计算机 | CCF推荐SCI期刊专刊信息9条

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Heterogeneous Computation in Specific Domain Accelerations (HC-SDA)

全文截稿: 2019-05-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Data are being generated at an unprecedented rate in the IoT era across various applications. How to process the Big Data in a timely manner is a major obstacle we are facing nowadays. The growing diversity and heterogeneity of the hardware platform just added another layer of difficulty on top of a challenging problem. Even though heterogeneous platform such a GPUs, Xeon Phis, and FPGAs, has been widely adopted. However, how to effectively and efficiently utilize different hardware accelerators together to serve one single application remains a challenge for heterogeneous computing researchers.

In this special issue, we seek original unpublished research on algorithms, models, applications and tools for heterogeneous computing to accelerate the performance, to improve energy efficiency, and to enhance reliability of heterogeneous platforms from edge to cloud and in between. We are particularly interested in heterogeneous computing research employing two or more different types of hardware accelerators.

Topics of interest include, but are not limited to, the following areas:

Microarchitecture design on heterogeneous processor/system combined with emerging memory/storage system (PCM, SSD, etc.)

Heterogeneous parallel programming paradigms and models.

Energy efficient parallel accelerating models for heterogeneous platforms.

Parallel algorithms for heterogeneous and/or hierarchical systems, including many-cores and hardware accelerators (FPGAs, GPUs, Xeon Phis, etc.)

Heterogeneous computation supports for autonomous vehicle driving, or other applications using artificial intelligent algorithms (e.g. images processing, features recognition, etc.)

Heterogeneous computing in large-scale datacenter.

Software engineering implementation on heterogeneous computing.

Multiple objectives optimization on heterogeneous platforms.

Task scheduling algorithms on heterogeneous computation, cloud and datacenter platforms.

Experience of porting parallel software from supercomputers to heterogeneous platforms.

Fault tolerance of parallel computations on heterogeneous platform.

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Big Data Analytics and Artificial Intelligence for Cyber Crime Investigation and Prevention

全文截稿: 2019-06-07

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Cybercrimes can be considered as a major challenge as they are not gathered in a finite set of local crime scenes. Possible traces of evidences are spread across multiple systems, with multiple victims, and cross more jurisdictions than ever before. It is difficult to have human experts to efficiently correlate data from different crimes and crime scenes. A strong demand for advanced data analytic has expanded and becoming disruptive. Another challenge is varieties of file formats, compression, encryption, file systems, etc.

Over last few years authors have been raising an importance of advanced data analytics for digital forensics in their research. Such that digital forensics is already considered to be a big data challenge and therefore require complete rethinking of principles and workflow (Franke 2008). The problem that investigators face now is that their tools - developed to analyse early 2000's technology - are no longer sufficient. For example, most of the computers in 1990th had storage equal to hundreds of MBytes. This means that most of the files could be reviewed by a single person in a timely manner. In 2018, smartphones have 128 GBytes storage, while computers and laptops hit 2-4 TBytes disk storage level already. Such capacity makes manual investigations simply infeasible. An idiom to this is the 'needle in the haystack' referring to a need to filter out the noise and to discover patterns in large heaps of data to uncover tiny pieces of evidence. However, the same method finding the needle in one haystack, does not necessarily work for another haystack. Therefore, there is a need for research for new ways of thinking and processing methods. For example, research into data reduction techniques, data mining and intelligent analysis (Quick et al. 2014).

The Scope of the Special Issue

The objective of the special issue is to attract research of novel methods, techniques and data analytic approaches, previously unpublished or substantially improved previous contributions (with at least 60% of new material). Authors of papers that fit these criteria will be invited to submit their contributions to the Special Issue. Moreover, following the positive feedback and great interest last year, authors of nominated best papers will be invited to submit their extended contributions from the 2nd International Workshop on Big Data Analytic for Cybercrime Investigation and Prevention 2018.

