Showing 1–21 of 21 results for author: Apostolaki, M

The Frontier LLM Trap in Network Automation

Authors: Minhao Jin, Sean Wang, Aarti Gupta, Maria Apostolaki

Abstract: Large LLMs are powerful tools for network automation, but they are expensive, slow to serve, hard to audit, poorly tailored to individual networks, and create long-term dependencies on a small number of AI providers. Existing alternatives fall short: small open-source models are cheaper but unreliable, while deterministic scripts and verification are controllable but hard to build and maintain.… ▽ More Large LLMs are powerful tools for network automation, but they are expensive, slow to serve, hard to audit, poorly tailored to individual networks, and create long-term dependencies on a small number of AI providers. Existing alternatives fall short: small open-source models are cheaper but unreliable, while deterministic scripts and verification are controllable but hard to build and maintain. We propose a middle ground. An offline loop composed of fuzzing and validation discovers the recurring mistakes small models make, then resulting networking knowledge is expressed as explicit logic rules. In production, these rules guide a small model on tasks such as configuration translation, yielding automation that is cheaper, lower-latency, auditable, and easier to adapt to a specific network. More critically, network knowledge and operational experience stay and are accumulated where they belong, the network itself, not a rented service. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Let AI Agents Translate Networks, Not Reason About Them

Authors: Hongyu Hè, Maria Apostolaki

Abstract: A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is hard to keep current as the network changes frequently. At its core, network modeling is a typographical exercise: it translates network artifacts (e.g., configurations, topology,… ▽ More A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network has one, since writing a model by hand demands rare expertise and is hard to keep current as the network changes frequently. At its core, network modeling is a typographical exercise: it translates network artifacts (e.g., configurations, topology, and routing state) into rules in formal logic. Translation of this kind is what large language models (LLMs) nowadays do well. Unlike free-form AI reasoning, such translation can be formally verified. Once modeling is no longer the bottleneck, trusting AI to reason over large, complex networks no longer makes sense. Our position therefore cuts against the prevailing race to put autonomous AI agents in charge end-to-end. We instead confine AI to translation and rely on a solver for reliable long-horizon reasoning, building a reusable formal model of general network behavior that can then be specialized to specific tasks, e.g., root-cause analysis (RCA). We build TypoNet that constructs and validates a symbolic model of an emulated production-scale WAN from the network's own artifacts. Our preliminary evaluation shows TypoNet helps in two ways. On its own, TypoNet answers operational questions (e.g., reachability verification and change-impact analysis) faster, more cheaply, and more reliably than an LLM. As a tool for an AI agent, TypoNet boosts fault localization at lower cost. The result makes the case for AI that builds verifiable network models and relies on a solver for reliable long-horizon reasoning. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 8 pages, 3 figures, 1 table

MSC Class: 68M15 (Primary); 68Q60; 68T27; 68T30 (Secondary) ACM Class: C.2.3; D.2.4; I.2.3

Invariant Discovery for Networked Systems

Authors: Hongyu Hè, Alexander Krentsel, Sylvia Ratnasamy, Maria Apostolaki

Abstract: Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation, yet writing them by hand demands rare expertise in both formal logic and networking. Automatic miners can help but fall short on two fronts: they still require the hardest input (the grammar of admissible invarian… ▽ More Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation, yet writing them by hand demands rare expertise in both formal logic and networking. Automatic miners can help but fall short on two fronts: they still require the hardest input (the grammar of admissible invariants) and they learn only exact, ``hard'' rules, struggling with real-world approximation caused by inherent noise in data. LLMs are tools that can provide semantic reasoning over data, but are non-deterministic and opaque in their learning. Our key idea is to partition the invariant search problem into an AI-driven grammar ``discovery'' problem, followed by a statistics-driven ``search'' problem within the learned grammar. Taken together, this allows non-deterministic, hallucination-prone AI to help produce auditable invariants with formal guarantees. We design and implement such a system, Autogram, and evaluate it on both public and production telemetry data, recovering expert-derived invariants with high coverage and low false positives. We close with discussion on open problems on the path toward fully open-ended discovery. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 8 pages, 4 figures, 1 table

