计算机网络 | CCF推荐 | SCI期刊专刊信息3条

全文截稿: 2021-01-31

影响因子: 3.03

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：硬件 - 3区

• 小类 : 计算机：信息系统 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/computer-networks

In recent years, world-wide users have enjoyed diverse desktop/mobile applications driven by rich information, which is delivered by the emerging networking technologies such as Software Defined Networking, 5G, and IoT. As such applications proliferate, however, it is increasingly challenging for network operators to cope with the complexity of network applications, dynamics of network traffic and non-stop security threats to network infrastructure. One of the core elements to secure and scalable network management and operation is the in-depth, non-intrusive and timely understanding of network traffic flowing through the network infrastructure. Albeit the growing number of AI and machine learning based studies on network traffic analytics, there remain many open questions due to the fluidity of the network flows, variety of objectives and security and privacy considerations. For example, there is not a comprehensive, open dataset for benchmarking the proposed algorithms and designs.

This special issue aims to provide a venue for the community to present and discuss the latest advances in network traffic analytics with an emphasis on novel machine learning based approaches and/or Software Defined Networking (SDN) environment. The analytics is broadly defined, including but not limited to flow classification, volume predication, measurement driven management, and covers the full life cycle of network flows. Given the recent development of AI driven applications, we see this an excellent opportunity for networking researchers to interact with ML/AI community to foster new knowledge and advance the state of art. We hope this special issue will catalyze the research and development of novel methods for network traffic analytics with rich datasets, vigorous discussions, new directions and fruitful collaborations.

Relevant topics/areas include but are not limited to


Network traffic analysis using machine learning methods

Malware detection using machine learning methods

Deep learning-based network traffic analytics

Encrypted network traffic analysis

Privacy preserving network traffic analysis

SDN support for network traffic analytics and measurement

Benchmarking and datasets for network traffic analysis

Comparison and validation of AI tools in networking domain

Platforms and environments enabling realistic network traffic capturing and analysis

Real-time network traffic analytics in uCPE and SD-WAN

Network traffic analysis in embedded systems with limited computing and memory resources

Hardware and software solutions for accelerated, high throughput network traffic analytics


Important Dates:

Deadline of initial submission: Jan 31, 2021

Completion of first round review: March 31, 2021

Deadline for revised manuscripts: May 31, 2021

Completion of review and final decision: July 1, 2021

Publication: Sept 1, 2021.

Lead Guest Editors:

Yan Luo, University of Massachusetts Lowell ([email protected])

Tong Zhang, Intel Corporation

Guest Editors:

Hongxin Hu, Clemson University

Franck Le, IBM

Peilong Li, Elizabethtown College

Richard Yang, Yale University

全文截稿: 2021-04-15

影响因子: 3.03

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：硬件 - 3区

• 小类 : 计算机：信息系统 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/computer-networks

In recent years, Blockchain and Federated Learning (FL) are both making great technological advances independently. Blockchain provides a distributed and secure decentralized technique to process and authenticate transactions. On the other hand, FL enables end-devices to collaboratively train and update a mutual machine learning model while preserving the privacy of their data-sets. Both technologies are known to have several desirable advantages for today's needs in terms of security and privacy. Moreover, when blockchain and FL technologies areenabled in B5G and 6G, security and privacyare preserved with full connectivity and distributivity. As both blockchain and FL advance further, the research focus is turning to integrate and unify the technologies to produce novel and smart networking services and applications. Currently, for large-scale advanced services and applications, the proposed solutions are far from practical. There are still many unresolved technical challenges in having systems that are scalable, and robust, and able to handle exponentially growing data.

Both blockchain and FL, supported bynext-generation networking (NGN) technologies, including Beyond 5G and 6G, have the potential to address smart networking services and applications’ shortcomings by providing power computing processing and handling massive volumes of generated data. The benefits of synergizing these two technologies with NGN will without doubt provide a revolutionized approach towards widespread smart networking services and applications. Smart healthcare, Internet of Things, intelligent and industrial automation (industry 4.0), cyber-physical sensory systems, and smart and critical infrastructures, energy trading, smart shopping, smart banking, and efficient manufacturing procedures are examples of use cases that may receive impressive benefits from integrating these technologies. As such, this special issue aims to explore recent advances and disseminate state-of-the-art research on blockchain-based solutions and FL services and applications focused on Computer Networking issues.



Topics of Interest

Researchers, developers, and industry experts are welcome to contribute to one of the followings topics or slightly similar ones:

● Integration of Blockchain and FL in Beyond 5G/6G Network Architectures

● FL for large-scale Internet of Things

● FL in vehicular networks

● Blockchain with lightweight computation

● Blockchain-based service and applications for vehicular clouds

● FL for future internet architectures

● Scalable Blockchain for intelligent networking services

● Application of FL in large-scale intelligent networking services

● Blockchain for emerging networks

● Byzantine-tolerant FL

● Churn-tolerant FL

● FL for NGN and 6G

● FL for IoT healthcare systems



Submission Guidelines:

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue and the journal theme. All submitted papers will be evaluated on the basis of relevance, the significance of contribution, technical quality, and quality of presentation. Manuscripts need to be prepared according to Guide for Authors as published in the Computer Networks Journal at https://www.elsevier.com/journals/computer-networks/1389-1286/guide-for-authors. We invite the prospective authors to submit their manuscript, via the online submission system on the main journal page. Please make sure you mention in your cover letter that you are submitting to this special issue. The timetable is as follows:

● Manuscript Submission Deadline: 15 April 2021

● Initial Decision Date: 15 June 2021

● Revised Manuscript Due: 15 July 2021

● Final Decision Date: 15 August 2021

● Final Manuscript Due: 30 October 2021



Guest Editors

●Moayad Aloqaily, xAnalytics Inc., Ottawa, ON, Canada

●Yaser Jararweh, Duquesne University, USA

●Lewis Tseng, Boston College, Chestnut Hill, MA, USA

●Giuseppe Piro,Politecnico di Bari, Italy

全文截稿: 2021-06-20

影响因子: 2.766

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：信息系统 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 电信学 - 3区

网址: http://www.journals.elsevier.com/computer-communications