Traditional training algorithms for Gumbel Softmax Variational Autoencoders (GS-VAEs) typically rely on an annealing scheme that gradually reduces the Softmax temperature τ according to a given function. This approach can lead to suboptimal results. To improve the performance, we propose a parallel framework for GS-VAEs, which embraces dual latent layers and multiple sub-models with diverse temperature strategies. Instead of relying on a fixed function for adjusting τ, our training algorithm uses loss difference as performance feedback to dynamically update each sub-model’s temperature τ, which is inspired by the need to balance exploration and exploitation in learning. By combining diversity in temperature strategies with the performance-based tuning method, our design helps prevent sub-models from becoming trapped in local optima and finds the GS-VAE model that best fits the given dataset. In experiments using four classic image datasets, our model significantly surpasses a standard GS-VAE that employs a temperature annealing scheme across multiple tasks, including data reconstruction, generalization capabilities, anomaly detection, and adversarial robustness. Our implementation is publicly available at https://github.com/wxzg7045/Gumbel-Softmax-VAE-2024/tree/main.

We revisit link lifetimes in random P2P graphs under dynamic node failure and create a unifying stochastic model that generalizes the majority of previous efforts in this direction. We not only allow nonexponential user lifetimes and age-dependent neighbor selection, but also cover both active and passive neighbor-management strategies, model the lifetimes of incoming and outgoing links, derive churn-related message volume of the system, and obtain the distribution of transient in/out degree at each user. We then discuss the impact of design parameters on overhead and resilience of the network.

Prior work has shown that resilience of random P2P graphs under dynamic node failure can be improved by age-biased neighbor selection that delivers outbound edges to more reliable users with higher probability. However, making the bias too aggressive may cause the group of nodes that receive connections to become small in comparison to graph size. As they become overloaded, these peers are forced to reject future connection requests, which leads to potentially unbounded join delays and high traffic overhead. To investigate these issues, we propose several analytical models for understanding the interplay between resilience and degree. We formulate a Pareto-optimal objective for this tradeoff, introduce new metrics of resilience and degree, analyze them under Pareto lifetimes, and discover that traditional techniques can be highly suboptimal in this setting. We then show evidence that optimization can be solved by a family of step-functions, which connect outgoing edges to uniformly random users whose age exceeds some threshold.

Previous analytical results on the resilience of unstructured P2P systems have not explicitly modeled heterogeneity of user churn (i.e, difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a unifying model of heterogeneous user churn and derive the distribution of the various metrics observed in prior experimental studies. We also show that the arrival process of in-edges to each user converges to Poisson when system size tends to infinity, model transient behavior of in-degree, and apply these results to obtain the joint in/out-degree isolation probability.

We revisit link lifetimes in random P2P graphs under dynamic node failure and create a unifying stochastic model that generalizes the majority of previous efforts in this direction. We not only allow non-exponential user lifetimes and age-dependent neighbor selection, but also cover both active and passive neighbor-management strategies, model the lifetimes of incoming and outgoing links, derive churn-related message volume of the system, and obtain the distribution of transient in/out degree at each user. We then discuss the impact of design parameters on overhead and resilience of the network.

Intrusion Detection Systems (IDS) have become ubiquitous in the defense against virus outbreaks, malicious exploits of OS vulnerabilities, and botnet proliferation. As attackers frequently rely on host scanning for reconnaissance leading to penetration, IDS is often tasked with detecting scans and preventing them. However, it is currently unknown how likely an IDS is to detect a given Internet-wide scan pattern and whether there exist sufficiently fast scan techniques that can remain virtually undetectable at large-scale. To address these questions, we propose a simple analytical model for the window-expiration rules of popular IDS tools (i.e., Snort and Bro) and utilize a variation of the Chen-Stein theorem to derive the probability that they detect some of the commonly used scan permutations. Using this analysis, we also prove the existence of stealth-optimal scan patterns, examine their performance, and contrast it with that of well-known techniques.

This paper builds a generic modeling framework for analyzing the edge-creation process in dynamic random graphs in which nodes continuously alternate between active and inactive states, which represent churn behavior of modern distributed systems. We prove that despite heterogeneity of node lifetimes, different initial out-degree, non-Poisson arrival/failure dynamics, and complex spatial and temporal dependency among creation of both initial and replacement edges, a superposition of edge-arrival processes to a live node under uniform selection converges to a Poisson process when system size becomes sufficiently large. Due to the convoluted dependency and non-renewal nature of various point processes, this result significantly advances classical Poisson convergence analysis and offers a simple analytical platform for future modeling of networks under churn in a wide range of degree-regular and -irregular graphs with arbitrary node lifetime distributions.

Previous analytical work on the resilience of P2P networks has been restricted to disconnection arising from simultaneous failure of all neighbors in routing tables of participating users. In this paper, we focus on a different technique for maintaining consistent graphs-Chord's successor sets and periodic stabilizations-under both static and dynamic node failure. We derive closed-form models for the probability that Chord remains connected under both types of node failure and show the effect of using different stabilization interval lengths (i.e., exponential, uniform, and constant) on the probability of partitioning in Chord.

Several models of user churn, resilience, and link lifetime have recently appeared in the literature; however, these results do not directly apply to classical Distributed Hash Tables (DHTs) in which neighbor replacement occurs not only when current users die, but also when new users arrive into the system, and where replacement choices are often restricted to the successor of the failed zone in the DHT space. To understand neighbor churn in such networks, which we call switching DHTs, this paper proposes a simple, yet accurate, model for capturing link dynamics in structured P2P systems and obtains the distribution of link lifetimes for fairly generic DHTs. Similar to, our results show that deterministic networks (e.g., Chord, CAN) unfortunately do not extract much benefit from heavy-tailed user lifetimes since link durations are dominated by small remaining lifetimes of newly arriving users that replace the more reliable existing neighbors. We also examine link lifetimes in randomized DHTs equipped with multiple choices for each link and show that selecting the best neighbor in these scenarios is rather complicated as it depends on the desired load balancing, link resilience, and overhead. We offer insight into the various selection algorithms, their performance, and possibilities for improvement.

Characterizing user churn has become an important topic in studying P2P networks, both in theoretical analysis and system design. Recent work has shown that direct sampling of user lifetimes may lead to certain bias (arising from missed peers and round-off inconsistencies) and proposed a technique that estimates lifetimes based on sampled residuals. In this paper, however, we show that under non-stationary arrivals, which are often present in real systems, residual-based sampling does not correctly reconstruct user lifetimes and suffers a varying degree of bias, which in some cases makes estimation completely impossible. We overcome this problem using two contributions: a novel non-stationary ON/OFF churn model and an unbiased randomized residual sampling technique for measuring user lifetimes. The former allows correlation between ON/OFF periods of the same user and exhibits different join rates during the day. The latter spreads sampling points uniformly during the day and uses a novel estimator to reconstruct the underlying lifetime distribution. We finish the paper with experimental measurements of Gnutella and discussing reduction in overhead compared to direct sampling methods.

Several models of user churn, resilience, and link lifetime have recently appeared in the literature [12], [13], [34], [35]; however, these results do not directly apply to classical distributed hash tables (DHTs) in which neighbor replacement occurs not only when current users die, but also when new user arrive into the system, and where replacement choices are often restricted to the successor of the failed zone in the DHT space. To understand neighbor churn in such networks, this paper proposes a simple, yet accurate, model for capturing link dynamics in structured P2P systems and obtains the distribution of link lifetimes for fairly generic DHTs. Similar to [8], our results show that deterministic networks (e.g., Chord [28], CAN [24]) unfortunately do not extract much benefit from heavy-tailed user lifetimes since link durations are dominated by small remaining lifetimes of newly arriving users that replace the more reliable existing neighbors. We also examine link lifetimes in randomized DHTs equipped with multiple choices for each link and show that users in such systems should prefer neighbors with smaller zones rather than larger age as suggested in prior work [13], [30]. We finish the paper by demonstrating the effectiveness of the proposed min-zone neighbor selection for heavy-tailed user lifetime distributions with the shape parameter alpha obtained from recent measurements [4], [31].

Previous analytical work [15], [16] on the resilience of P2P networks has been restricted to disconnection arising from simultaneous failure of all neighbors in routing tables of participating users. In this paper, we focus on a different technique for maintaining consistent graphs - Chord's successor sets and periodic stabilizations - under both static and dynamic node failure. We derive closed-form models for the probability that Chord remains connected under both types of node failure and show the effect of using different stabilization interval lengths (i.e., exponential, uniform, and constant) on the probability of partitioning in Chord.

Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users "created" in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape a alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.9.

Previous analytical studies [12], [18] of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared to uniform selection of neighbors. In fact, the second strategy based on random walks on age-weighted graphs demonstrates that for lifetimes with infinite variance, the system monotonically increases its resilience as its age and size grow. Specifically, we show that the probability of isolation converges to zero as these two metrics tend to infinity. We finish the paper with simulations in finite-size graphs that demonstrate the effect of this result in practice.

Previous analytical results on the resilience of un-structured P2P systems have not explicitly modeled heterogeneity of user churn (i.e., difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a generic model of heterogeneous user churn, derive the distribution of the various metrics observed in prior experimental studies (e.g., lifetime distribution of joining users, joint distribution of session time of alive peers, and residual lifetime of a randomly selected user), derive several closed-form results on the transient behavior of in-degree, and eventually obtain the joint in/out degree isolation probability as a simple extension of the out-degree model in [13].

In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, under both types of node failure. We finish the paper by demonstrating that our models match simulations very well and that dynamic P2P systems are extremely resilient under node churn as long as the neighbor replacement delay is much smaller than the average user lifetime.

We propose a new bidirectional hierarchical clustering system for addressing challenges of Web mining. The key feature of the approach is that it aims to maximize the intra-cluster similarity in the bottom-up cluster-merging phase and it ensures to minimize the inter-cluster similarity in the top-down refinement phase. This two-pass approach achieves better clustering than existing one-pass approaches. We also propose a new cluster-merging criterion for allowing more than two clusters to be merged in each step and a new measure of similarity for taking into consideration not only the inter-connectivity between clusters but also the internal connectivity within the clusters. These result in reducing the average complexity for creating the final hierarchical structure of clusters from O(n/sup 2/) to O(n). The hierarchical structure represents a semantic structure between concepts of clusters and is directly applicable to the future of semantic net.