Selection and Evaluation Criteria

1. Relevance to the cybercrime investigation and prevention

2. Applicability in large-scale digital evidences analytics

3. Research novelty and impact of the submitted work

4. Readability and technical quality

Research Topics

1. New development in data-driven methods

- Novel datasets

- New data formats

- Digital forensics data simulation

- Anonymised case data

- New data formats and taxonomies

2. Novel computational intelligence methods and improvement of existing algorithms

- Machine learning-aided analysis

- Graph-based detection

- Topic modelling

- Improvements of existing methods

- Decision support systems

3. Application areas and cross-domain information exchange

- Cyber threats intelligence

- Network forensics readiness

- Malware analysis & detection

- Emails mining & authorship identification

- Social network mining

- Events correlations

- Access logs analysis

- Mobile forensics

- Fraud detection

- Database forensics

- Internet of things forensics

- Blockchain technologies

- Industrial systems

4. Platforms, architecture and infrastructure for efficient data analytics

- Secure collaborative platforms

- Distributed storage and processing

- Technologies for data streams

- Hardware and software architectures for large-scale data

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Issue on Computing in Healthcare

全文截稿: 2019-06-15

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 3区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Healthcare and treatment have long been one of the main concerns of humans. Along with the invention of a variety of medical sensors that can sense the physiological signs of the human body as well as mobile computers (such as smartphones), which always accompany the users, computer science and information technology have entered the healthcare and treatment domain and various projects and research studies have been defined and completed. Nowadays, computer sciences and technologies play an important role in promoting healthcare services around the world. Employing robots in surgeries, machine learning and artificial intelligence techniques in medical diagnosis, pervasive computing systems for anytime, anywhere and high-quality medical care, and distributed systems (such as clouds) for processing huge volume of medical data are just a few examples of computer use in healthcare.

This special issue focuses on healthcare computing systems and technologies. In particular, this issue will publish papers on the computational theories, methodologies, techniques and tools that, in practice, can improve health services. This issue will consider revised and substantially extended version of selected papers presented at the "First Conference on Healthcare Computing Systems and Technologies (CHEST 2019) ", which will be held in Birjand, Iran on 17-18 of April, 2019. We also strongly encourage researchers who are unable to attend the conference to submit their outstanding papers to this special issue.

The topics of interest are:

1. Network infrastructures, architectures, and protocols for e-health

- Pervasive healthcare systems

- Big data in healthcare

- Parallel and distributed systems in healthcare

2. Data mining in healthcare

- Smart hospitals

- Continuous and event-driven monitoring of patients

- Image processing in healthcare

3. Modeling and simulation in healthcare

- Computer assisted learning in medical education

- Emerging cloud-based services and applications in healthcare

- E-Health services and applications for physical and mental health

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Blockchain-as-a-Service for Industrial Internet of Things and Big Data Applications (Baas-iot-big-data)

全文截稿: 2019-06-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

With the progressive development of information technology, the Industrial Internet of Things (IIoT) has become an essential component of the industrial systems. It connects billions of devices, intelligent and autonomous machines, industrial equipment, etc., to generate an unprecedented volume of industrial data. This humongous data has given birth to Big Data typically characterized by the 5V's, i.e., volume, velocity, variety, veracity, and variability. Thus, handling this massive amount of data in an efficient, secure and economical manner has become problematic. Further, the number of IoT devices is expected to surpass 50 billion by 2020 which in turn will open doors for a plethora of potential security and privacy risks. With such an exponential increase in the data, penetration of security threats in the IIoT domain will also witness a significant blow in the years to come. Amongst these, security and privacy issues will be the most crucial concerns. As IIoT has limitless applications in a wide variety of domains such as transportation systems, e-healthcare, smart grids, etc., the ongoing explosion in security breaches may diminish the confidence of industries towards the adoption of the IoT technology. To potentially overcome the deficiencies of IIoT, it requires a quick turnaround. Although there exist several works that address the security and privacy challenges in IIoT, limited research efforts have been made in providing distributed trust, security and privacy.

In order to cater to the problems mentioned above, blockchain has emerged as a dynamic and fast-growing research area that allows data and transactions to be recorded in a reliable and verifiable way. As a revolutionary technology, it combines distributed data storage, point-to-point transmission, consensus mechanisms, and encryption algorithms to record data and transactions efficiently. In order to simplify the management of trusted information over the communication networks, it provides several characteristics like decentralization, persistency, anonymity, and audit-ability. Thus, the convergence of IIoT with blockchains can potentially overcome the deficiencies of IIoT and Big Data Applications (BDA). Consequently, their wide-scale realization in the industrial domain can cater to a wide variety of services ranging from agriculture, healthcare, and power systems to retail, finance, and advertising. However, employing blockchain mechanisms in communication networks still has some technical challenges and limitations. For instance, efficient and reliable means to achieve interoperability between different blockchains is one of the significant problems. Additionally, another critical issue that requires serious attention relates to the minimization of overhead associated with blockchains. On top of these challenges, the adaptability of blockchain in IoT is still in its infancy and requires designing a concrete consensus for the same. Thus, the practical amalgamation of blockchain with IoT will serve as a new revolution in realizing the next generation IIoT and BDA.