MSC Class: 68M15 (Primary); 68T27; 68T05 (Secondary) ACM Class: C.2.3; I.2.6; I.2.3

HOWLR: A Client-Driven Approach to BGP Hijack Detection

Authors: Constantine Doumanidis, Anya Kalogerakos, Maria Apostolaki

Abstract: BGP hijacking enables impersonation attacks in which adversaries divert traffic at the prefix level and serve malicious content to unsuspecting clients. Detecting such attacks has traditionally been the responsibility of network operators, leaving end hosts exposed for hours. We argue that end hosts can detect prefix-level impersonation independently, exploiting a fundamental asymmetry: a BGP hija… ▽ More BGP hijacking enables impersonation attacks in which adversaries divert traffic at the prefix level and serve malicious content to unsuspecting clients. Detecting such attacks has traditionally been the responsibility of network operators, leaving end hosts exposed for hours. We argue that end hosts can detect prefix-level impersonation independently, exploiting a fundamental asymmetry: a BGP hijack diverts traffic for an entire IP prefix, but impersonating every co-hosted service within that prefix is prohibitively difficult at scale, especially if each service is authenticated by a different Certificate Authority. We propose HOWLR, a tool that operationalizes this insight by using co-hosted, TLS-authenticated services as witnesses: if a client can no longer authenticate them, it has evidence of an ongoing attack. This work evaluates the feasibility of this method by quantifying the existence and diversity of witnesses in the wild. We show that HOWLR can protect 89% of Tor relay prefixes, and 75% of Bitcoin pool gateway prefixes. △ Less

Submitted 19 June, 2026; originally announced June 2026.

Comments: 11 pages, 7 figures

ACM Class: C.2.2

Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers

Authors: Hongyu Hè, Minhao Jin, Maria Apostolaki

Abstract: RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed-loop natu… ▽ More RL-based controllers achieve strong average-case performance in networking tasks such as congestion control and adaptive bitrate streaming. Yet their performance can degrade severely under network conditions where strong performance is still achievable. Identifying such conditions and quantifying the resulting performance gap is intractable by enumeration, while the sequential and closed-loop nature of RL controllers makes formal verification methods impractical. We present ReGuard, a framework that discovers worst-case scenarios for a given RL controller and protects it against them at inference time without retraining. Discovery is formulated as a bilevel regret-maximization problem, which yields a certified lower bound on the worst-case performance gap. The discovered trajectories are then analyzed as counterfactuals and compiled into lightweight logic rules that intervene only when a risky state is detected, leaving the controller's behavior unchanged otherwise. We evaluate ReGuard across three RL-based network controllers: Pensieve, Sage, and Park. ReGuard discovers scenarios in which the controller's performance is 43$-$64% worse than what is achievable. ReGuard not only discovers gaps 57% to 6$\times$ larger than those found by the strongest baselines but also shrinks them by 79$-$85% via lightweight rule-based protection while preserving nominal performance. ReGuard's protection extends beyond the scenarios it discovers, improving performance across a wider range of network conditions. △ Less

Submitted 5 May, 2026; originally announced May 2026.

Comments: 23 pages, 12 figures, 4 tables

MSC Class: 68M12; 68M15; 68T05; 68Q25; 68M20 ACM Class: C.2.2; I.2.6; C.4; C.2.6

To Reconfigure or Not to Reconfigure: Optimizing All-to-All Collectives in Circuit-Switched Photonic Interconnects

