Role of Cyber Insurance in India to Protect Cyber Theft: A Socio-Legal Study

Cyber insurance can help protect your business from potential cyber threats. With the right cyber insurance, you can protect yourself and your data from potential damage. Here are some tips to help you choose the right cyber insurance policy- Check the cyber risk associated with your business. Make sure that the cyber risks your business faces are realistic and not exaggerated. Look at the cyber insurance policies available. There are many cyber insurance policies available, so make sure to compare prices and policies to find the best deal for your business. Choose the right cyber insurance policy for your business. Make sure to choose a policy that covers all of your cyber needs, including data loss, cyber theft, and third-party cyber-attacks. Get a policy that covers your business’s entire staff. A policy that covers your entire staff can help protect your data and business from potential cyber threats. Get a policy that has generous coverage. A policy that has generous coverage will protect your business from potential cyber threats, even if they are not specifically covered in the policy. Get a policy that is easy to use. A policy that is easy to use will help you understand the policy and make sure you are getting security

Cybercrime Insurance Is A Protection Tool Of The Society: An Analytical Study

Despite the increasing awareness of cybercrime, there are many people who are still not sure what it is. Cybercrime is a criminal activity that is committed using computers or the internet. It can include anything from hacking and identity theft to fraud and child pornography. With the rise of technology, cybercrime has become one of the most common forms of crime. According to a report by Norton, a cyber-security company, there were 4.1 billion records breached in the first six months of 2019 alone. And the rate of cybercrime is only increasing. The same report stated that the average cost of a data breach globally was $3.86 million in 2018, which is up 6% from the previous year. During the duration of the Pandemic of Covid-19 most, most of the cyber-crimes increased by around five hundred times is stated by Chief of Defence Staff (CDS) General Bipin Rawat in a discussion with the Hindu Newspaper reporter in Nov. 2021. With the growing rate of cybercrime, many businesses are starting to purchase cybercrime insurance. Cybercrime insurance is a protection tool that businesses can use to financially protect themselves in the event of a data breach or other type of cyber-attack. In this research paper, we will explore the need for cybercrime insurance and how it can help businesses recover from a cyber-attack. We will also look at some of the challenges that businesses face when it comes to purchasing such insurance

Parameter Estimation in Electrical Distribution Systems with limited Measurements using Regression Methods

This paper presents novel methods for parameter identification in electrical grids with small numbers of spatially distributed measuring devices, which is an issue for distribution system operators managing aged and not properly mapped underground Low Voltage (LV) grids, especially in Germany. For this purpose, the total impedance of individual branches of the overall system is estimated by measuring currents and voltages at a subset of all system nodes over time. It is shown that, under common assumptions for electrical distsribution systems, an estimate of the total impedance can be made using readily computable proxies. Different regression methods are then used and compared to estimate the total impedance of the respective branches, with varying weights of the input data. The results on realistic LV feeders with different branch lengths and number of unmeasured segments are discussed and multiple influencing factors are investigated through simulations. It is shown that estimates of the total impedances can be obtained with acceptable quality under realistic assumptions

Fault Diagnosis and Prognosis of Critical Components

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Toward Transactive Control of Coupled Electric Power and District Heating Networks

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.Comment: 35 pages, 16 Figure

Toward Transactive Control of Coupled Electric Power and District Heating Networks

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.Comment: 35 pages, 16 Figure

Polymer Gear Fault Classification Using EMD-DWT Analysis Based on Combination of Entropy and Hjorth Features

339-346Polymer gears have proven to be an adequate replacement for traditional metal gears in various applications. They are lighter, have less inertia, and are much quieter than their metal counterparts. Polymer gears, however, are rarely employed because there is a lack of failure data. Hence, there is tremendous scope for fault detection of polymer gears. In this paper, a novel technique of polymer gear fault detection is proposed following the double decomposition of vibration signals. The experimentally acquired vibration signals are processed through two steps of decomposition, i.e., empirical mode decomposition and discrete wavelet transform based Time-Frequency decomposition. Subsequently, entropy features (EF), Hjorth parameter (HP), and a combination of EF and HP are extracted. A combination of these feature sets is used to train the classifier: support vector machine (SVM), ensemble learning, and decision tree. Among all classification methods, the ensemble learning classifier reached the maximum classification accuracy of 99.2 % using a combination of EF and HP features. Furthermore, EMD and DWT are compared with the proposed double decomposition method (EMD-DWT) for accuracy validation. The experiments demonstrated that the proposed EMD-DWT method is efficient and yields promising results for classifying polymer gear faults