Advance Authentication Technique: 3D Password

Providing more security to any system requires providing any authentication strategy to that system. There are many authentication strategies, such as textual password, graphical password, etc. But these techniques have some limitation and drawback like they can easily hacked or cracked by using various tools. One of the tools is brute-force algorithm. So, to overcome the drawbacks of existing authentication technique, a new improved authentication strategy is proposed. This strategy is multi-password and multi-factor authentication system as it combines a various authentication techniques such as textual password, graphical password etc. Most important part of 3d password scheme is inclusion of 3d virtual environment. This authentication Strategy is more advanced than any other schemes as we can combine existing schemes. Also this Strategy is tough to break & easy to use. This paper introduced contribution towards 3D Password to make it more secure & more user-friendly to users of all categories

Genetic Algorithm based Application Research in Computer Network Security Evaluation

Network security is a complex systematic job. Current safety methods available are having some shortcomings like feasibility , man made interference, smaller application scope and so on. The imitation results shows that using the established evaluation model for network security assessment is simple, also eliminate the interference of human factors and can quickly find the correct results of the assessment. This article provides new ideas and methods to work for a wide-ranging evaluation of computer network security condition, specially with a certain reference value to predict and control of network security issues in the futur

A probabilistic framework for source localization in anisotropic composite using transfer learning based multi-fidelity physics informed neural network (mfPINN)

The practical application of data-driven frameworks like deep neural network in acoustic emission (AE) source localization is impeded due to the collection of significant clean data from the field. The utility of the such framework is governed by data collected from the site and/or laboratory experiment. The noise, experimental cost and time consuming in the collection of data further worsen the scenario. To address the issue, this work proposes to use a novel multi-fidelity physics-informed neural network (mfPINN). The proposed framework is best suited for the problems like AE source detection, where the governing physics is known in an approximate sense (low-fidelity model), and one has access to only sparse data measured from the experiment (highfidelity data). This work further extends the governing equation of AE source detection to the probabilistic framework to account for the uncertainty that lies in the sensor measurement. The mfPINN fuses the data-driven and physics-informed deep learning architectures using transfer learning. The results obtained from the data-driven artificial neural network (ANN) and physicsinformed neural network (PINN) are also presented to illustrate the requirement of a multifidelity framework using transfer learning. In the presence of measurement uncertainties, the proposed method is verified with an experimental procedure that contains the carbon-fiberreinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers. The results conclude that the proposed technique based on a probabilistic framework can provide a reliable estimation of AE source location with confidence intervals by taking measurement uncertainties into account

Multiresolution Dynamic Mode Decomposition (mrDMD) of Elastic Waves for Damage Localisation in Piezoelectric Ceramic

The performance of piezoelectric sensors deteriorated due to the presence of defect, delamination, and corrosion that needed to be diagnosed for the effective implementation of the structural health monitoring (SHM) framework. A novel experimental approach based on Coulomb coupling is devised to visualise the interaction of ultrasonic waves with microscale defects in the Lead Zirconate Titanate (PZT). Multiresolution dynamic mode decomposition (mrDMD) technique in conjunction with image registration, and Kullback Leibler (KL) divergence is utilised to diagnose and localise the surface defect in the PZT. The mrDMD technique extracts the spatiotemporal coherent mode and provides an equation-free architecture to reconstruct underlying system dynamics. Additionally, due to the strong connection between mrDMD and Koopman operator theory, the proposed technique is well suited to resolve the nonlinear and dispersive interaction of elastic waves with boundaries and defects. The mrDMD sequentially decomposes the three-dimensional spatiotemporal data into low and high frequency modes. The spectral modes are sensitive to defects based on the scaling of wavelength with the size of the defect. The error due to offset and distortion was minimised with ad hoc image registration technique. Further, localisation and quantification of defect are performed by evaluating the distance metric of the probability distribution of coherent data of mrDMD acquired from healthy and defected samples. In the arena of big-data that is ubiquitous in SHM, the paper demonstrates an efficient damage localisation algorithm that explores the nonlinear system dynamics using spectral multi-mode resolution techniques by sensitising the damage features

A deep learning approach for anomaly identification in PZT sensors using point contact method

The implementation of piezoelectric sensors is degraded due to surface defects, delamination, and extreme weathering conditions, to mention a few. Hence, the sensor needs to be diagnosed before the efficacious implementation in the structural health monitoring (SHM) framework. To rescue the problem, a novel experimental method based on Coulomb coupling is utilised to visualise the evolution of elastic waves and interaction with the surface anomaly in the lead zirconate titanate (PZT) substrate. Recently, machine learning (ML) has been expeditiously becoming an essential technology for scientific computing, with several possibilities to advance the field of SHM. This study employs a deep learning-based autoencoder neural network in conjunction with image registration and peak signal-to-noise ratio (PSNR) to diagnose the surface anomaly in the PZT substrate. The autoencoder extracts the significant damage-sensitive features from the complex waveform big data. Further, it provides a nonlinear input–output model that is well suited for the non-linear interaction of the wave with the surface anomaly and boundary of the substrate. The measured time-series waveform data is provided as input into the autoencoder network. The mean absolute error (MAE) between the input and output of the deep learning model is evaluated to detect the anomaly. The MAEs are sensitive to the anomaly that lies in the PZT substrate. Further, the challenge arising from offset and distortion is addressed with ad hoc image registration technique. Finally, the localisation and quantification of the anomaly are performed by computing PSNR values. This work proposes an advanced, efficient damage detection algorithm in the scenario of big data that is ubiquitous in SHM

Review of an Enhanced Authentication Strategy for Multiservice Authorization over Mobile Cloud

Cloud computing has changed the corporate as well as educational industry since it was introduced. The cloud computing is basically cost effective, convenient and on demand service offered to the end users. For instance it lead to cost reserve funds as well as better resource usage and removing the need of specialized technical skill for the users. There exists a huge security concern when utilizing cloud services. The security is extremely vital in cloud computing since individuals and organizations store private information in the cloud and it should likewise be not difficult to utilize the services provided to users. Since the control of services and information required for the regular run of a organization is handled by third party service providers, End user needs to believe the third party cloud service providers and trust that they handle their information in a right way and resources are available as and when needed. There are many approaches proposed for authentication in cloud services. They are intricate, insecure or highly exclusive. In this Paper we have carried of the comparative study of different authentication schemes in cloud computing finally summarize on the basis of different evaluation criteria

An Enhanced Authentication Strategy for Multiservice Authorization over Mobile Cloud

Over the past decade the enterprise computing has been shifted to new paradigm called as Cloud computing. The cloud-computing paradigm provides several service models fitting the needs of an individual or organization. The ease of deployment, reduced costs, availability, scalability, accessibility, flexibility and location independence are some of the very strengths of this paradigm, giving rise to its popularity. Another emerging trend in enterprise computing is the use of smart phone devices, the Smart phone devices has been advanced greatly, in recent years, so has malicious code. Although, smart phones are advancing in terms of computational power, rapidly replacing Personal Computers (PCs) as first choice of a computing device but there is a major problem of resource poverty. For overcoming that issues now most of the organizations started to providing cloud services to their users. This flexibility and accessibility increases the popularity of Mobile cloud computing. On the contrary, security and privacy issues are limiting its wide spread deployment. This paper introduced a new Authentication Strategy for a mobile cloud, Which will provide high security while accessing the number of services provided by the cloud. The propose system overcomes the issue of stealing the authorization tokens through malicious insiders by using a multi tokens as well provides a security against Eaves Dropping and Man In Middle attack. DOI: 10.17762/ijritcc2321-8169.150316

Investigation of Combustion Characteristics of a Cottonseed Biodiesel Fuelled Diesel Engine

AbstractDiesel engines are very well known for their high torque and high thermal efficiency. But, the increase in demand of energy, rapid depletion of fossil fuels and meeting the stringent emission regulations, the researchers take interest to derive the alternative fuels from renewable resources. Among from all the different alternative fuels, vegetable oil has potential to substitute the traditional diesel fuels. The well-established transesterification process is generally used for the production of biodiesel from vegetable oil. In transesterification process the tri-glycerides are converted into mono glycerides with the help of alcohol and catalyst under certain temperature. In the present research work cottonseed vegetable oil is used to produce biodiesel by transesterification process using methanol and KOH as catalyst. As diesel engine combustion is heterogeneous spray combustion, it is very complex phenomenon. The physico-chemical properties of biodiesel are slightly different from diesel fuel. It is very interesting to study the combustion characteristics of a cottonseed based biodiesel fuelled diesel engine. This was the main motivation to take up this study to understand and analyze the combustion characteristics of a cottonseed biodiesel fuelled diesel engine. The experiment was carried out on a single cylinder diesel engine for base diesel and cottonseed biodiesel blends (B5, B10, B15, and B20) as a fuel. The combustion characteristics such as ignition delay, start of combustion, premixed, diffusion and after burning combustion phases, end of combustion and combustion duration were analyzed and compared with base diesel. It was observed that ignition delay and maximum rate of pressure rise decreased with biodiesel as compared to base diesel due to higher cetane number of biodiesel blends which confirms the smooth running of the engine. Ignition delay decreased from 11°CA with base diesel to 6.5°CA with B20 biodiesel. The start of combustion was advanced with all biodiesel blends due to higher bulk modulus results in automatic advance in dynamic injection timing and lesser ignition delay. The combustion duration was longer with all biodiesel blends as compared to base diesel due to longer injection duration results in poor performance of the engine with biodiesel blends

Anonymous Key Generation Technique with Contributory Broadcast Encryption

Encryption is used in a communication system to secure information in the transmitted messages from anyone other than the well-intended receiver. To perform the encryption and decryption the transmitter and receiver should have matching encryption and decryption keys. For sending safeguard information to group needed broadcast encryption (BE). BE allows a sender to securely broadcast to any subset of members and require a trusted party to distribute decryption keys. Group key agreement (GKA) protocol allows a number of users to establish a common secret channel via open networks. Observing that a major goal of GKA for most applications is to create a confidential channel among group members, but a sender cannot omit any particular member from decrypting the cipher texts. By bridging BE and GKA notion with a hybrid primitive referred to as contributory broadcast encryption (CBE). With these primitives, a group of members move through a common public encryption key while each member having there decryption key. A sender seeing the public group encryption key can limit the decryption to subset of members of sender’s choice. A simple way to generate these keys is to use the public key distribution system invented by Diffie and Hellman. That system, however, pass only one pair of communication stations to share a particular pair of encryption and decryption keys. Key distribution sets are used to generate keys and Elliptic Curve Cryptography (ECC) is used for the encryption and decryption of documents; and this going to provide the security for the documents over group communication

Hand Gesture Recognization Using Virtual Canvas

Computer vision based hand tracking can be used to interact with computers in a new innovative way. The input components of a normal computer system include keyboard, mouse, joystick are avoided. Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, fingers, arms, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Existing challenges and future research possibilities are also highlighted. Gestures are expressive, meaningful body motions involving physical movements of the fingers, hands, arms, head, face, or body with the intent of conveying meaningful information orinteracting with the environment. A gesture may also be perceived by the environment as a compression technique for the information to be transmitted elsewhere and subsequently reconstructed by the receive