Emerging technologies and design aspects of next generation cyber physical system with a smart city application perspective

Cyber threat is a major issue that has been terrorizing the computing work. A typical cyber-physical system is crucial in ensuring a safe and secure architecture of a sustainable computing ecosystem. Cyber Threat Intelligence (CTI) is a new methodology that is used to address some of the existing cyber threats and ensure a more secure environment for communication. Data credibility and reliability plays a vital role in increasing the potential of a typical CTI and the data collected for this purpose is said to be highly reliable. In this paper, we have introduced a CTI system using blockchain to tackle the issues of sustainability, scalability, privacy and reliability. This novel approach is capable of measuring organizations contributions, reducing network load, creating a reliable dataset and collecting CTI data with multiple feeds. We have testing various parameters to determine the efficiency of the proposed methodology. Experimental results show that when compared to other methodologies, we can save upto 20% of storage space using the proposed methodology.

https://irojournals.com/irosws/V2/I4/02.pdf

Data Elimination on Repetition using a Blockchain based Cyber Threat Intelligence

In this paper, we examine the microgrids and the long-term dynamic capacity expansion planning in their architecture. Many resources contribute towards the supply to microgrid such as energy, micro gas turbine, solar and wind storage system. Moreover the electric vehicle charging stations use these microgrids as a source of electricity. The electric vehicles that are used in charging stations are based on vehicle-to-grid wherein it is possible to regulate the charging rate and time and to transmit energy to the microgrid. Hence, these charging stations are found to be present in generating unit or flexible load. In the microgrid, the capacity expansion planning is initiated to expand the capacity of battery, wind turbine, solar and micro turbine energy storage system. We have elaborated a 6–year planning horizon, targeting a long term plan through capacity expansion. On the other hand, we have also conducted a short term plan simultaneously to improve the hourly operation of electric vehicle charging station, energy and micro turbine storage system. An expansion of about 200% on wind system is used such that expansion cost is about 53% and incorporation of further resources will increase it by 58% in terms of cost.

https://irojournals.com/iroeea/V3/I1/06.pdf

Energy Storage Capacity Expansion of Microgrids for a Long-Term

The accessibility of the internet and mobile platforms has risen dramatically due to digital technology innovations. Web applications have opened up a variety of market possibilities by supplying consumers with a wide variety of digital technologies that benefit from high accessibility and functionality. Around the same time, web application protection continues to be an important challenge on the internet, and security must be taken seriously in order to secure confidential data. The threat is caused by inadequate validation of user input information, software developed without strict adherence to safety standards, vulnerability of reusable software libraries, software weakness, and so on. Through abusing a website's vulnerability, introduers are manipulating the user's information in order to exploit it for their own benefit. Then introduers inject their own malicious code, stealing passwords, manipulating user activities, and infringing on customers' privacy. As a result, information is leaked, applications malfunction, confidential data is accessed, etc. To mitigate the aforementioned issues, stacking ensemble based classifier model for Cross-site scripting (XSS) attack detection is proposed. Furthermore, the stacking ensembles technique is used in combination with different machine learning classification algorithms like k-Means, Random Forest and Decision Tree as base-learners to reliably detect XSS attack. Logistic Regression is used as meta-learner to predict the attack with greater accuracy. The classification algorithms in stacking model explore the problem in their own way and its results are given as input to the meta-learner to make final prediction, thus improving the overall detection accuracy of XSS attack in stacking than the individual models. The simulation findings demonstrate that the proposed model detects XSS attack successfully.

Stacking Ensemble-based XSS Attack Detection Strategy Using Classification Algorithms

https://irojournals.com/irosws/V2/I3/02.pdf

A Novel Encryption and Decryption of Data using Mobile Cloud Computing Platform

Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.

https://www.irojournals.com/tcsst/V2/I3/03.pdf

Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme

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Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme