Hybrid approach to ensure data confidentiality and tampered data recovery for RFID tag

Radio Frequency Identification (RFID) is an emerging wireless object identification technology with many potential applications such as supply chain management, personnel tracking and healthcare. However, security vulnerabilities of the RFID system have been a serious concern for its wide adoption in many applications. Although there are lots of work to provide privacy and anonymity, little focus has been given to ensure confidentiality and integrity of RFID tag data. To this end, we propose a lightweight hybrid approach based on stenographic and watermarking to ensure data confidentiality, linkability resistance and integrity on the RFID tags data. The proposed technique is capable of tampered data recovering and restoring for RFID tag. It has been validated and tested on EPC class 1 gen2 tags

Current status of MSSM Higgs sector with LHC 13 TeV data

ATLAS and CMS collaborations have reported the results on the Higgs search analyzing $\sim 36$ fb$^{-1}$ data from Run-II of LHC at 13 TeV. In this work, we study the Higgs sector of the phenomenological Minimal Supersymmetric Standard Model, in light of the recent Higgs data, by studying separately the impact of Run-I and Run-II data. One of the major impacts of the new data on the parameter space comes from the direct searches of neutral CP-even and CP-odd heavy Higgses ($H$ and $A$, respectively) in the $H/A \to \tau^{+} \tau^{-}$ channel which disfavours high $\tan\beta$ regions more efficiently than Run-I data. Secondly, we show that the latest result of the rare radiative decay of $B$ meson imposes a slightly stronger constraint on low $\tan \beta$ and low $M_A$ region of the parameter space, as compared to its previous measurement. Further, we find that in a global fit Run-II light Higgs signal strength data is almost comparable in strength with the corresponding Run-I data. Finally, we discuss scenarios with the Heavy Higgs boson decaying into electroweakinos and third generation squarks and sleptons.Comment: 51 pages, 22 figure

Machine Learning to Ensure Data Integrity in Power System Topological Network Database

Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms

Vulnerability and Impact Analysis of the IEC 61850 Goose Protocol in the Smart Grid

IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders

To Confluence 2018 organizers and delegates [Letter]

Ray, BR ORCiD: 0000-0002-3016-1695Letter of support for Confluence 2018

To Confluence 2018 organizers and delegates [Letter]

Letter of support for Confluence 2018

A New Data Driven Long-Term Solar Yield Analysis Model of Photovoltaic Power Plants

Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs)

Academic debate on using social networking media: Teachers’ and students’ perceptions from two tertiary institutions

Ray, BR ORCiD: 0000-0002-3016-1695A number of existing quantitative and qualitative studies have proven that motivation and collaboration impact positively on students’ engagement and learning outcomes. Online Social Networking (OSN) is a widely used media that not only offers opportunities for collaboration and motivation but also potential for connectivity and peer feedback for e-learning. Many researchers have explored the importance of OSN sites for e-learning. They have proven that the current e-learning pedagogy supports the use of OSN. However, less research has done on teachers’ and students’ perception of using OSN. This investigation sought to discover teachers and students perception of using online social networking sites to motivate learners and foster collaboration. Two tertiary institutes were selected for this research from the Melbourne CBD in Australia. A group of students and teachers were invited to participate in this study. All participants were asked to complete a questionnaire. The collected data from the questionnaire survey were analysed through simple statistical. The findings of this research demonstrate that students were more enthusiastic than teachers to use OSN for e-learning. Most of the students’ belief, OSN should be included institutionally as an e-learning tool to improve their learning experience. On the other hand, teachers were cautious about the inclusion of OSN into practices. Most of the teachers wanted more control on this media before it gets institutionalize for e-learning. Some of them also worried that it might negatively influence on students’ performance. This paper also argues that more research needs to be done rigorously to find teachers’ and students’ attitude of using OSN sites productively in order to enrich and make effective use of e-learning to achieve the most powerful learning outcomes

A lightweight protocol for RFID authentication

Radio frequency identification (RFID) technology has many applications such as supply chain management, asset tracking, healthcare and logistics. Since RFID tags and readers communicate through a wireless medium, they are prone to a wide range of attacks. There are a number of measures to safeguard the security of RFID device operations and communications: mutual authentication, confidentiality, indistinguishability, forward security, and desynchronisation resilience. Due to limited computational power and memory, heavy-weight encryption functions cannot be performed in the RFID tags to execute the security protocols. Therefore, RFID security protocols are restricted to light-weight encryption functions such as simple one-way hash function, cyclic redundancy check (CRC), pseudo-random number generator (PRNG) and exclusive-OR (XOR). This paper develops a lightweight secure authentication protocol to mutually authenticate the RFID tag and the reader through an insecure radio communication channel. The protocol assumes that each RFID tag pre-shares a secret key with the reader. The protocol uses two random values to guarantee the freshness of the messages in order to outwit any replay attack. An analysis of the protocol using Scyther verification tool shows that the protocol ensures secure communication between the reader and the RFID tag provided the communication channel between the back-end server and the reader is protected

Priority based Modeling and Comparative study of Google Cloud Resources between 2011 and 2019

The cloud resource allocation for jobs must be further optimized and prioritised due to ever increasing demand for cloud computing resources to handle big data. In this research, we have examined the relationship between resource allocation, usages, and priority of tasks to reveal the influence of priority in resource allocation and resource usages. The analysis and modeling of this paper have used the Google cloud public dataset of 2011 and 2019. After processing and cleaning of one month data of Google cloud, we have revealed, the tasks are classified in 12 priorities in the 2011 cluster model whereas 500 priorities in the 2019 cluster model. However, both models have grouped these priorities into five groups. Therefore, we have modeled resource allocation versus usages based on five main priority groups using XGBoost (Extreme Gradient Boosting) and correlation coefficient. The comparative study on the developed models shows, the priority grouping of 2019 has better evenly distribution of resources for jobs but less efficient in most of the priority groups for resource allocation. Based on the performance parameters of the developed models, the resource allocation works more efficiently for most of the 2011 priority groups except 'other'. These findings are useful for researchers to develop a balanced priority-based resource allocation-usages model to further optimise resources to reduce the management cost of cloud clusters