Authors: Anchengcheng Zhou, Vamsi Addanki, Maria Apostolaki

Abstract: All-to-all collective communication is a core primitive in distributed machine learning and high-performance computing. At the server scale, the communication demands of these workloads are increasingly outstripping the bandwidth and energy limits of electrical interconnects, driving a growing interest in photonic interconnects. However, leveraging these interconnects for all-to-all communication… ▽ More All-to-all collective communication is a core primitive in distributed machine learning and high-performance computing. At the server scale, the communication demands of these workloads are increasingly outstripping the bandwidth and energy limits of electrical interconnects, driving a growing interest in photonic interconnects. However, leveraging these interconnects for all-to-all communication is nontrivial. The core challenge lies in jointly optimizing a sequence of topologies and flow schedules, reconfiguring only when the transmission savings from traversing shorter paths outweigh the reconfiguration cost. Yet the search space of this joint optimization is enormous. Existing work sidesteps this challenge by making unrealistic assumptions on reconfiguration costs so that it is never or always worthwhile to reconfigure. In this paper, we show that any candidate sequence of topologies and flow schedules can be expressed as a sum of adjacency matrices and their powers. This abstraction captures the entire solution space and yields a lower bound on all-to-all completion time. Building on this formulation, we identify a family of topology sequences with strong symmetry and high expansion that admits bandwidth-efficient schedules, which our algorithm constructs with low computational overhead. Together, these insights allow us to efficiently construct near-optimal solutions, effectively avoiding enumeration of the combinatorial design space. Evaluation shows that our approach reduces all-to-all completion time by up to 44% on average across a wide range of network parameters, message sizes and workload types. △ Less

Submitted 10 February, 2026; originally announced February 2026.

Cross-Flow Correlations Survive Synthesis: Measuring Source-Level Privacy Leakage in Synthetic Network Traces

Authors: Minhao Jin, Hongyu Hè, Maria Apostolaki

Abstract: Synthetic network data generators (SynNetGens) are increasingly used to share realistic traffic traces without exposing sensitive raw data. While substantial effort has gone into improving fidelity, privacy is either assumed to be a built-in property of synthesis or addressed through differential privacy at the packet or flow level. This paper uncovers a fundamental privacy vulnerability: SynNet… ▽ More Synthetic network data generators (SynNetGens) are increasingly used to share realistic traffic traces without exposing sensitive raw data. While substantial effort has gone into improving fidelity, privacy is either assumed to be a built-in property of synthesis or addressed through differential privacy at the packet or flow level. This paper uncovers a fundamental privacy vulnerability: SynNetGens preserve cross-flow behavioral correlations that expose source-level membership, allowing an attacker to determine whether traffic of specific user, or service was included in the training data. This leakage arises from a mismatch in abstraction: existing SynNetGens operate and are protected at the packet or flow level, while sensitive information is encoded in correlations across flows from the same source. To demonstrate that this vulnerability is exploitable in practice, we develop TraceBleed, the first source-level membership inference attack against black-box SynNetGens. Our evaluation spans five datasets and six SynNetGens, revealing that: (i) every generator leaks source-level information on at least some datasets; (ii) flow- or packet-level differential privacy fails to protect source privacy unless fidelity is degraded to unusable levels; and (iii) releasing 10X more synthetic data amplifies leakage by 130% on average. To support ongoing research in this area, we will maintain a public privacy-fidelity leaderboard so practitioners can choose generators that fit their needs and researchers can benchmark new designs faithfully. △ Less

Submitted 8 May, 2026; v1 submitted 15 August, 2025; originally announced August 2025.

Data-Plane Telemetry to Mitigate Long-Distance BGP Hijacks

Authors: Satadal Sengupta, Hyojoon Kim, Daniel Jubas, Maria Apostolaki, Jennifer Rexford

Abstract: Poor security of Internet routing enables adversaries to divert user data through unintended infrastructures (hijack). Of particular concern -- and the focus of this paper -- are cases where attackers reroute domestic traffic through foreign countries, exposing it to surveillance, bypassing legal privacy protections, and posing national security threats. Efforts to detect and mitigate such attacks… ▽ More Poor security of Internet routing enables adversaries to divert user data through unintended infrastructures (hijack). Of particular concern -- and the focus of this paper -- are cases where attackers reroute domestic traffic through foreign countries, exposing it to surveillance, bypassing legal privacy protections, and posing national security threats. Efforts to detect and mitigate such attacks have focused primarily on the control plane while data-plane signals remain largely overlooked. In particular, change in propagation delay caused by rerouting offers a promising signal: the change is unavoidable and the increased propagation delay is directly observable from the affected networks. In this paper, we explore the practicality of using delay variations for hijack detection, addressing two key questions: (1) What coverage can this provide, given its heavy dependence on the geolocations of the sender, receiver, and adversary? and (2) Can an always-on latency-based detection system be deployed without disrupting normal network operations? We observe that for 86% of victim-attacker country pairs in the world, mid-attack delays exceed pre-attack delays by at least 25% in real deployments, making delay-based hijack detection promising. To demonstrate practicality, we design HiDe, which reliably detects delay surges from long-distance hijacks at line rate. We measure HiDe's accuracy and false-positive rate on real-world data and validate it with ethically conducted hijacks. △ Less

Submitted 20 July, 2025; originally announced July 2025.

Making Logic a First-Class Citizen in Generative ML for Networking

Authors: Hongyu Hè, Minhao Jin, Maria Apostolaki

Abstract: Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suffer from two shortcomings: \emph{(i)} their output is often visibly violating well-known networking rules, which undermines their trustworthiness; and \emph{(ii)} they are difficult to control, frequently requiring retra… ▽ More Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation. Despite their capabilities, they suffer from two shortcomings: \emph{(i)} their output is often visibly violating well-known networking rules, which undermines their trustworthiness; and \emph{(ii)} they are difficult to control, frequently requiring retraining even for minor changes. To address these limitations and unlock the benefits of generative models for networking, we propose a new paradigm for integrating explicit network knowledge, in the form of first-order logic rules, into ML models used for networking tasks. Rules capture well-known relationships among observed signals, e.g., that increased latency precedes packet loss. While the idea is conceptually straightforward, its realization is challenging: networking knowledge is rarely formalized into rules, and naively injecting rules into ML models often hampers their effectiveness. This paper introduces NetNomos, a multi-stage framework that \emph{(i)} learns rules directly from data (e.g., measurements); \emph{(ii)} filters them to select semantically meaningful ones; and \emph{(iii)} enforces them through collaborative generation between an ML model and a Satisfiability Modulo Theories (SMT) solver. %We evaluate NetNomos both component-wise and end-to-end across four diverse network datasets. We show that NetNomos learns diverse, meaningful rules from four real-world datasets and is 1.6--6.5$\times$ more scalable than DuoAI, a state-of-the-art (SOTA) rule-learning method. By enforcing these rules on a generic GPT-2 model, NetNomos achieves performance on par with or even surpassing specialized SOTA systems such as Zoom2Net and NetShare across three networking tasks: telemetry imputation, traffic forecasting, and synthetic data generation. △ Less

Submitted 29 April, 2026; v1 submitted 30 June, 2025; originally announced June 2025.

Comments: Published at NSDI '26; Code available at https://github.com/HongyuHe/NetNomos and https://github.com/HongyuHe/LeJIT

ACM Class: C.2.3; I.2.6; I.2.3

Routing Attacks in Ethereum PoS: A Systematic Exploration

Authors: Constantine Doumanidis, Maria Apostolaki

Abstract: With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper ma… ▽ More With the promise of greater decentralization and sustainability, Ethereum transitioned from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism. The new consensus protocol introduces novel vulnerabilities that warrant further investigation. The goal of this paper is to investigate the security of Ethereum's PoS system from an Internet routing perspective. To this end, this paper makes two contributions: First, we devise a novel framework for inferring the distribution of validators on the Internet without disturbing the real network. Second, we introduce a class of network-level attacks on Ethereum's PoS system that jointly exploit Internet routing vulnerabilities with the protocol's reward and penalty mechanisms. We describe two representative attacks: StakeBleed, where the attacker triggers an inactivity leak, halting block finality and causing financial losses for all validators; and KnockBlock, where the attacker increases her expected MEV gains by preventing targeted blocks from being included in the chain. We find that both attacks are practical and effective. An attacker executing StakeBleed can inflict losses of almost 300 ETH in just 2 hours by hijacking as few as 30 IP prefixes. An attacker implementing KnockBlock could increase their MEV expected gains by 44.5% while hijacking a single prefix for less than 2 minutes. Our paper serves as a call to action for validators to reinforce their Internet routing infrastructure and for the Ethereum P2P protocol to implement stronger mechanisms to conceal validator locations. △ Less

Submitted 7 April, 2026; v1 submitted 12 May, 2025; originally announced May 2025.

