Content an Insight to Security Paradigm for BigData on Cloud: Current Trend and Research

The sucesssive growth of collabrative applications prodcuing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presente

Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

Industrial Networks (INs) are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs), and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs) have received wide attention from the scientific community.While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of BigData frameworks such asHadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security,mainly focusing on the Information Technology (IT) domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing INbased ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development

A Comparative Analysis on Handling Big Data Using Cloud Services

In this era of technology, a lot of advancements have been done in almost every field such as medical, science, aerospace and other fields. With the increasing advancements in technology, a lot of data is being produced at the same time. For instances in the field of medicine there is a huge amount of data that is being generated as there are hundreds and thousands of patients who came for their checkup. So now the question arises where this huge amount of data is being stored. This huge amount of data is called as Big Data. And the major problem faced is how to manage and organize this huge amount of data along with its security and not being lost. Big data is used for extracting a lot of useful information but it is not easy to organize it. If the data is being lost than there are a lot of problems that can occur on a huge level as a lot of data being stored in big data is very confidential. This data can be stored on cloud which is the new advancement in the field of technology as it is highly reliable for huge amount of information. So, in this survey paper we will discuss about the solutions of organizing and handling big data proposed by different authors

The Efficacy of Perceived Big Data Security, Trust, Perceived Leadership Competency, Information Sensitivity, Privacy Concern and Job Reward on Disclosing Personal Security Information Online

Individuals’ reluctance to provide sensitive personal information online could affect the US Governments’ ability to hire and retain qualified personnel for sensitive cleared positions. The aim of this research study was to show how perceived big data security, trust, perceived leadership competency, information sensitivity, privacy concern and reward of a job play a significant role in limiting an individuals’ willingness of disclosing sensitive personal information online. While a significant volume of research has examined information disclosure in the health care field, there has not been any published studies on the willingness of online disclosure in order to attain a US Government job. Therefore, this study was undertaken to address this gap, where the principles of Utility Theory were applied, which posits that people make choices by maximizing their utility function over multiple choices. This study was a quantitative study that collected data through online survey using a 7-Point Likert Scale. Random sampling was used to collect data by sending the survey link through email and through Survey Monkey’s participant outreach program to random participants. Partial Least Square Structural Equation Modeling (PLS-SEM) was used to analyze the data collected from a total of 206 responses received. Based on the results, it was found that leadership competency, trust in website and job reward have a significant impact on an individual’s willingness to disclose, while perceived big data security and privacy concern did not. It is recommended that the government thoroughly vet leaders in charge, as increase in perceived leadership competency has shown to have an increase in website trust, eventually leading to an individual’s willingness to disclose. Of particular interest and contrary to previous studies on information disclosure, privacy concern did not show a significant influence on willingness to disclose information online. Similarly, from the three personality traits of extraversion, intellect and conscientiousness, only individuals with the conscientiousness trait, showed to have any significant impact on privacy concern. Finally, the aim of this study was to help the government understand online disclosure reluctance in order to hire and retain qualified personnel for cleared positions and contribute to the body of knowledge

Building a security framework for smart cities: A case study from UAE

© 2020 IEEE. Smart cities have evolved in the last years, leading the cities to implement initiatives related to technical aspects to improve quality of life. City-rankings have become a central tool for assessing the attractiveness of urban regions. The development of smart cities, however, is not without risk. Cities and citizens are putting more and more responsibilities in urban systems. Hence, all key stakeholders should provide an effective safety and security response to any situation affecting their citizens or organizations. Special attention should be paid with respect to development of services aimed at reducing cybercrime. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. The paper provides an overview of security aspects of a cyber security in smart cities. It starts with exploration of various definitions, threats, and risks in cyber security as well as threats from environments both internal and external and how these threats are currently mitigated with tools, processes and technologies as smart cities utilizes IoT with big data. Presently, most of the data is transmitted and collected online. Hackers usually try to exploit the vulnerabilities using various tools in order to know more about the customers and then misuse the customer\u27s information. Enterprises, on the other hand, continue to collect more information in order to improve services, infrastructure, and security. They collect tons of data from customers in order to complete their requirements for the different services they provide. The main concern with the collected data is that it can be vulnerable to misuse by hackers. Building a security framework based on concepts and repositories of big data and leveraging on the intelligence of predictive analytics can help build a security system that can counter these threats and help to guard from these risk and threats to a large extent. Various concepts, applications, and technologies interact to cover every aspect of the digital citizen\u27s life. Understanding this privacy-challenging environment is the basic requirement for the development of effective protection mechanisms. Thus, the paper aims to address sentiments on cyber security technologies and cybercrime awareness in order to come up with recommendations for innovative solutions. A survey has been conducted and the findings have been analyzed of a case study to come up with recommendations for building a conceptualizing security framework for smart cities. The survey is conducted in the United Arab Emirates (UAE), one of the most advanced countries in the MENA region, who is applying smart city concepts

From Dataveillance to Data Economy: Firm View on Data Protection

The increasing availability of electronic records and the expanded reliance on online communications and services have made available a huge amount of data about people’s behaviours, characteristics, and preferences. Advancements in data processing technology, known as big data, offer opportunities to increase organisational efficiency and competitiveness. Analytically sophisticated companies excel in their ability to extract value from the analysis of digital data. However, in order to exploit the potential economic benefits produced by big data and analytics, issues of data privacy and information security need to be addressed. In Europe, organisations processing personal data are being required to implement basic data protection principles, which are considered difficult to implement in big data environments. Little is known in the privacy studies literature about how companies manage the trade-off between data usage and data protection. This study contributes to explore the corporate data privacy environment, by focusing on the interrelationship between the data protection legal regime, the application of big data analytics to achieve corporate objectives, and the creation of an organisational privacy culture. It also draws insights from surveillance studies, particularly the idea of dataveillance, to identify potential limitations of the current legal privacy regime. The findings from the analysis of survey data show that big data and data protection support each other, but also that some frictions can emerge around data collection and data fusion. The demand for the integration of different data sources poses challenges to the implementation of data protection principles. However, this study finds no evidence that data protection laws prevent data gathering. Implications relevant for the debate on the reform of European data protection law are also drawn from these findings

