their non-functional properties using a rule-based inference model. Utility, performance, and soundness of our approach have been experimentally evaluated on a simulated Named Data Networking (NDN) network targeting properties availability, integrity, and non-repudiation.
Liu, Ying, Han, Yuzheng, Zhang, Ao, Xia, Xiaoyu, Chen, Feifei, Zhang, Mingwei, He, Qiang.  2021.  QoE-aware Data Caching Optimization with Budget in Edge Computing. 2021 IEEE International Conference on Web Services (ICWS). :324—334.
Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.
Pasias, Achilleas, Kotsiopoulos, Thanasis, Lazaridis, Georgios, Drosou, Anastasios, Tzovaras, Dimitrios, Sarigiannidis, Panagiotis.  2021.  Enabling Cyber-attack Mitigation Techniques in a Software Defined Network. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). :497–502.
Software Defined Networking (SDN) is an innovative technology, which can be applied in a plethora of applications and areas. Recently, SDN has been identified as one of the most promising solutions for industrial applications as well. The key features of SDN include the decoupling of the control plane from the data plane and the programmability of the network through application development. Researchers are looking at these features in order to enhance the Quality of Service (QoS) provisioning of modern network applications. To this end, the following work presents the development of an SDN application, capable of mitigating attacks and maximizing the network’s QoS, by implementing mixed integer linear programming but also using genetic algorithms. Furthermore, a low-cost, physical SDN testbed was developed in order to evaluate the aforementioned application in a more realistic environment other than only using simulation tools.
Jahan, Sharmin, Gamble, Rose F..  2021.  Applying Security-Awareness to Service-Based Systems. 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C). :118—124.
A service-based system (SBS) dynamically composes third-party services to deliver comprehensive functionality. As adaptive systems, SBSs can substitute equivalent services within the composition if service operations or workflow requirements change. Substituted services must maintain the original SBS quality of service (QoS) constraints. In this paper, we add security as a QoS constraint. Using a model problem of a SBS system created for self-adaptive system technology evaluation, we demonstrate the applicability of security assurance cases and service security profile exchange to build in security awareness for more informed SBS adaptation.