An Evasion and Counter-Evasion Study in Malicious Websites Detection

Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites detection: the attacker essentially has access to the same data that the defender uses to train its detection models. This 'symmetry' can be exploited by the attacker, at least in principle, to evade the defender's detection models. In this paper, we present a framework for characterizing the evasion and counter-evasion interactions between the attacker and the defender, where the attacker attempts to evade the defender's detection models by taking advantage of this symmetry. Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism. The framework is geared toward the popular detection model of decision tree, but can be adapted to accommodate other classifiers

Adaptive Epidemic Dynamics in Networks: Thresholds and Control

Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily accommodated in the biological epidemic models. In this paper we initiate the study of such a perspective, namely that of adaptive defense against epidemic spreading in arbitrary networks. More specifically, we investigate a non-homogeneous Susceptible-Infectious-Susceptible (SIS) model where the model parameters may vary with respect to time. In particular, we focus on two scenarios we call semi-adaptive defense and fully-adaptive} defense, which accommodate implicit and explicit dependency relationships between the model parameters, respectively. In the semi-adaptive defense scenario, the model's input parameters are given; the defense is semi-adaptive because the adjustment is implicitly dependent upon the outcome of virus spreading. For this scenario, we present a set of sufficient conditions (some are more general or succinct than others) under which the virus spreading will die out; such sufficient conditions are also known as epidemic thresholds in the literature. In the fully-adaptive defense scenario, some input parameters are not known (i.e., the aforementioned sufficient conditions are not applicable) but the defender can observe the outcome of virus spreading. For this scenario, we present adaptive control strategies under which the virus spreading will die out or will be contained to a desired level.Comment: 20 pages, 8 figures. This paper was submitted in March 2009, revised in August 2009, and accepted in December 2009. However, the paper was not officially published until 2014 due to non-technical reason

LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF

Neural Radiance Fields (NeRFs) have made great success in representing complex 3D scenes with high-resolution details and efficient memory. Nevertheless, current NeRF-based pose estimators have no initial pose prediction and are prone to local optima during optimization. In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF. In place recognition stage, we train a regressor through images generated from trained NeRFs, which provides an initial value for global localization. In pose optimization stage, we minimize the residual between the observed image and rendered image by directly optimizing the pose on tangent plane. To avoid convergence to local optimum, we introduce a Truncated Dynamic Low-pass Filter (TDLF) for coarse-to-fine pose registration. We evaluate our method on both synthetic and real-world data and show its potential applications for high-precision navigation in large-scale city scenes. Codes and data will be publicly available at https://github.com/jike5/LATITUDE.Comment: 7 pages, 6 figures, submitted to ICRA 202

MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis

MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis Changsheng Du1,5, Chang Liu1,5, Jiuhong Kang1,2, Guixian Zhao3, Zhiqiang Ye4, Shichao Huang1, Zhenxin Li3, Zhiying Wu3 & Gang Pei1,2 Interleukin 17 (IL-17)-producing T helper cells (TH-17 cells) are increasingly recognized as key participants in various autoimmune diseases, including multiple sclerosis. Although sets of transcription factors and cytokines are known to regulate TH-17 differentiation, the role of noncoding RNA is poorly understood. Here we identify a TH-17 cell–associated microRNA, miR-326, whose expression was highly correlated with disease severity in patients with multiple sclerosis and mice with experimental autoimmune encephalomyelitis (EAE). In vivo silencing of miR-326 resulted in fewer TH-17 cells and mild EAE, and its overexpression led to more TH-17 cells and severe EAE. We also found that miR-326 promoted TH-17 differentiation by targeting Ets-1, a negative regulator of TH-17 differentiation. Our data show a critical role for microRNA in TH-17 differentiation and the pathogenesis of multiple sclerosis

Intrauterine Growth Restriction Induces Adulthood Chronic Metabolic Disorder in Cardiac and Skeletal Muscles

ObjectiveAlthough population-based studies of intrauterine growth restriction (IUGR) demonstrated a series of postnatal complications, several studies identified that IUGR could definitely cause dysfunction of metabolism of cardiac and skeletal muscles in the perinatal period and early life. However, it is still unknown if such metabolic alternation would remain for long term or not, and whether normal protein diet administration postnatally would protect the IUGR offsprings from a “catch-up growth” and be able to reverse the premature metabolic remodeling.Materials and MethodsWe established an IUGR rat model with pregnant rats and a low-protein diet, and the developmental phenotypes had been carefully recorded. The cardiac and skeletal muscles had been collected to undergo RNA-seq.ResultsAccording to a series of comparisons of transcriptomes among various developmental processes, programmed metabolic dysfunction and chronic inflammation activity had been identified by transcriptome sequencing in IUGR offsprings, even such rats presented a normal developmental curve or body weight after normal postnatal diet feeding.ConclusionThe data revealed that IUGR had a significant adverse impact on long-term cardiovascular function in rats, even they exhibit good nutritional status. So that, the fetal stage adverse events would encode the lifelong disease risk, which could hide in young age. This study remaindered that the research on long-term molecular changes is important, and only nutrition improvement would not totally reverse the damage of IUGR

Circ_0040039 May Aggravate Intervertebral Disk Degeneration by Regulating the MiR-874-3p-ESR1 Pathway

The functional alteration of nucleus pulposus cells (NPCs) exerts a crucial role in the occurrence and progression of intervertebral disk degeneration (IDD). Circular RNAs and microRNAs (miRs) are critical regulators of NPC metabolic processes such as growth and apoptosis. In this study, bioinformatics tools, encompassing Gene Ontology pathway and Venn diagrams analysis, and protein–protein interaction (PPI) network construction were used to identify functional molecules related to IDD. PPI network unveiled that ESR1 was one of the most critical genes in IDD. Then, a key IDD-related circ_0040039-miR-874-3p-ESR1 interaction network was predicted and constructed. Circ_0040039 promoted miR-874-3p and repressed ESR1 expression, and miR-874-3p repressed ESR1 expression in NPCs, suggesting ESR1 might be a direct target of miR-874-3p. Functionally, circ_0040039 could enhance NPC apoptosis and inhibit NPC growth, revealing that circ_0040039 might aggravate IDD by stabilizing miR-874-3p and further upregulating the miR-874-3p-ESR1 pathway. This signaling pathway might provide a novel therapeutic strategy and targets for the diagnosis and therapy of IDD-related diseases