Facilitating dynamic network control with software-defined networking

This dissertation starts by realizing that network management is a very complex and error-prone task. The major causes are identified through interviews and systematic analysis of network config- uration data on two large campus networks. This dissertation finds that network events and dynamic reactions to them should be programmatically encoded in the network control program by opera- tors, and some events should be automatically handled for them if the desired reaction is general. This dissertation presents two new solutions for managing and configuring networks using Software- Defined Networking (SDN) paradigm: Kinetic and Coronet. Kinetic is a programming language and central control platform that allows operators to implement traffic control application that reacts to various kinds of network events in a concise, intuitive way. The event-reaction logic is checked for correction before deployment to prevent misconfigurations. Coronet is a data-plane failure recovery service for arbitrary SDN control applications. Coronet pre-plans primary and backup routing paths for any given topology. Such pre-planning guarantees that Coronet can perform fast recovery when there is failure. Multiple techniques are used to ensure that the solution scales to large networks with more than 100 switches. Performance and usability evaluations show that both solutions are feasible and are great alternative solutions to current mechanisms to reduce misconfigurations.Ph.D

Surface-hopping dynamics and decoherence with quantum equilibrium structure

In open quantum systems decoherence occurs through interaction of a quantum subsystem with its environment. The computation of expectation values requires a knowledge of the quantum dynamics of operators and sampling from initial states of the density matrix describing the subsystem and bath. We consider situations where the quantum evolution can be approximated by quantum-classical Liouville dynamics and examine the circumstances under which the evolution can be reduced to surface-hopping dynamics, where the evolution consists of trajectory segments evolving exclusively on single adiabatic surfaces, with probabilistic hops between these surfaces. The justification for the reduction depends on the validity of a Markovian approximation on a bath averaged memory kernel that accounts for quantum coherence in the system. We show that such a reduction is often possible when initial sampling is from either the quantum or classical bath initial distributions. If the average is taken only over the quantum dispersion that broadens the classical distribution, then such a reduction is not always possible.Comment: 11, pages, 8 figure

Effect of an external field on the reversible reaction of a neutral particle and a charged particle in three dimensions. II. Excited-state reaction

The excited-state reversible reaction of a neutral particle and a charged particle in an external electric field is studied in three dimensions. This work extends the previous investigation for the ground-state reaction [S. Y. Reigh et al., J. Chem. Phys. 129, 234501 (2008)] to the excited-state reaction with two different lifetimes and quenching. The analytic series solutions for all the fundamental probability density functions are obtained with the help of the diagonal approximation. They are found to be in excellent agreement with the exact numerical solutions of anisotropic diffusion-reaction equations. The analytical solutions for reaction rates and survival probabilities are also obtained. We find that the long-time kinetic transition from a power-law decrease to an exponential increase can be controlled by the external field strength or excited-state decay rates or both. (C) 2010 American Institute of Physics. [doi: 10.1063/1.3394894open

Correlation between Deep Neural Network Hidden Layer and Intrusion Detection Performance in IoT Intrusion Detection System

As the Internet of Things (IoT) continues to grow, a vast amount of data is generated. The IoT environment is quite sensitive to security challenges because personal information may be leaked or sensor data may be manipulated, which could cause accidents. Because traditional intrusion detection system (IDS) studies are often designed to work well on datasets, it is unknown whether they would work well in a changing network environment. In addition, IDSs for protecting IoT environments have been studied, but their performance was verified using datasets unrelated to the IoT, so it is not known whether the performance would be effective in an IoT environment. In this study, we propose an intrusion detection hyperparameter control system (ID-HyConSys) that automates the IDS using proximal policy optimization (PPO) to solve these problems and reliably protect the IoT environment. ID-HyConSys consists of an intrusion detection module consisting of a deep neural network (DNN) feature extractor that extracts efficient features from a changing network environment, a k-means cluster that clusters the extracted data, and a PPO agent that automates the IDS through learning and control. Through experimentation, it was confirmed that the hidden layer configuration, the number of feature extractions by the DNN feature extractor, and the number of clusters in the k-means cluster significantly affected the intrusion detection performance. The PPO directly controls these hyperparameters and determines the optimized value itself. The performance of ID-HyConSys was evaluated using the CICIDS2017 and MQTTset datasets. An F1-score of 0.9707 on CICIDS2017 and an F1-score of 0.9973 on the MQTTset were obtained. Finally, we merged the two datasets and obtained an F1-score of 0.9901. The superiority of the ID-HyConSys proposed in this study was confirmed because ID-HyConSys showed high performance on each dataset and, at the same time, very high performance on complex merged datasets. ID-HyConSys is expected to protect the IoT environment more quickly and safely by automatically learning network changes and adjusting the intrusion detection module accordingly

An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization

The complexity of network intrusion detection systems (IDSs) is increasing due to the continuous increases in network traffic, various attacks and the ever-changing network environment. In addition, network traffic is asymmetric with few attack data, but the attack data are so complex that it is difficult to detect one. Many studies on improving intrusion detection performance using feature engineering have been conducted. These studies work well in the dataset environment; however, it is challenging to cope with a changing network environment. This paper proposes an intrusion detection hyperparameter control system (IDHCS) that controls and trains a deep neural network (DNN) feature extractor and k-means clustering module as a reinforcement learning model based on proximal policy optimization (PPO). An IDHCS controls the DNN feature extractor to extract the most valuable features in the network environment, and identifies intrusion through k-means clustering. Through iterative learning using the PPO-based reinforcement learning model, the system is optimized to improve performance automatically according to the network environment, where the IDHCS is used. Experiments were conducted to evaluate the system performance using the CICIDS2017 and UNSW-NB15 datasets. In CICIDS2017, an F1-score of 0.96552 was achieved and UNSW-NB15 achieved an F1-score of 0.94268. An experiment was conducted by merging the two datasets to build a more extensive and complex test environment. By merging datasets, the attack types in the experiment became more diverse and their patterns became more complex. An F1-score of 0.93567 was achieved in the merged dataset, indicating 97% to 99% performance compared with CICIDS2017 and UNSW-NB15. The results reveal that the proposed IDHCS improved the performance of the IDS by automating learning new types of attacks by managing intrusion detection features regardless of the network environment changes through continuous learning