Tool support for the evaluation of anomaly traffic classification for network resilience

Resilience is the ability of the network to maintain an acceptable level of operation in the face of anomalies, such as malicious attacks, operational overload or misconfigurations. Techniques for anomaly traffic classification are often used to characterize suspicious network traffic, thus supporting anomaly detection schemes in network resilience strategies. In this paper, we extend the PReSET toolset to allow the investigation, comparison and analysis of algorithms for anomaly traffic classification based on machine learning. PReSET was designed to allow the simulation-based evaluation of resilience strategies, thus enabling the comparison of optimal configurations and policies for combating different types of attacks (e.g., DDoS attacks, worms) and other anomalies. In such resilience strategies, policies written in the Ponder2 language can be used to activate/reconfigure traffic classification modules and other mechanisms (e.g., traffic shaping), depending on monitored results in the simulation environment. Our results show that PReSET can be a valuable tool for network operators to evaluate anomaly traffic classification techniques in terms of standard performance metrics

Comparison of neural and control theoretic techniques for nonlinear dynamic systems

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution May 1994This thesis compares classical nonlinear control theoretic techniques with recently developed neural network control methods based on the simulation and experimental results on a simple electromechanical system. The system has a configuration-dependent inertia, which contributes a substantial nonlinearity. The controllers being studied include PID, sliding control, adaptive sliding control, and two different controllers based on neural networks: one uses feedback error learning approach while the other uses a Gaussian network control method. The Gaussian network controller is tested only in simulation due to lack of time. These controllers are evaluated based on the amount of a priori knowledge required, tracking performance, stability guarantees, and computational requirements. Suggestions for choosing appropriate control techniques to one's specific control applications are provided based on these partial comparison results

Speed estimators using stator resistance adaptation for sensorless induction motor drive

The paper describes speed estimators for a speed sensorless induction motor drive with the direct torque and flux control. However, the accuracy of the direct torque control depends on the correct information of the stator resistance, because its value varies with working conditions of the induction motor. Hence, a stator resistance adaptation is necessary. Two techniques were developed for solving this problem: model reference adaptive system based scheme and artificial neural network based scheme. At first, the sensorless control structures of the induction motor drive were implemented in Matlab-Simulink environment. Then, a comparison is done by evaluating the rotor speed difference. The simulation results confirm that speed estimators and adaptation techniques are simple to simulate and experiment. By comparison of both speed estimators and both adaptation techniques, the current based model reference adaptive system estimator with the artificial neural network based adaptation technique gives higher accuracy of the speed estimation

Modeling of activated sludge process using various nonlinear techniques: a comparison study

This paper presents a comparison study between radial basis function neural network (RBFNN), feed forward multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy (ANFIS) technique to model the activated sludge process (ASP). All of these techniques are based on the nonlinear autoregressive with eXogenous input (NARX) structure. The ASP inputs and outputs data are generated from activated sludge model 1 (ASM1). This work will cover the dissolved oxygen (DO), substrate and biomass modeling. The performances of the model are evaluated based on R2, mean square error (MSE) and root mean square error RMSE. The simulation result shows that ANFIS with NARX structure given a better performance compared with the other modeling techniques

Nonlinear autoregressive moving average-L2 model based adaptive control of nonlinear arm nerve simulator system

This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30th 202