Comments: 26 pages, 10 figures, to be published in Financial Cryptography and Data Security 2026

ACM Class: C.2.2

Robustifying ML-powered Network Classifiers with PANTS

Authors: Minhao Jin, Maria Apostolaki

Abstract: Multiple network management tasks, from resource allocation to intrusion detection, rely on some form of ML-based network traffic classification (MNC). Despite their potential, MNCs are vulnerable to adversarial inputs, which can lead to outages, poor decision-making, and security violations, among other issues. The goal of this paper is to help network operators assess and enhance the robustness… ▽ More Multiple network management tasks, from resource allocation to intrusion detection, rely on some form of ML-based network traffic classification (MNC). Despite their potential, MNCs are vulnerable to adversarial inputs, which can lead to outages, poor decision-making, and security violations, among other issues. The goal of this paper is to help network operators assess and enhance the robustness of their MNC against adversarial inputs. The most critical step for this is generating inputs that can fool the MNC while being realizable under various threat models. Compared to other ML models, finding adversarial inputs against MNCs is more challenging due to the existence of non-differentiable components e.g., traffic engineering and the need to constrain inputs to preserve semantics and ensure reliability. These factors prevent the direct use of well-established gradient-based methods developed in adversarial ML (AML). To address these challenges, we introduce PANTS, a practical white-box framework that uniquely integrates AML techniques with Satisfiability Modulo Theories (SMT) solvers to generate adversarial inputs for MNCs. We also embed PANTS into an iterative adversarial training process that enhances the robustness of MNCs against adversarial inputs. PANTS is 70% and 2x more likely in median to find adversarial inputs against target MNCs compared to state-of-the-art baselines, namely Amoeba and BAP. PANTS improves the robustness of the target MNCs by 52.7% (even against attackers outside of what is considered during robustification) without sacrificing their accuracy. △ Less

Submitted 31 January, 2025; v1 submitted 6 September, 2024; originally announced September 2024.

Global BGP Attacks that Evade Route Monitoring

Authors: Henry Birge-Lee, Maria Apostolaki, Jennifer Rexford

Abstract: As the deployment of comprehensive Border Gateway Protocol (BGP) security measures is still in progress, BGP monitoring continues to play a critical role in protecting the Internet from routing attacks. Fundamentally, monitoring involves observing BGP feeds to detect suspicious announcements and taking defensive action. However, BGP monitoring relies on seeing the malicious BGP announcement in the… ▽ More As the deployment of comprehensive Border Gateway Protocol (BGP) security measures is still in progress, BGP monitoring continues to play a critical role in protecting the Internet from routing attacks. Fundamentally, monitoring involves observing BGP feeds to detect suspicious announcements and taking defensive action. However, BGP monitoring relies on seeing the malicious BGP announcement in the first place! In this paper, we develop a novel attack that can hide itself from all state-of-the-art BGP monitoring systems we tested while affecting the entire Internet. The attack involves launching a sub-prefix hijack with the RFC-specified NO_EXPORT community attached to prevent networks with the malicious route installed from sending the route to BGP monitoring systems. We study the viability of this attack at four tier-1 networks and find all networks we studied were vulnerable to the attack. Finally, we propose a mitigation that significantly improves the robustness of the BGP monitoring ecosystem. Our paper aims to raise awareness of this issue and offer guidance to providers to protect against such attacks. △ Less

Submitted 18 August, 2024; originally announced August 2024.