Thus, this special issue aims to bring together researchers, developers and industry experts in order to foster the investigations on cutting-edge research and allowing one to contribute in advancing the blockchain innovation. With this topic, the research community from worldwide can expand an important discussion on the limitations of current state-of-the-art solutions and develop new algorithms, technologies and protocols for addressing the various security, privacy and authentication issues in blockchain-IIoT/BDA integration.

Potential topics include, but are not limited to:

New architectures, frameworks, and models

Blockchain for lightweight protocol designs

Emerging applications of blockchain in Cloud, Edge and Fog computing

Blockchain based lightweight protocols for future wireless IIoT applications

AI and ML based blockchain solutions for distributed systems

Blockchain in energy management/smart grids

Advancement of Blockchain in Cyber-Physical Systems

Distributed Blockchain Architectures for SDN and NFV

Other Blockchain implementations for IIoT and BDA

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Clusters, Clouds and Grids for Life Sciences (LIFE2019)

全文截稿: 2019-06-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

"Future Generation Computer Systems", a forum for the publication of peer-reviewed, high-quality original papers showing advances in distributed systems, collaborative environments, high performance and high-performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT), is seeking original manuscripts for aSpecial Issue on Clusters, Clouds and Grids for Life Sciences (LIFE2019)scheduled to appear in the second half of 2019.

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods like deep learning models rely heavily on large-scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges, such as biomodelling, predictive models of disease and treatment, evolutionary biology, medical biology, cell biology, biomedical image processing, biosignal sensoring or computer-supported diagnosis. Clouds, Edge/Fogs and Big Data Environments are promising to address research, clinical and medical research community requirements as they allow for significant reduction of computational time to run large experiments, for speeding-up development time for new algorithms, and to reduce barriers for large-scale multi-centric collaborations.

The special issue will provide a forum for presenting research works showing advances of bioinformatics and medical applications using distributed IT systems, new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making, and novel proposal to tackle specific challenges in Life Sciences computing such as security, traceability, data interoperability, simulation of complex models, creation of cloud services, or application of artificial intelligence techniques to enhance decisions and to speed up processes.

The special issue will be open to any author, but it will also invite extended versions of selected papers of CCGrid-Life 2019 workshop, held with CCGRID 2019, whose topics fit in the scope of this special issue. Each submission will be reviewed by at least three reviewers to ensure a very high quality of papers selected for the Special Issue.

AREAS OF INTEREST

This special issue of Future Generation Computing Systems will feature articles that discuss the following areas of interest:

Novel exploitation techniques of distributed IT resources for Life Sciences, HealthCare and research applications, for example medical imaging, disease modeling, bioinformatics, Public health informatics, drug discovery, and clinical trials.

New distributed algorithms applicable to medical and bioinformatics applications,

Modeling and simulation of complex biological processes (Genomics and Molecular Structure evolution, Molecular Dynamics, etc.)

New scientific gateways and user environments targeting distributed medical and bioinformatics applications Clouds for big data manipulation in bioinformatics and medicine.

Cloud services for life sciences (genomics as a service, medical image as a service, protein folding, etc.)

Biological data mining and visualization, ontologies and text mining.

New deep learning and machine learning experiences in Life Sciences.

Distributed and heterogeneous bioinformatics and medical data management for data-enabled life sciences, including standardization, interoperability for data exchanges, privacy, security and access control.

Novel development environments, programming paradigms and tools distributed bioinformatics applications

Big Medical and Bioinformatics Data applications and solutions for Clouds, Edge/Fogs and Big Data Environments.

New solutions for process optimizations using smart contracts and block chain technology.

Reproducibility and traceability of experiments in Life sciences.

Detailed application use-cases highlighting achievements and roadblocks using hybrid or public clouds.

