Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology

About: Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology is a education organization based out in Karachi, Pakistan. It is known for research contribution in the topics: Stock exchange & Population. The organization has 737 authors who have published 882 publications receiving 7653 citations.

Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges

Abstract: Industrial systems always prefer to reduce their operational expenses. To support such reductions, they need solutions that are capable of providing stability, fault tolerance, and flexibility. One such solution for industrial systems is cyber physical system (CPS) integration with the Internet of Things (IoT) utilizing cloud computing services. These CPSs can be considered as smart industrial systems, with their most prevalent applications in smart transportation, smart grids, smart medical and eHealthcare systems, and many more. These industrial CPSs mostly utilize supervisory control and data acquisition (SCADA) systems to control and monitor their critical infrastructure (CI). For example, WebSCADA is an application used for smart medical technologies, making improved patient monitoring and more timely decisions possible. The focus of the study presented in this paper is to highlight the security challenges that the industrial SCADA systems face in an IoT-cloud environment. Classical SCADA systems are already lacking in proper security measures; however, with the integration of complex new architectures for the future Internet based on the concepts of IoT, cloud computing, mobile wireless sensor networks, and so on, there are large issues at stakes in the security and deployment of these classical systems. Therefore, the integration of these future Internet concepts needs more research effort. This paper, along with highlighting the security challenges of these CI’s, also provides the existing best practices and recommendations for improving and maintaining security. Finally, this paper briefly describes future research directions to secure these critical CPSs and help the research community in identifying the research gaps in this regard.

Security threats in cloud computing

Abstract: Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. General example of cloud services is Google apps, provided by Google and Microsoft SharePoint. The rapid growth in field of “cloud computing” also increases severe security concerns. Security has remained a constant issue for Open Systems and internet, when we are talking about security cloud really suffers. Lack of security is the only hurdle in wide adoption of cloud computing. Cloud computing is surrounded by many security issues like securing data, and examining the utilization of cloud by the cloud computing vendors. The wide acceptance www has raised security risks along with the uncountable benefits, so is the case with cloud computing. The boom in cloud computing has brought lots of security challenges for the consumers and service providers. How the end users of cloud computing know that their information is not having any availability and security issues? Every one poses, Is their information secure? This study aims to identify the most vulnerable security threats in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing. Our work will enable researchers and security professionals to know about users and vendors concerns and critical analysis about the different security models and tools proposed.

Exploring the impact of COVID-19 on tourism: transformational potential and implications for a sustainable recovery of the travel and leisure industry

Abstract: The study stipulates phases to observe the proposed mechanism in formulating the travel and leisure industry's recovery strategies. The present pandemic COVID-19 has resulted in global challenges, economic and healthcare crises, and posed spillover impacts on the global industries, including tourism and travel that the major contributor to the service industry worldwide. The tourism and leisure industry has faced the COVID-19 tourism impacts hardest-hit and lies among the most damaged global industries. The leisure and internal tourism indicated a steep decline amounting to 2.86 trillion US dollars, which quantified more than 50% revenue losses. In the first step, the study explores the consequences and settings of the COVID-19 pandemic and how innovation and change can contribute to the tourism industry's revival to the next normal. Thus, the study determines that tourism enterprises and scholars must consider and change the basic principles, main assumptions, and organizational situations related to research and practice framework through rebuilding and establishing the tourism sector. In the second step, the study discusses direct COVID-19 tourism impacts, attitudes, and practices in gaining the leisure industry's boom and recovery. In the third phase, the study proposes to observe the characteristics and COVID-19 tourism consequences on the travel and tourism research. The findings provide insights in regaining the tourism industry's operational activities and offer helpful suggestions to government officials, scholars, and tourism firms to reinvest in the tourism industry to set it back to a normal position.

DBSCAN: Past, present and future

Abstract: Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.

Liquidity risk and performance of banking system

Abstract: Purpose – The purpose of this paper is to examine liquidity risk in Pakistani banks and evaluate the effect on banks' profitability.Design/methodology/approach – Data are retrieved from the balance sheets, income statements and notes of 22 Pakistani banks during 2004‐2009. Multiple regressions are applied to assess the impact of liquidity risk on banks' profitability.Findings – The results of multiple regressions show that liquidity risk affects bank profitability significantly, with liquidity gap and non‐performing as the two factors exacerbating the liquidity risk. They have a negative relationship with profitability.Research limitations/implications – The period studied in this paper is 2004‐2009, due to availability of the data. However, the sample period does not impair the findings since the sample includes 22 banks, which constitute the main part of the Pakistani banking system. Moreover, only profitability is used as the measure of performance. Economic factors contributing to liquidity risk are n...