Comments: 10 pages

Network Function Capacity Reconnaissance by Remote Adversaries

Authors: Aqsa Kashaf, Aidan Walsh, Maria Apostolaki, Vyas Sekar, Yuvraj Agarwal

Abstract: There is anecdotal evidence that attackers use reconnaissance to learn the capacity of their victims before DDoS attacks to maximize their impact. The first step to mitigate capacity reconnaissance attacks is to understand their feasibility. However, the feasibility of capacity reconnaissance in network functions (NFs) (e.g., firewalls, NATs) is unknown. To this end, we formulate the problem of ne… ▽ More There is anecdotal evidence that attackers use reconnaissance to learn the capacity of their victims before DDoS attacks to maximize their impact. The first step to mitigate capacity reconnaissance attacks is to understand their feasibility. However, the feasibility of capacity reconnaissance in network functions (NFs) (e.g., firewalls, NATs) is unknown. To this end, we formulate the problem of network function capacity reconnaissance (NFCR) and explore the feasibility of inferring the processing capacity of an NF while avoiding detection. We identify key factors that make NFCR challenging and analyze how these factors affect accuracy (measured as a divergence from ground truth) and stealthiness (measured in packets sent). We propose a flexible tool, NFTY, that performs NFCR and we evaluate two practical NFTY configurations to showcase the stealthiness vs. accuracy tradeoffs. We evaluate these strategies in controlled, Internet and/or cloud settings with commercial NFs. NFTY can accurately estimate the capacity of different NF deployments within 10% error in the controlled experiments and the Internet, and within 7% error for a commercial NF deployed in the cloud (AWS). Moreover, NFTY outperforms link-bandwidth estimation baselines by up to 30x. △ Less

Submitted 15 May, 2024; originally announced May 2024.

Super-resolution on network telemetry time series

Authors: Fengchen Gong, Divya Raghunathan, Aarti Gupta, Maria Apostolaki

Abstract: Fine-grained monitoring is crucial for multiple data-driven tasks such as debugging, provisioning, and securing networks. Yet, practical constraints in collecting, extracting, and storing data often force operators to use coarse-grained sampled monitoring, degrading the performance of the various tasks. In this work, we explore the feasibility of leveraging the correlations among coarse-grained ti… ▽ More Fine-grained monitoring is crucial for multiple data-driven tasks such as debugging, provisioning, and securing networks. Yet, practical constraints in collecting, extracting, and storing data often force operators to use coarse-grained sampled monitoring, degrading the performance of the various tasks. In this work, we explore the feasibility of leveraging the correlations among coarse-grained time series to impute their fine-grained counterparts in software. We present Zoom2Net, a transformer-based model for network imputation that incorporates domain knowledge through operational and measurement constraints, ensuring that the imputed network telemetry time series are not only realistic but also align with existing measurements and are plausible. This approach enhances the capabilities of current monitoring infrastructures, allowing operators to gain more insights into system behaviors without the need for hardware upgrades. We evaluate Zoom2Net on four diverse datasets (e.g. cloud telemetry and Internet data transfer) and use cases (such as bursts analysis and traffic classification). We demonstrate that Zoom2Net consistently achieves high imputation accuracy with a zoom-in factor of up to 100 and performs better on downstream tasks compared to baselines by an average of 38%. △ Less

Submitted 6 March, 2024; originally announced March 2024.

Confucius: Achieving Consistent Low Latency with Practical Queue Management for Real-Time Communications

Authors: Zili Meng, Nirav Atre, Mingwei Xu, Justine Sherry, Maria Apostolaki

Abstract: Real-time communication applications require consistently low latency, which is often disrupted by latency spikes caused by competing flows, especially Web traffic. We identify the root cause of disruptions in such cases as the mismatch between the abrupt bandwidth allocation adjustment of queue scheduling and gradual congestion window adjustment of congestion control. For example, when a sudden b… ▽ More Real-time communication applications require consistently low latency, which is often disrupted by latency spikes caused by competing flows, especially Web traffic. We identify the root cause of disruptions in such cases as the mismatch between the abrupt bandwidth allocation adjustment of queue scheduling and gradual congestion window adjustment of congestion control. For example, when a sudden burst of new Web flows arrives, queue schedulers abruptly shift bandwidth away from the existing real-time flow(s). The real-time flow will need several RTTs to converge to the new available bandwidth, during which severe stalls occur. In this paper, we present Confucius, a practical queue management scheme designed for offering real-time traffic with consistently low latency regardless of competing flows. Confucius slows down bandwidth adjustment to match the reaction of congestion control, such that the end host can reduce the sending rate without incurring latency spikes. Importantly, Confucius does not require the collaboration of end-hosts (e.g., labels on packets), nor manual parameter tuning to achieve good performance. Extensive experiments show that Confucius outperforms existing practical queueing schemes by reducing the stall duration by more than 50%, while the competing flows also fairly enjoy on-par performance. △ Less

