CCF推荐 | SCI期刊专刊信息3条

全文截稿: 2021-02-01

影响因子: 3.062

CCF分类: B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

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

The emerging zero trust security shows great promises of vastly enhanced security, usability, data protection and governance in cloud computing environments, which assumes that all participants, systems, or services operating from within the cloud-based perimeter are not trusted by default and instead must verify everything trying to access the cloud-based system. In cloud-based system, the traditional perimeter security approaches are vulnerable for data breaches and cyberattacks. The zero-trust model provides new security model that requires restrict access control and trusts nothing by default for any user, applications, or services in both inside and outside a cloud-based system perimeter. The zero trust specifically effect in cloud computing environment, where enterprises should not inherently trust any users, application, and any attempt to access a system or application must be verified before granting access. The zero-trust security model can significantly improve the security of cloud by creates a map of what it has in the cloud and implement strong access control, including multi-factor authentication, adaptive access control, risk-based adaptive authentication, artificial intelligence enabled dynamic security policies, and more.

The aim of this special issue is to foster novel and multidisciplinary approaches that improve the security in cloud environments by addressing challenges in zero trust security in cloud environments, including fitting with legacy network-centric methods, strengthening security of user and application centric approaches, supporting access to internal apps from any device anywhere, etc.

This special issue will focus on (but not limited to) the following topics:

Zero trust methods and models for cloud security solutions

Zero trust architecture in cloud computing

Zero trust security architecture/framework in cloud computing

Micro-segmentation in cloud systems

Autonomic security for zero trust cloud-based systems

Real-time access control and optimisation

Granular perimeter security

Multifactor authentication in zero trust model

Governance policies in cloud computing

Zero trust identity management multi-factor authentication (MFA)

Access privilege in zero trust enabled cloud environments

Cyberattacks in zero trust environments

Granular access control and security

Implementation of zero trust security in cloud computing

Machine learning enabled zero trust security in cloud computing

全文截稿: 2021-02-15

影响因子: 5.768

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 - 1区

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

Personality traits are generally referred to as relatively stable patterns of thoughts, feelings, and behaviours that have been associated with a wide range of important life outcomes and choices. Specifically, personality traits have repeatedly been related to the individual (e.g., well-being, psychopathology), interpersonal (e.g., relationship satisfaction), and social-institutional outcomes (e.g., occupational choices, job success). Hence, in the recent years, there has been a massive increase in the interest to develop models which use online data on human behaviour and preferences (i.e., digital footprints) to automatically predict an individuals’ personality traits.

Advances in consumer electronics (e.g., smartphones, wearables) and the subsequent development of mobile sensing methods have facilitated the collection of highly detailed, multi-dimensional data on behaviours and situations. Social media gives users the opportunity to build an online persona through posting of content such as text, images, links or through interaction with others. The way in which users present themselves is a type of behaviour usually determined by differences in demographic or psychological traits. The behavioural residue harvested from websites and online social media platforms is also another valuable source of data on behaviour linked to personality traits. Hence, automated personality prediction has important practical applications in diverse areas ranging from recommendation systems, computational advertising, marketing science, job screening to aiding in psychological counselling, intervention and therapy, enhanced human-computer interaction, etc. It is also interesting to see the benchmarking studies with regard to the sensitivity of the data, prediction performance of the models, and cost for businesses to securely store the data, models, react to GDPR (General Data Protection Regulation) requests, etc.

However, it should be noted that automated personality prediction is a controversial topic and serious concerns have been raised with regard to implications for individual privacy and the conception of informed consent. While the performance of these models is not high enough to allow for the precise distinction of people based on their traits, predictions can still be "right" on average and be utilized for digital mass persuasion and for personalization efforts. Focusing research on explainable models, rather than just using them as black-box personality predictors can help to bridge the seemingly distant fields of computational personality detection and personality research in psychological science.

The primary objective of this special issue is to bring together diverse, novel and impactful work on personality prediction in one place, thereby accelerating research in this field.

The topics of interest for this special issue include, but are not limited to:

Personality prediction from multimodal and diverse input modalities (e.g., audio, video, text) along with new approaches effectively fusing features extracted from multiple sources (for e.g., using heterogeneous data collected from different devices)

Deep learning-based approaches (e.g., CNNs, GANs for data augmentation, deep RL, etc.)

Machine learning for automated personality prediction from user behaviour. For example:

○ Social media interaction

○ Author profiling based on writing

○ Consumer device usage patterns (e.g., wearable devices, smartphones, etc.)

全文截稿: 2021-02-28

影响因子: 1.15

CCF分类: C类

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/integration