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Issue on Recent Advances in Blockchain Infrastructures and Applications

全文截稿: 2019-06-30

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 3区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Blockchain has emerged as a disruptive, and transformational technology, with great potential, benefits, and impact. Blockchain is now foreseen as a panacea for many challenging problems across many businesses, industries, and domains. Many businesses and governments have been moving aggressively in adopting blockchain, with $2.1 billion spent globally on blockchain during 2018. According to Gartner, blockchain will require another 5 to 10 years to reach a state of maturity and become mainstream.

This special Issue focuses on recent advances in research and development of blockchain infrastructures and applications. Researchers are invited to contribute high quality articles on the latest advances related to improving and solving key challenges within blockchain underlying infrastructure, networks, architecture, platforms, and algorithms to improve the overall blockchain performance, scalability, governance, privacy, and security. We also encourage researchers to submit articles sharing experiences on the application and use of blockchain to solve problems seen in diverse domains, including finance, digital identity, supply chain, logistics, manufacturing, healthcare, real estate, food industry, education and certification, insurance, oil and gas, real estate, and voting. Researchers are also invited to share articles on the many other emerging innovative blockchain-based applications such as prediction markets, bidding for smart traffic control, shared compute power, and many others. Furthermore, we welcome papers on the integration of blockchain with other emerging technologies such as AI, IoT, 3D printing, big data, fog computing, robotics, and smart connected devices.

Topics:

Both original practical work and review articles are welcome. Topics of interest include:

Interoperability of blockchains

Cross-blockchain ecosystems

Decentralized applications

Consortium governance and ownership models

Privacy and security of ledgers

Secure smart contracts

Formal verification for smart contract code

Smart contract languages and oracles

Trusted Execution Environment for blockchain

Zero knowledge proof

Consensus mechanisms

Scalability and performance

Identity and reputation management

Blockchain-based IoT, fog, cloud, and mobile devices

Supporting architecture for blockchain

Side chains and channels

Auditability and traceability

On-chain and off-chain applications

Blockchain in AI, ML, cyber security, IAM.

Distributed storage in blockchain

Building private blockchain systems

Blockchain tools, simulators, and test-nets

Blockchain PaaS

GDPRP solutions for blockchain ledgers

Decentralized financing and payments

Applications for smart cities, governments, and manufacturing

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Machine Learning and Big Data Analytics for IoT Security

全文截稿: 2019-07-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

The "Internet of things" heralds the connections of a nearly countless number of devices to the internet thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks. The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analysed data is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is one of them.

The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth.

One of the main challenge of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyse, detect and defend cyber-attacks in the distributed IoT systems. The IoT security includes: (i) the information security of the cyber space, and (ii) the device and environmental security of the physical space. These challenges call for novel approaches to consider the parameters and elements from both spaces, and get enough knowledge for ensuring the IoT's security. As the data has been collecting in the IoT, and the data analytics has been becoming mature, it is possible to conquer this challenge with novel machine learning or deep learning methods to analyse the data which synthesize the information from both spaces.

Therefore, this special issue will explore the potentials of machine learning and big data analytics by going beyond the existing simple approaches and present more advanced practices with well authentic implementations and results both at academic and industrial level. We believe that machine learning and big data analytics will play a vital role to provide and enhance the security of IoT and enable organizations to make crucial changes to their security landscape.

Topics of Interest

This special issue seeks recent important contributions on the machine learning and big data analytics for IoT security, with an emphasis on interdisciplinary approaches. Some topics of interest include, but are not limited to:

Novel machine learning and big data analytics methods for IoT security

Big data analytics/machine learning/deep learning for IoT security such as smart grid security analytics

Data mining and statistical modelling for the secure IoT

Machine learning and big data analytics architectures for IoT security

Machine learning based security detecting protocols

Machine learning experiments, test-beds and prototyping systems for IoT security

Analytics and machine learning applications to IoT security

Data based metrics and risk assessment approaches for IoT

Data confidentiality and privacy in IoT

Authentication and access control for data usage in IoT

Data-driven co-design of communication, computing and control for IoT security

Big data analytics/machine learning/deep learning edge/fog security

Emerging standards for IoT security

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Call-for-papers Computational Biostatistics and Biometrics in Internet-of-Medical-Things

