Cybersecurity through the lens of Digital Identity and Data Protection: Issues and Trends

The use of a secure and robust digital identification system that is capable of protecting privacy is an essential, reliable and user-friendly element for a strong cyber resilience strategy and is a source of new business opportunities and applications for banks, private sector with a return on their investment.The march towards Digital Identity is well underway therefore, focus should be on both adoption and adaption of the new structures and regulations. These are needed to govern the associated services and transactions as well as establishing laws that enforce penalties for violations.There is no doubt then that more and more entities and institutions would move to the cloud. Security challenges affecting the cloud may not be new but the mode of addressing them would be different. The authors develop a Data Colouring technique for securing data processed or stored on both cloud and non-cloud platforms. The technique combines Public Key Infrastructure (PKI), concatenated fingerprints and digital watermarking. Using this technique, data can be secured at creation or during storage and remains secure during processing.     

基于数据运营安全的个人信息保护

个人信息因其自身携带隐私特性,与每个个体息息相关。个人信息保护不当,影响公众权益、企业利益以及社会秩序。在互联网、大数据、5G万物互联的时代,个人信息被广泛收集和使用,必须妥善解决个人信息保护问题,才能保障整个数据产业健康发展。而现有的个人信息保护方法或技术,不足以应对新形势下的保护诉求。基于数据运营安全的个人信息保护,针对当前个人信息保护的新形势和新诉求,提出结合人工智能,通过数据运营安全对结构化、半结构化、非结构化的个人信息流动的保护,涵盖从生产到运维,从采集、传输、存储、处理、分析、共享、销毁全生命周期保护,深入数据运营中内嵌防护,同时与业务解耦,达到保护个人信息安全的目标。     

中国石油工业上游发展面临的挑战与未来科技攻关方向

石油和天然气是全球和中国最重要的一次能源。石油工业的生存发展是由油气资源、市场、技术和社会政治经济环境等要素决定的，其中，技术进步是最活跃和最关键的因素之一。中国已成为全球油气生产消费大国，中国石油工业上游的发展也高度依赖石油科技的进步。中国石油工业已形成了先进完整的理论技术研发和装备制造体系，支撑了油气产量持续稳产和增产。未来是中国社会经济发展的关键期，石油工业必将面对重大挑战与新的技术需求，大力实施国家创新战略，发展具有国际领先水平的新一代勘探开发理论技术，支撑油气产业发展，保障国家油气能源安全。中国石油工业上游在未来面临的重大挑战与技术需求包括：①满足中国未来现代化建设的巨大油气需求和保障油气供应安全，这当中必须加大中国油气勘探开发，同时进一步扩大全球及"一带一路"油气投资与生产；②实现中国石油长期稳产2×10⁸t/a以上；③实现中国天然气产量上升至3 000×10⁸m³/a并长期稳产；④发展海洋及深水油气勘探开发先进技术与装备；⑤形成新一代石油工程服务技术装备和数字化转型。中国石油工业上游未来的科技攻关方向和研发重点包括：①先进的石油大幅度提高采收率技术；②大气田勘探与复杂气田提高采收率技术；③非常规油气勘探开发技术；④海洋及深水油气勘探开发技术及装备；⑤"一带一路"油气勘探开发技术；⑥新一代石油工程服务技术装备和数字化转型。     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Predicting employee information security policy compliance on a daily basis: The interplay of security-related stress,emotions, and neutralization

We conceptualized security-related stress (SRS) and proposed a theoretical model linking SRS, discrete emotions, coping response, and information security policy (ISP) compliance. We used an experience sampling design, wherein 138 professionals completed surveys. We observed that SRS had a positive association with frustration and fatigue, and these negative emotions were associated with neutralization of ISP violations. Additionally, frustration and fatigue make employees more likely to follow through on their rationalizations of ISP violations by decreased ISP compliance. Our findings provide evidence that neutralization is not a completely stable phenomenon but can vary within individuals from one time point to another.     

Information Security Policy Compliance: Leadership and Trust

ABSTRACT

The purpose of this study was to examine the influence of trust variables (trust: competence, trust: benevolence, trust: integrity) on leadership regarding the organization’s information security policy (ISP) compliance. An instrument with four constructs was used to collect data from 474 non-management subjects from various organizations in the USA. Collected data were analyzed through multiple regression procedure. Results revealed that all trust variables (trust: competence, trust: benevolence, trust: integrity) were influential in predicting the leadership regarding the organization’s ISP compliance. The findings are discussed and implications for practice are outlined. Conclusion, limitations, and recommendations for future research are drawn.     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.