Vulnerability Analysis, Intrusion Prevention and Detection for Link State Routing Protocols

The objective of this dissertation is to study the vulnerabilities of link state routing protocol, design and implement new approaches for intrusion prevention and detection. As one of the cornerstones of network infrastructure, routing systems are facing more threats than ever: they are vulnerable by nature and challenging to protect. Drawing upon working results from two DARPA research projects, JiNao (Scalable Intrusion Detection for the Emerging Network Infrastructure) and GIANT (Global Intrusion Assessment Through Distributed Decision Making), the dissertation makes the following contributions: First, it systematically analyzes the vulnerabilities of link state routing protocol from design, impl-mentation, environment, and configuration aspects, making comparisons with other distance vector based protocols when necessary and discovering potential attack points. The vulnerability analysis establishes foundations for prevention and intrusion detection. Second, it describes the design and implementation of wrapper-based active protection for routing protocol, which are most suitable to prevent known vulnerabilities and provide architectural advantage to legacy systems. Third, it describes integrated network management (INM) based intrusion detection method. The integration of management and control planes wil

A Portable Smartphone-Based Sensing System Using a 3D-Printed Chip for On-Site Biochemical Assays

Recently, smartphone-based chromogenic sensing with paper-based microfluidic technology has played an increasingly important role in biochemical assays. However, generally there were three defects: (i) the paper-based chips still required complicated fabrication, and the hydrophobic boundaries on the chips were not clear enough; (ii) the chromogenic signals could not be steadily captured; (iii) the smartphone apps were restricted to the detection of specific target analytes and could not be extended for different assays unless reprogrammed. To solve these problems, in this study, a portable smartphone-based sensing system with a 3D-printed chip was developed. A 3D-printed imaging platform was designed to significantly reduce sensing errors generated during signal capture, and a brand-new strategy for signal processing in downloadable apps was established. As a proof-of-concept, the system was applied for detection of organophosphorus pesticides and multi-assay of fruit juice, showing excellent sensing performance. For different target analytes, the most efficient color channel could be selected for signal analysis, and the calibration equation could be directly set in user interface rather than programming environment, thus the developed system could be flexibly extended for other biochemical assays. Consequently, this study provides a novel methodology for smartphone-based biochemical sensing

Dynamics of Counterfactual Retrieval

People often think about counterfactual possibilities to an event and imagine how it could have been otherwise. The study of how this occurs is central to many areas of cognitive science, including decision making, social cognition, and causal judgment; however, modeling the memory processes at play in naturalistic counterfactual retrieval has been difficult. We use established memory models to evaluate and compare multiple mechanisms that could be involved in counterfactual retrieval. Our models are able to capture nuanced dynamics of retrieval (e.g. how retrieved counterfactuals cue subsequent counterfactuals), and can predict the effects of retrieval on evaluations and decisions. In doing so, we show how existing theories of counterfactual thinking can be combined with quantitative models of memory search to provide new insights about the formation and consequences of counterfactual thought

A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainlandKey points

Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods in the training dataset, which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset. Tests using synthetic data demonstrate that the proposed method is much faster, and the results for the vS model are more accurate and robust than those of conventional methods. We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent (ChinaVs-DL1.0), which has smaller dispersion misfits than those from the traditional method. The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data

Single-cell gene regulatory network analysis for mixed cell populations with applications to COVID-19 single cell data

Gene regulatory network (GRN) refers to the complex network formed by regulatory interactions between genes in living cells. In this paper, we consider inferring GRNs in single cells based on single cell RNA sequencing (scRNA-seq) data. In scRNA-seq, single cells are often profiled from mixed populations and their cell identities are unknown. A common practice for single cell GRN analysis is to first cluster the cells and infer GRNs for every cluster separately. However, this two-step procedure ignores uncertainty in the clustering step and thus could lead to inaccurate estimation of the networks. To address this problem, we propose to model scRNA-seq by the mixture multivariate Poisson log-normal (MPLN) distribution. The precision matrices of the MPLN are the GRNs of different cell types and can be jointly estimated by maximizing MPLN's lasso-penalized log-likelihood. We show that the MPLN model is identifiable and the resulting penalized log-likelihood estimator is consistent. To avoid the intractable optimization of the MPLN's log-likelihood, we develop an algorithm called VMPLN based on the variational inference method. Comprehensive simulation and real scRNA-seq data analyses reveal that VMPLN performs better than the state-of-the-art single cell GRN methods.Comment: 95 pages,28 figure