全文截稿: 2019-08-31

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

In recent decades, there have been increasing utility of computational bio-statistical methods to clinical and health examination. This new generation of research is also known as biometrics or biometry in the Internet-of-Medical-Things (IoMT), and extends to applications such as medical research, epidemiology, clinical and public health science. Biostatistics involves the utility of quantification indicators to analyze medical data and signals from biological systems effectively. This involves determining correlation among different groups of data that seemed unrelated. In medical research, biostatistics has become an indispensable tool in enhancement of clinical diagnosis and treatment. For instance, it may be utilized in the study of cardiology, or respiratory disease, development and testing of new drugs, assessment of population health, analyzing the severity of cancer condition, evaluating mental health and psychiatric symptoms, monitoring disease infection, understanding biological system response or geographical patterns of disease, and studying anatomical dysfunction and disability. Biometry may also involve statistical work in areas of social and natural science, such as physics, chemistry, biology, agriculture, archaeology, veterinary, etc.

More specifically, future generation biostatistics techniques involve essential roles in designing studies and analyzing data from huge datasets biometry in the IoMT domain. It can formulate the scientific questions, determine the appropriate sampling techniques, coordinate data collection procedures, and perform the relevant statistical analyses to resolve the formulated scientific questions. In particular, active statistical methodologies include Bayesian methods, high-speed computing and simulation, data analysis, and key performance indicators for the analysis of IoMT data from epidemiologic studies or clinical trials.

This special issue focuses on the applications for problems in the medical domain, covering the solution for special problems or finding the potential correlation between diseases and some factors that seem unrelated using computational techniques. The main goal of this special issue is to provide the overview of the current state-of-the-art advances in health informatics and algorithms used for searching solutions as well as analyzing difficult treatment problems in IoMT.

Potential topics include, but are not limited to:

Medical statistical analysis, operation research, and information management in surgical, clinical, or psychiatric studies.

Biostatistical applications based on IoMT to provide online medical consultants including diagnosis, therapy planning, and treatment follow-ups, etc.

Deep learning based biometrics techniques in medical diagnostics.

Big data analysis techniques towards medical domain such as collection, analysis, learning, processing of widely used medical data.

Health informatics and statistical analysis of biological systems in the IoMT computational domain.

Management of biomedical data to assist with clinical decision-making and therapy guidance using statistics of IoMT data.

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Emerging Topics in Defending Networked Systems

全文截稿: 2020-01-25

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

In recent years, novel security threats arose, be it due to sophisticated malware obfuscation, anti-forensics techniques, advanced methods of network steganography/information hiding, newer de-anonymization methods or improved social engineering approaches. Increasingly heterogenous and inter-networked environments allow such threats to become more difficult to combat, e.g., due to the ever-broader spectrum of IoT and CPS protocols and heterogenous hardware platforms, over-complex frameworks for inter-connectivity and professionalization and funding of attackers.

Researchers aim to address these new threats with the development of novel methods (countermeasures) for defending networked systems. This is challenging and important at the same time. One of the most important advancements proposed by the community of security experts (both from industry and academia) deals with new forms of traffic normalization or active wardens, which allow to mitigate attacks, but do not offer a comprehensive protection. Moreover, novel attacks target highly specific features of the system to be exploited, for instance, vulnerabilities of the hardware and its energy consumption and network side channels.

In this perspective, this special issue desires to foster the progress in research on the development of novel defense methods in information security, especially for sophisticated and networked/hyper-connected systems, including those within the IoT and CPS.

Topics of interest include (but are not limited to):

Novel and effective countermeasures (techniques against modern threats, such as dynamic and adaptive countermeasures).

Methods that increase the efficiency and effectiveness of countermeasures over the state-of-the-art.

Surveys of defense methods in current domains of information security and surveys that systematize commonalities between different types of countermeasures.

Evaluation of existing taxonomies and proposals for new taxonomies in cyber defense.

Work that unifies terminological inconsistencies in cyber defense.

Work that reproduces existing experiments, i.e., that confirms/disproves experimental results on the defense of networked systems, and that additionally proposes experimentally verified improvements.

Work discussing methodologies to collect data and samples for modeling threats for the benefit of optimizing countermeasure design.

Work that discusses the underlying criteria for the design and evaluation for cyber defense research testbeds.

Work discussing machine-learning-based approaches for revealing unknown network-level threats.

Methodology for privacy, information sharing and collaborative work in the context of cyber defense.

"Open science" for cyber defense.

Policy issues that influence cyber defense.

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