An improved agent-based adaptive protection model for distributed denial of service flooding attack and flash crowd flooding traffic

Recently, a serious disturbance for network security could be a Distributed Denial of Service (DDoS) attack. The advent of technological era has also brought along the threat of DDoS attacks for a variety of services and applications that use the Internet. Firms can incur huge financial losses even if there is a disruption in services for a fraction of period. Similar to a DDoS attack is the Flash Crowd (FC) flooding traffics, in which a particular service is assessed by many legitimate users concurrently, which results in the denial of service. Overloading of network resources is a common issue associated with both of these events, which impact CPU, available bandwidth, and memory for legitimate users, thereby leading to limited accessibility. To address this issue, this thesis proposes an adaptive agent-based protection model known as Adaptive Protection of Flooding Attacks (APFA) specific for DDoS attacks and FC flooding traffics. The APFA model is aimed to protect the Network Application Layer (NAL) against such attacks. The APFA model consists of analysis, detection, decision and filter modules. The main contribution of this work in the APFA model is the decision module that employs a software agent to adapt and recognize the DDoS attacks (Demons and Zombies) and FC flooding traffics. The agent is equipped with three analysis functions that operate on three parameters of normal traffic intensity, traffic attack behavior, and IP address history log. The agent accordingly reacts on each of these attacks with different types of filtering actions as required. APFA model was implemented and tested by applying different attack scenarios using CIDDS standard dataset. The APFA model testing results achieve an accuracy of 99.64%, a precision of 99.62% and sensitivity of 99.96%. The APFA model results outperform similar models of the related work and the adaptive agent is able to distinguish between demons and zombies of the DDoS attacks with high accuracy of 99.91%

Secure and energy-efficient multicast routing in smart grids

A smart grid is a power system that uses information and communication technology to operate, monitor, and control data flows between the power generating source and the end user. It aims at high efficiency, reliability, and sustainability of the electricity supply process that is provided by the utility centre and is distributed from generation stations to clients. To this end, energy-efficient multicast communication is an important requirement to serve a group of residents in a neighbourhood. However, the multicast routing introduces new challenges in terms of secure operation of the smart grid and user privacy. In this paper, after having analysed the security threats for multicast-enabled smart grids, we propose a novel multicast routing protocol that is both sufficiently secure and energy efficient.We also evaluate the performance of the proposed protocol by means of computer simulations, in terms of its energy-efficient operation

The Optimization of Combined Power-Power Generation Cycles

An investigation into the performance of several combined gas-steam power generating plants’ cycles was undertaken at the School of Engineering and Technology at the University of Hertfordshire and it is predominantly analytical in nature. The investigation covered in principle the aspect of the fundamentals and the performance parameters of the following cycles: gas turbine, steam turbine, ammonia-water, partial oxidation and the absorption chiller. Complete thermal analysis of the individual cycles was undertaken initially. Subsequently, these were linked to generate a comprehensive computer model which was employed to predict the performance and characteristics of the optimized combination. The developed model was run using various input parameters to test the performance of the cycle’s combination with respect to the combined cycle’s efficiency, power output, specific fuel consumption and the temperature of the stack gases. In addition, the impact of the optimized cycles on the generation of CO2 and NOX was also investigated. This research goes over the thermal power stations of which most of the world electrical energy is currently generated by. Through which, to meet the increase in the electricity consumption and the environmental pollution associated with its production as well as the limitation of the natural hydrocarbon resources necessitated. By making use of the progressive increase of high temperature gases in recent decades, the advent of high temperature material and the use of large compression ratios and generating electricity from high temperature of gas turbine discharge, which is otherwise lost to the environment, a better electrical power is generated by such plant, which depends on a variety of influencing factors. This thesis deals with an investigation undertaken to optimize the performance of the combined Brayton-Rankine power cycles' performance. This work includes a comprehensive review of the previous work reported in the literature on the combined cycles is presented. An evaluation of the performance of combined cycle power plant and its enhancements is detailed to provide: A full understanding of the operational behaviour of the combined power plants, and demonstration of the relevance between power generations and environmental impact. A basic analytical model was constructed for the combined gas (Brayton) and the steam (Rankine) and used in a parametric study to reveal the optimization parameters, and its results were discussed. The role of the parameters of each cycle on the overall performance of the combined power cycle is revealed by assessing the effect of the operating parameters in each individual cycle on the performance of the CCPP. P impacts on the environment were assessed through changes in the fuel consumption and the temperature of stack gases. A comprehensive and detailed analytical model was created for the operation of hypothetical combined cycle power and power plant. Details of the operation of each component in the cycle was modelled and integrated in the overall all combined cycle/plant operation. The cycle/plant simulation and matching as well as the modelling results and their analysis were presented. Two advanced configurations of gas turbine cycle for the combined cycle power plants are selected, investigated, modelled and optimized as a part of combined cycle power plant. Both configurations work on fuel rich combustion, therefore, the combustor model for rich fuel atmosphere was established. Additionally, models were created for the other components of the turbine which work on the same gases. Another model was created for the components of two configurations of ammonia water mixture (kalina) cycle. As integrated to the combined cycle power plant, the optimization strategy considered for these configurations is for them to be powered by the exhaust gases from either the gas turbine or the gases leaving the Rankine boiler (HRSG). This included ChGT regarding its performance and its environmental characteristics. The previously considered combined configuration is integrated by as single and double effect configurations of an ammonia water absorption cooling system (AWACS) for compressor inlet air cooling. Both were investigated and designed for optimizing the triple combination power cycle described above. During this research, tens of functions were constructed using VBA to look up tables linked to either estimating fluids' thermodynamic properties, or to determine a number of parameters regarding the performance of several components. New and very interesting results were obtained, which show the impact of the input parameters of the individual cycles on the performance parameters of a certain combined plant’s cycle. The optimized parameters are of a great practical influence on the application and running condition of the real combined plants. Such influence manifested itself in higher rate of heat recovery, higher combined plant thermal efficiency from those of the individual plants, less harmful emission, better fuel economy and higher power output. Lastly, it could be claimed that various concluding remarks drawn from the current study could help to improve the understanding of the behaviour of the combined cycle and help power plant designers to reduce the time, effort and cost of prototyping

