Management of Distributed Denial of Service Attack in Cloud Computing Environment

Cloud Computing is a recent technology, it provides a simple and unambiguous taxonomy of three service models available to cloud consumers: software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). There are several security issues with the delivery model of cloud. Our work is to dealing with management of Distributed Denial of Service attack on SaaS model of cloud computing environment. If DDoS attack is capable enough to violate the Service Level Agreement (SLA) on availability it can cause huge financial claim and it will affect the reputation of industries in a market. So our basic aim is to design a management model that will avoid the SLA violation on availability due to a DDoS attack. Our model works in three stages (1) Detection of DDoS attack (2) Avoidance of DDoS attack and (3) prevention of DDoS attack. Feedforward Neural Network method for detection of DDoS attack. Sigmoid function is used as Neural modal for obtaining the desire output. The Supervised learning model adjusts the connection weight and bias value of ANN model. Using predefined datasets to train the ANN model. For the Avoidance of DDoS attack data center dynamically allocate the resources on virtual machines. A new virtual machine will be clone based on the image file of the original. Replicate the resources on a virtual machine in order to avoid the SLA violation (Availability issues). Message Authentication Code (MAC) is used for prevention of DDoS. The message Authentication code increases the overhead on the network. The design goal is to decrease the overhead of MAC on the network so we are using Router Packet Filtering method that reduces that MAC overhead on packet over the network. This lower overhead increases the speed of authentication and reduces the amount of dynamically allocated resources that will prevent the violation of the SLA on cloud computing

Scada and its Application in Power Generation and Distribution System

The essential common sources like Coal, gas, Diesel, nuclear and so forth. Is single time useable due to which the quantity of these sources is decreasing day by day. The emission of these fuels is also responsible for air pollution. On the other hand, if we can use renewable sources like hydro power, solar power and wind power to generate electricity such that SCADA system can incorporate to have better monitoring and reliability of the system for proper distribution of load optimise. So we have developed a system in which consumers are connected to different types of power plants via a Grid. The network load and plants are checked and controlled by the SCADA framework. This provides the uninterrupted power supply to the distributors with more reliable solution

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed

Damage Propagation Modeling for Aircraft Engine Prognostics

This paper describes how damage propagation can be modeled within the modules of aircraft gas turbine engines. To that end, response surfaces of all sensors are generated via a thermo-dynamical simulation model for the engine as a function of variations of flow and efficiency of the modules of interest. An exponential rate of change for flow and efficiency loss was imposed for each data set, starting at a randomly chosen initial deterioration set point. The rate of change of the flow and efficiency denotes an otherwise unspecified fault with increasingly worsening effect. The rates of change of the faults were constrained to an upper threshold but were otherwise chosen randomly. Damage propagation was allowed to continue until a failure criterion was reached. A health index was defined as the minimum of several superimposed operational margins at any given time instant and the failure criterion is reached when health index reaches zero. Output of the model was the time series (cycles) of sensed measurements typically available from aircraft gas turbine engines. The data generated were used as challenge data for the Prognostics and Health Management (PHM) data competition at PHM 08

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions

Prognostics and Health Management of Batteries and Composites

Prognostics and health management (PHM) algorithms track the health condition of a system and make an assessment of the time until which this system can perform within desired specifications. These algorithms require development of fault growth models and data analysis on measurements available from the system. During the course of this program I will engage in the above activities by means of two research projects. The model development will be done for Lithium Iron Phosphate (LiFePO4) batteries. By understanding the physical and chemical processes within the battery a model for charge capacity degradation will be developed for these batteries that are used in hybrid electric vehicles (HEV), plug-in HEV, laptop, aircraft, etc. This effort will include conducting the experiments in the lab and collecting the data. Contingent of available time data analysis and algorithm development may follow. In a parallel effort, data analysis exposure will be gained by working on fatigue cycling data on carbon-carbon composites. This analysis extracts features of material degradation as it is subjected to fatigue cycling. This experiments will help develop fault propagation models for composite materials that are expected to be used for aerospace structures such as spacecraft and aircraft fuselage. PHM of these systems is a critical step in keeping these systems safe and running efficiently

Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments

Towards Requirements in Systems Engineering for Aerospace IVHM Design

Health management (HM) technologies have been employed for safety critical system for decades, but a coherent systematic process to integrate HM into the system design is not yet clear. Consequently, in most cases, health management resorts to be an after-thought or 'band-aid' solution. Moreover, limited guidance exists for carrying out systems engineering (SE) on the subject of writing requirements for designs with integrated vehicle health management (IVHM). It is well accepted that requirements are key to developing a successful IVHM system right from the concept stage to development, verification, utilization, and support. However, writing requirements for systems with IVHM capability have unique challenges that require the designers to look beyond their own domains and consider the constraints and specifications of other interlinked systems. In this paper we look at various stages in the SE process and identify activities specific to IVHM design and development. More importantly, several relevant questions are posed that system engineers must address at various design and development stages. Addressing these questions should provide some guidance to systems engineers towards writing IVHM related requirements to ensure that appropriate IVHM functions are built into the system design

Evaluating Algorithm Performance Metrics Tailored for Prognostics

Prognostics has taken a center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of the system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Specifically four algorithms namely; Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR) are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in different manner and depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, these metrics offer ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized.