IMPROVING NETWORK POLICY ENFORCEMENT USING NATURAL LANGUAGE PROCESSING AND PROGRAMMABLE NETWORKS

Computer networks are becoming more complex and challenging to operate, manage, and protect. As a result, Network policies that define how network operators should manage the network are becoming more complex and nuanced. Unfortunately, network policies are often an undervalued part of network design, leaving network operators to guess at the intent of policies that are written and fill in the gaps where policies don’t exist. Organizations typically designate Policy Committees to write down the network policies in the policy documents using high-level natural languages. The policy documents describe both the acceptable and unacceptable uses of the network. Network operators then take the responsibility of enforcing the policies and verifying whether the enforcement achieves expected requirements. Network operators often encounter gaps and ambiguous statements when translating network policies into specific network configurations. An ill-structured network policy document may prevent network operators from implementing the true intent of the policies, and thus leads to incorrect enforcement. It is thus important to know the quality of the written network policies and to remove any ambiguity that may confuse the people who are responsible for reading and implementing them. Moreover, there is a need not only to prevent policy violations from occurring but also to check for any policy violations that may have occurred (i.e., the prevention mechanisms failed in some way), since unwanted packets or network traffic, were somehow allowed to enter the network. In addition, the emergence of programmable networks provides flexible network control. Enforcing network routing policies in an environment that contains both the traditional networks and programmable networks also becomes a challenge. This dissertation presents a set of methods designed to improve network policy enforcement. We begin by describing the design and implementation of a new Network Policy Analyzer (NPA), which analyzes the written quality of network policies and outputs a quality report that can be given to Policy Committees to improve their policies. Suggestions on how to write good network policies are also provided. We also present Network Policy Conversation Engine (NPCE), a chatbot for network operators to ask questions in natural languages that check whether there is any policy violation in the network. NPCE takes advantage of recent advances in Natural Language Processing (NLP) and modern database solutions to convert natural language questions into the corresponding database queries. Next, we discuss our work towards understanding how Internet ASes connect with each other at third-party locations such as IXPs and their business relationships. Such a graph is needed to write routing policies and to calculate available routes in the future. Lastly, we present how we successfully manage network policies in a hybrid network composed of both SDN and legacy devices, making network services available over the entire network

A Novel Method to Directly Analyze Dissolved Acetic Acid in Transformer Oil without Extraction Using Raman Spectroscopy

Analyzing the concentration of low molecular acids dissolved in oil is vital in the oil-paper insulation aging diagnostic procedure of power transformers. The existing methods cannot distinguish between different acid types and their strengths. In this study, an improved solution Raman detection platform is fabricated. The direct measurement of dissolved acetic acid, a kind of low molecular acids, is observed in transformer oil without extraction. The Raman shift line of oil-dissolved acetic acid at 891 cm−1 corresponding to H–C–H symmetrical swing and O–H swing modes is taken as its characteristic value. Taking Raman shift line of pure oil at 932 cm−1 as an internal standard, a linear regression curve for quantitative analysis is obtained with a slope of 0.19. The best platform parameter of accumulation number is 300, which is determined by Allan deviation analysis. The current concentration detection limit and accuracy for oil-dissolved acetic acid are obtained at about 0.68 mg/mL and 91.66%, separately. The results show that Raman spectroscopy could be a useful alternative method for evaluation insulation aging state of an operating power transformer in the future

Charge Transfer Effect on Raman and Surface Enhanced Raman Spectroscopy of Furfural Molecules

The detection of furfural in transformer oil through surface enhanced Raman spectroscopy (SERS) is one of the most promising online monitoring techniques in the process of transformer aging. In this work, the Raman of individual furfural molecules and SERS of furfural-Mx (M = Ag, Au, Cu) complexes are investigated through density functional theory (DFT). In the Raman spectrum of individual furfural molecules, the vibration mode of each Raman peak is figured out, and the deviation from experimental data is analyzed by surface charge distribution. In the SERS of furfural-Mx complexes, the influence of atom number and species on SERS chemical enhancement factors (EFs) are studied, and are further analyzed by charge transfer effect. Our studies strengthen the understanding of charge transfer effect in the SERS of furfural molecules, which is important in the online monitoring of the transformer aging process through SERS