An SVAR Analysis of Monetary Policy Dynamics and Housing Market Responses in Australia

This paper examines the impact of monetary policy and a range of sector-specific and macroeconomic shocks on the Australian housing market using quarterly data for a period of 1974-2008. The paper develops a structural vector autoregressive (SVAR) model based on contemporaneous restrictions to analyse the dynamics of these shocks. The results indicate that supply of new houses in Australia rises with higher real house prices; and that house prices rise and fall with higher inflation rate and interest rate, respectively. Dynamics of the impulse responses reveal significant effect of monetary policy on new house constructions, real house prices, material costs and inflation. Results also suggest that housing output, real house prices and interest rates respond significantly to shocks to housing supply, housing demand and to a number of other variables. These results are expected to shed some lights on the current policy environment pertaining to the Australian housing sector.Monetary transmission, Housing market, Structural VAR

A Cost-Effective Method for Power Factor ‎Metering Systems ‎

The power factor (PF) is an important measurement in an AC electrical system that indicates how much power ‎is utilized to accomplish productive work by a load and how much power is consumed. As a result, it\u27s one of ‎the primary causes of excessive energy costs and power outages. This paper aims to present a simple, cost-‎effective, and accurate PF metering and monitoring system implemented using an Arduino microcontroller with ‎a novel methodology different from other papers. The proposed method is to design the software code instead ‎of using external components of Zero-Crossing Detectors (Z-CDs) for both voltage (V) and current (I) signals, and ‎instead of using Exclusive-OR (X-OR) gates also. Determining the phase-angle and PF in an efficient manner ‎can be useful in many approaches to electrical systems: 1-for synchronization of parallel connections of ‎alternators; 2-for directional protection systems; 3-for PF correction and load management; and 4-for designing ‎watt/energy meters. Using the Proteus 8 Professional (ISIS program), the proposed designed circuit was ‎simulated for more verification. The simulation and experimental findings are presented to validate the ‎proposed metering system\u27s effectiveness.

Comparison between neural network and P&O method in optimizing MPPT control for photovoltaic cell

The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell

On the identifiability of steady-state induction machine models using external measurements

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose

TRAINING COURSE ON DELIVERY CARE AND NEONATAL CARE IN RURAL HOSPITALS, GEZIRA STATE, SUDAN

This paper describes a training course for medical officers working in the rural hospitals in Gezira State to achieve a general objective; safe management of obstetric emergencies, miscarriage and the newborn . the description of the course includes; an introduction, the specific learning objectives, a detailed plan of work, instructional methods, resources , the course timetable, evaluation methods, description of the logbook and the feedback questionnaire. This article also includes descriptions of our experience in three rounds during which 43 medical officesr were traine