The Influence of Cognitive Factors and Personality Traits on Mobile Device User\u27s Information Security Behavior

As individuals have become more dependent on mobile devices to communicate, to seek information, and to conduct business, their susceptibility to various threats to information security has also increased. Research has consistently shown that a user’s intention is a significant antecedent of information security behavior. Although research on user’s intention has expanded in the last few years, not enough is known about how cognitive factors and personality traits impact the adoption and use of mobile device security technologies. The purpose of this research was to empirically investigate the influence of cognitive factors and personality traits on mobile device user’s intention in regard to mobile device security technologies. A conceptual model was developed by combining constructs from both the Protection Motivation Theory (PMT) and the Big Five Factor Personality Traits. The data was collected using a web-based survey according to specific inclusion and exclusion criteria. Respondents were limited to adults 18 years or older who have been using their mobile devices to access the internet for at least one year. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to analyze the data gathered from a total of 356 responses received. The findings of this study show that perceived threat severity, perceived threat susceptibility, perceived response costs, response efficacy, and mobile self-efficacy have a significant positive effect on user’s intention. In particular, mobile self-efficacy had the strongest effect on the intention to use mobile device security technologies. Most of the personality traits factors were not found significant, except for conscientiousness. The user’s intention to use mobile device security technologies was found to have a significant effect on the actual usage of mobile device security technologies. Hence, the results support the suitability of the PMT and personality factors in the mobile device security technologies context. This study has contributed to information security research by providing empirical results on factors that influence the use of mobile device security technologies

A Study of Big Data for Business Growth in SMEs: Opportunities & Challenges

In today's world the data is considered as an extremely valued asset and its volume is increasing exponentially every day. This voluminous data is also known as Big Data. The Big Data can be described by 3Vs: the extreme Volume of data, the wide Variety of data types, and the Velocity required processing the data. Business companies across the globe, from multinationals to small and medium enterprises (SMEs), are discovering avenues to use this data for their business growth. In order to bring significant change in businesses growth the use of Big Data is foremost important. Nowadays, mostly business organization, small or big, wishes valuable and accurate information in decision-making process. Big data can help SMEs to anticipate their target audience and customer preferences and needs. Simply, there is a dire necessity for SMEs to seriously consider big data adoption. This study focusses on SMEs due to the fact that SMEs are backbone of any economy and have ability and flexibility for quicker adaptation to changes towards productivity. The big data holds different contentious issues such as; suitable computing infrastructure for storage, processing and producing functional information from it, and security and privacy issues. The objective of this study is to survey the main potentials & threats to Big Data and propose the best practices of Big Data usage in SMEs to improve their business process

A Study of Big Data for Business Growth in SMEs: Opportunities & Challenges

In today's world the data is considered as an extremely valued asset and its volume is increasing exponentially every day. This voluminous data is also known as Big Data. The Big Data can be described by 3Vs: the extreme Volume of data, the wide Variety of data types, and the Velocity required processing the data. Business companies across the globe, from multinationals to small and medium enterprises (SMEs), are discovering avenues to use this data for their business growth. In order to bring significant change in businesses growth the use of Big Data is foremost important. Nowadays, mostly business organization, small or big, wishes valuable and accurate information in decision-making process. Big data can help SMEs to anticipate their target audience and customer preferences and needs. Simply, there is a dire necessity for SMEs to seriously consider big data adoption. This study focusses on SMEs due to the fact that SMEs are backbone of any economy and have ability and flexibility for quicker adaptation to changes towards productivity. The big data holds different contentious issues such as; suitable computing infrastructure for storage, processing and producing functional information from it, and security and privacy issues. The objective of this study is to survey the main potentials & threats to Big Data and propose the best practices of Big Data usage in SMEs to improve their business process

A Survey on Integrated Circuit Trojans

Traditionally, computer security has been associated with the software security, or the information-data security. Surprisingly, the hardware on which the software executes or the information stored-processed-transmitted has been assumed to be a trusted base of security. The main building blocks of any electronic device are Integrated circuits (ICs) which form the fabric of a computer system. Lately, the use of ICs has expanded from handheld calculators and personal computers (PCs) to smartphones, servers, and Internet-of-Things (IoT) devices. However, this significant growth in the IC market created intense competition among IC vendors, leading to new trends in IC manufacturing. System-on-chip (SoC) design based on intellectual property (IP), a globally spread supply chain of production and distribution of ICs are the foremost of these trends. The emerging trends have resulted in many security and trust weaknesses and vulnerabilities, in computer systems. This includes Hardware Trojans attacks, side-channel attacks, Reverse-engineering, IP piracy, IC counterfeiting, micro probing, physical tampering, and acquisition of private or valuable assets by debugging and testing. IC security and trust vulnerabilities may cause loss of private information, modified/altered functions, which may cause a great economical hazard and big damage to society. Thus, it is crucial to examine the security and trust threats existing in the IC lifecycle and build defense mechanisms against IC Trojan threats. In this article, we examine the IC supply chain and define the possible IC Trojan threats for the parties involved. Then we survey the latest progress of research in the area of countermeasures against the IC Trojan attacks and discuss the challenges and expectations in this area. Keywords: IC supply chain, IC security, IP privacy, hardware trojans, IC trojans DOI: 10.7176/CEIS/12-2-01 Publication date: April 30th 202