Showing papers in "Journal of Network and Computer Applications in 2020"

TL;DR: A taxonomy of the security research areas in IoT/IIoT along with their corresponding solutions is designed and several open research directions relevant to the focus of this survey are identified.

TL;DR: This survey aims at providing a systematic and detailed overview of machine learning techniques for malware detection and in particular, deep learning techniques with special emphasis on deep learning approaches.

TL;DR: The architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks, and ML-based potential solutions for IoT security has been presented and future challenges are discussed.

TL;DR: A state-of-art survey on the integration of blockchain with 5G networks and beyond, including discussions on the potential of blockchain for enabling key 5G technologies, including cloud/edge computing, Software Defined Networks, Network Function Virtualization, Network Slicing, and D2D communications.

TL;DR: This comprehensive survey both studies and summarizes the existing UAV-assisted research, such as routing, data gathering, cellular communications, Internet of Things (IoT) networks, and disaster management that supports existing enabling technologies.

TL;DR: This paper provides the first comprehensive review of the state-of-the-art sidechains and platforms, identifying current advancements and analyzing their impact from various viewpoints, highlighting their limitations and discussing possible remedies for the overall improvement of the blockchain domain.

TL;DR: A new lightweight authentication mechanism in cloud-based IoT environment, called LAM-CIoT, which offers better security, and low communication and computation overheads as compared to the closely related authentication schemes.

TL;DR: A taxonomy of deep learning models in intrusion detection is introduced and desirable evaluation metrics on all four datasets in terms of accuracy, F1-score and training and inference time are suggested.

TL;DR: This work presents some important edge computing architectures and classify the previous works on computation offloading into different categories, and discusses some basic models such as channel model, computation and communication model, and energy harvesting model that have been proposed in offloading modeling.

TL;DR: A taxonomy of the current network traffic classification within the IoT context is presented and commercial and real-world use cases of the IoT traffic classification are exposed and open research issues and challenges in this domain are outlined.

TL;DR: An in-depth review of existing blockchain-based identity management papers and patents published between May 2017 and January 2020 is provided, which identifies potential research gaps and opportunities that will hopefully help inform future research agenda.

TL;DR: The experiments show that the IoT micro-security add-on running the proposed CNN model is capable of detecting phishing attacks with an accuracy of 94.3% and a F-1 score of 93.58%.

TL;DR: A description of security threats that menace SDN and a list of attacks that take advantage of vulnerabilities and misconfigurations in SDN constitutive elements are described and a discussion emphasizing the duality SDN-for-security andSDN-security is presented.

TL;DR: This survey details the simulation tools of IoT networks, IoT sensors along with their recent application areas, broad IoT research challenges, as well as in-depth analysis of IoT research history and recommendations that attract current IoT researchers' attention.

TL;DR: This paper introduces various link prediction approaches and addresses how researchers combined link prediction as a base method to perform other applications in social networks such as recommender systems, community detection, anomaly detection and influence analysis.

TL;DR: A comprehensive survey of the work conducted over the period 2010-2018 in various areas of human activity recognition with main focus on device-free solutions and proposes a new taxonomy for categorizing the research work conducted in the field of activity recognition.

TL;DR: This work introduces some privacy security risks of cloud computing and proposes a comprehensive privacy security protection framework, and shows and discusses the research progress of several technologies, such as access control; ciphertext policy attribute-based encryption (CP-ABE); key policy attributes based encryption (KP-ABe); and multi-tenant, trust, and a combination of multiple technologies.

TL;DR: In this article, the authors provide insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures.

TL;DR: This paper presents a comprehensive survey on the blockchain- based storage systems and how they work and then compares them with cloud-based storage networks.

TL;DR: Existing emerging vulnerabilities of blockchain related to the recent attacks on bitcoin and etherum is presented along with the defensive methodologies and future trends in blockchain are presented.

TL;DR: A sophisticated traffic reduction mechanism, integrated with a reinforcement learning technique is proposed, which achieves a relatively low false positive rate and achieves a detection rate of 98.3%.

TL;DR: The experimental results show that as compared with traditional supervised classifiers, the approach is more effective in detecting intrusions and reducing false alarms by automatically leveraging unlabeled data.

TL;DR: In this article, the authors present a comprehensive study of SDN interfaces and programming protocols, which are primarily classified into southbound, northbound, and east/westbound interfaces.

TL;DR: A detailed survey of existing applications of ML for intelligent optical networks is presented, classified in terms of their use cases, which are categorised into optical network control and resource management, and optical network monitoring and survivability.

TL;DR: A comprehensive survey of recent ML-based detection techniques, including intrusion detection against adversarial attacks, collaborative and decentralized detection framework, detection with privacy preservation, and some potential advanced ML techniques are provided.

TL;DR: Three mainstream algorithms are used to generate adversarial examples to attack three classical deep learning algorithms for SAR image target recognition, showing that SAR target recognition algorithms based on deep learning are potentially vulnerable to adversarialExamples.

TL;DR: This paper is aimed to analyze and explore various research challenges and open issues related to security and use of IoT protocols, and highlight the research trends and simulation tools used for analysis of IoT layer protocols.

TL;DR: A practical taxonomy based on the contents of the present research studies is represented as resource-management-based and service- management-based approaches and an abreast comparison of the aforementioned solutions is demonstrated and assesses them under the same evaluation factors.

TL;DR: The paper includes extensive reviews on different frameworks and categories for state-of-the-art techniques, critical analysis of their performances, and discussions of their applications, trends and future directions to serve as guidelines for readers towards this emerging research area.

TL;DR: This work identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH), throughput maximization, spectrum management, network utility, and power allocation.