Evaluation of Cloud-Based Cyber Security System

Cloud-based cyber security systems leverage the power of cloud computing to protect digital assets from cyber threats. By utilizing remote servers and advanced algorithms, these systems provide real-time monitoring, threat detection, and incident response. They offer scalable solutions, enabling businesses to adapt to evolving threats and handle increasing data volumes. Cloud-based security systems provide benefits such as reduced infrastructure costs, continuous updates and patches, centralized management, and global threat intelligence. They protect against various attacks, including malware, phishing, DDoS, and unauthorized access. With their flexibility, reliability, and ease of deployment, cloud-based cyber security systems are becoming essential for organizations seeking robust protection in today's interconnected digital landscape. The research significance of cloud-based cyber security systems lies in their ability to address the growing complexity and scale of cyber threats in today's digital landscape. By leveraging cloud computing, these systems offer several key advantages for researchers and organizations: Scalability: Cloud-based systems can scale resources on-demand, allowing researchers to handle large volumes of data and analyze complex threat patterns effectively. Cost-efficiency: The cloud eliminates the need for extensive on-premises infrastructure, reducing costs associated with hardware, maintenance, and upgrades. Researchers can allocate resources based on their needs, optimizing cost-effectiveness. Real-time monitoring and threat detection: Cloud-based systems provide real-time monitoring of network traffic, enabling quick identification of suspicious activities and potential threats. Researchers can leverage advanced analytics and machine learning algorithms to enhance threat detection capabilities. Collaboration and knowledge sharing: Cloud platforms facilitate collaboration among researchers and organizations by enabling the sharing of threat intelligence, best practices, and research findings. Compliance and regulatory requirements: Cloud platforms often offer built-in compliance features and tools to meet regulatory requirements, assisting researchers in adhering to data protection and privacy standards. Overall, the research significance of cloud-based cyber security systems lies in their ability to provide scalable, cost-effective, and advanced security capabilities, empowering researchers to mitigate evolving cyber threats and protect sensitive data and systems effectively. We will be using Weighted Product Methodology (WPM) which is a decision-making technique that assigns weights to various criteria and ranks alternatives based on their weighted scores. It involves multiplying the ratings of each criterion by their corresponding weights and summing them up to determine the overall score. This method helps prioritize options and make informed decisions in complex situations. Taken of Operational, Technological, Organizational Recorded Electronic Delivery, Recorded Electronic Deliver, Blockchain technology, Database security, Software updates, Antivirus and antimalware The Organizational cyber security measures comes in last place, while Technological cyber security measures is ranked top and Operational measures comes in between the above two in second place. In conclusion, a cloud-based cyber security system revolutionizes the way organizations safeguard their digital assets. By utilizing remote servers, advanced algorithms, and real-time monitoring, it offers scalable and robust protection against evolving threats. With features like threat detection, data encryption, and centralized management, it ensures enhanced security, agility, and efficiency. Embracing a cloud-based approach empowers organizations to stay ahead in the ever-changing landscape of cyber security, effectively safeguarding their critical data and infrastructure

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

Information technology and urban green analysis

It is well recognized that green area plays a pivotal role in improving urban environment, such as preserving water and soil, controlling temperature and humidity of air, preventing pollution, flood prevention, functioning as buffers between incompatible land uses, preserving natural habitat, and providing space for recreation and relaxation. However, due to pressures from new development both in urban fringes and urban centres, urban green and open spaces are seen to be rapidly declining in term of allocated spaces and quality. Without careful urban land use planning, many open spaces will be filled with residential and commercial buildings. Therefore, there is a need for proper planning control to ensure that the provisions of green spaces are adequately being conserved for current and future generations. The need for an urban green information system is particularly important for strategic planning at macro level and local planning at the micro level. The advent of information technology has created an opportunity for the development of new approaches in preserving and monitoring the development of urban green and open spaces. This paper will discuss the use of Geographical Information Systems (GIS) incorporated with other data sources such as remote sensing images and aerial photographs in providing innovative and alternative solutions in the management and monitoring of urban green. GIS is widely accepted in urban landscape planning as it can provide better understanding on the spatial pattern and changes of land use in an area. This paper will primarily focus on digital database that are developed to assist in monitoring urban green and open spaces at regional and local context. The application of GIS in the Klang Valley region or better known as AGISwlk developed since mid-1990's is currently being used by various organisations in the region. The focus of AGISwlk is not merely in providing relevant database to its stakeholders but more importantly, assist in making specific and relevant decisions with regard to spatial planning. It is also used to monitor the loss of green areas by using several temporal data sets. The method of classifying green and open spaces in the region is also being discussed. This paper demonstrates that GIS can be an effective tool in preserving and monitoring green and open spaces in an urban area. The contribution of urban green digital database in someway may leads toward landscape sustainability as to satisfy the ever changing society

Recombination in the Open-Ended Value Landscape of Digital Innovation

Digital innovation introduces a new open-ended value landscape to anyone seeking to generate or capture new value. To understand this landscape, we distinguish between design recombination and use recombination, explore how they play out together, and redirect the attention from products and services toward digital resources. Digital resources serve as building-blocks in digital innovation, and they hold the potential to simultaneously be part of multiple value paths, offered through design recombination and assembled through use recombination. Building on this perspective, we offer the value spaces framework as a tool for better understanding value creation and capture in digital innovation. We illustrate the framework and offer the early contours of a research agenda for information systems researchers

A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Stan i perspektywy kształcenia w zakresie GIS i geoinformacji w Polsce na uniwersyteckich kierunkach geograficznych

Norway Grants FSS/2014/HEI/W/0114/U/001

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea