An official website of the United States government
Denial of service (DoS) attacks increasingly exploit algorithmic, semantic, or implementation characteristics dormant in victim applications, often with minimal attacker resources. Practical and efficient detection of these asymmetric DoS attacks requires us to (i) catch offending requests in-flight, before they consume a critical amount of resources, (ii) remain agnostic to the application internals, such as the programming language or runtime system, and (iii) introduce low overhead in terms of both performance and programmer effort. This paper introduces FINELAME, a language-independent framework for detecting asymmetric DoS attacks. FINELAME leverages operating system visibility across the entire software stack to instrument key resource allocation and negotiation points. It leverages recent advances in the Linux extended Berkeley Packet Filter virtual machine to attach application-level interposition probes to key request processing functions, and lightweight resource monitors--user/kernel-level probes--to key resource allocation functions. The data collected is used to train a model of resource utilization that occurs throughout the lifetime of individual requests. The model parameters are then shared with the resource monitors, which use them to catch offending requests in-flight, inline with resource allocation. We demonstrate that FINELAME can be integrated with legacy applications with minimal effort, and that it is able to detect resource abuse attacks much earlier than their intended completion time while posing low performance overheads.
more »
« less
This content will become publicly available on October 13, 2026
Modal Verification Patterns for Systems Software
Although they differ in the functionality they offer, low-level systems exhibit certain patterns of design and utilization of computing resources. In this paper we examine how modalities have emerged as a common structure in formal verification of low-level software, and explain how many recent examples naturally share common structure in the relationship between the modalities and software features they are used to reason about. We explain how the concept of a resource context (a class of system resources to reason about) naturally corresponds to families of modal operators indexed by system data, and how this naturally leads to using modal assertions to describe resource elements (data in the relevant context).
more »
« less
- Award ID(s):
- 1844964
- PAR ID:
- 10641498
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 25 to 33
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Artificial intelligence, machine learning, and algorithmic techniques in general, provide two crucial abilities with the potential to improve decision-making in the context of allocation of scarce societal resources. They have the ability to flexibly and accurately model treatment response at the individual level, potentially allowing us to better match available resources to individuals. In addition, they have the ability to reason simultaneously about the effects of matching sets of scarce resources to populations of individuals. In this work, we leverage these abilities to study algorithmic allocation of scarce societal resources in the context of homelessness. In communities throughout the United States, there is constant demand for an array of homeless services intended to address different levels of need. Allocations of housing services must match households to appropriate services that continuously fluctuate in availability, while inefficiencies in allocation could “waste” scarce resources as households will remain in-need and re-enter the homeless system, increasing the overall demand for homeless services. This complex allocation problem introduces novel technical and ethical challenges. Using administrative data from a regional homeless system, we formulate the problem of “optimal” allocation of resources given data on households with need for homeless services. The optimization problem aims to allocate available resources such that predicted probabilities of household re-entry are minimized. The key element of this work is its use of a counterfactual prediction approach that predicts household probabilities of re-entry into homeless services if assigned to each service. Through these counterfactual predictions, we find that this approach has the potential to improve the efficiency of the homeless system by reducing re-entry, and, therefore, system-wide demand. However, efficiency comes with trade-offs - a significant fraction of households are assigned to services that increase probability of re-entry. To address this issue as well as the inherent fairness considerations present in any context where there are insufficient resources to meet demand, we discuss the efficiency, equity, and fairness issues that arise in our work and consider potential implications for homeless policies.more » « less
-
Over the last decade, research has revealed the high prevalence of cyberbullying among youth and raised serious concerns in society. Information on the social media platforms where cyberbullying is most prevalent (e.g., Instagram, Facebook, Twitter) is inherently multi-modal, yet most existing work on cyberbullying identification has focused solely on building generic classification models that rely exclusively on text analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data (e.g., image, video, user profile, time, and location), and thus fail to offer a comprehensive understanding of cyberbullying. Conventionally, when information from different modalities is presented together, it often reveals complementary insights about the application domain and facilitates better learning performance. In this paper, we study the novel problem of cyberbullying detection within a multi-modal context by exploiting social media data in a collaborative way. This task, however, is challenging due to the complex combination of both cross-modal correlations among various modalities and structural dependencies between different social media sessions, and the diverse attribute information of different modalities. To address these challenges, we propose XBully, a novel cyberbullying detection framework, that first reformulates multi-modal social media data as a heterogeneous network and then aims to learn node embedding representations upon it. Extensive experimental evaluations on real-world multi-modal social media datasets show that the XBully framework is superior to the state-of-the-art cyberbullying detection models.more » « less
-
Understanding how resource characteristics influence variability in social and material inequality among foraging populations is a prominent area of research. However, obtaining cross-comparative data from which to evaluate theoretically informed resource characteristic factors has proved difficult, particularly for investigating interactions of characteristics. Therefore, we develop an agent-based model to evaluate how five key characteristics of primary resources (predictability, heterogeneity, abundance, economy of scale and monopolizability) structure pay-offs and explore how they interact to favour both egalitarianism and inequality. Using iterated simulations from 243 unique combinations of resource characteristics analysed with an ensemble machine-learning approach, we find the predictability and heterogeneity of key resources have the greatest influence on selection for egalitarian and nonegalitarian outcomes. These results help explain the prevalence of egalitarianism among foraging populations, as many groups probably relied on resources that were both relatively less predictable and more homogeneously distributed. The results also help explain rare forager inequality, as comparison with ethnographic and archaeological examples suggests the instances of inequality track strongly with reliance on resources that were predictable and heterogeneously distributed. Future work quantifying comparable measures of these two variables, in particular, may be able to identify additional instances of forager inequality. This article is part of the theme issue ‘Evolutionary ecology of inequality’.more » « less
-
Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen (Ed.)This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.more » « less