Submitted 7 February, 2024; v1 submitted 27 October, 2023; originally announced October 2023.

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

Authors: Mehdi Hosseinzadeh, Karthick Shankar, Maria Apostolaki, Jay Ramachandran, Steven Adams, Vyas Sekar, Bruno Sinopoli

Abstract: Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions;… ▽ More Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CANE, a practical network-assisted QoE framework. CANE uses machine learning techniques to approximate each player's behavior as a black-box model and model predictive control to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by ~50% compared to pure client-side adaptive bitrate algorithms and by ~20% compared to uniform traffic shaping. △ Less

Submitted 13 January, 2023; originally announced January 2023.

FB: A Flexible Buffer Management Scheme for Data Center Switches

Authors: Maria Apostolaki, Vamsi Addanki, Manya Ghobadi, Laurent Vanbever

Abstract: Today, network devices share buffer across priority queues to avoid drops during transient congestion. While cost-effective most of the time, this sharing can cause undesired interference among seemingly independent traffic. As a result, low-priority traffic can cause increased packet loss to high-priority traffic. Similarly, long flows can prevent the buffer from absorbing incoming bursts even if… ▽ More Today, network devices share buffer across priority queues to avoid drops during transient congestion. While cost-effective most of the time, this sharing can cause undesired interference among seemingly independent traffic. As a result, low-priority traffic can cause increased packet loss to high-priority traffic. Similarly, long flows can prevent the buffer from absorbing incoming bursts even if they do not share the same queue. The cause of this perhaps unintuitive outcome is that today's buffer sharing techniques are unable to guarantee isolation across (priority) queues without statically allocating buffer space. To address this issue, we designed FB, a novel buffer sharing scheme that offers strict isolation guarantees to high-priority traffic without sacrificing link utilizations. Thus, FB outperforms conventional buffer sharing algorithms in absorbing bursts while achieving on-par throughput. We show that FB is practical and runs at line-rate on existing hardware (Barefoot Tofino). Significantly, FB's operations can be approximated in non-programmable devices. △ Less

Submitted 21 May, 2021; originally announced May 2021.

Securing Internet Applications from Routing Attacks

Authors: Yixin Sun, Maria Apostolaki, Henry Birge-Lee, Laurent Vanbever, Jennifer Rexford, Mung Chiang, Prateek Mittal

Abstract: Attacks on Internet routing are typically viewed through the lens of availability and confidentiality, assuming an adversary that either discards traffic or performs eavesdropping. Yet, a strategic adversary can use routing attacks to compromise the security of critical Internet applications like Tor, certificate authorities, and the bitcoin network. In this paper, we survey such application-speci… ▽ More Attacks on Internet routing are typically viewed through the lens of availability and confidentiality, assuming an adversary that either discards traffic or performs eavesdropping. Yet, a strategic adversary can use routing attacks to compromise the security of critical Internet applications like Tor, certificate authorities, and the bitcoin network. In this paper, we survey such application-specific routing attacks and argue that both application-layer and network-layer defenses are essential and urgently needed. While application-layer defenses are easier to deploy in the short term, we hope that our work serves to provide much needed momentum for the deployment of network-layer defenses. △ Less

Submitted 11 August, 2020; v1 submitted 20 April, 2020; originally announced April 2020.

Performance-Driven Internet Path Selection

Authors: Maria Apostolaki, Ankit Singla, Laurent Vanbever

Abstract: Internet routing can often be sub-optimal, with the chosen routes providing worse performance than other available policy-compliant routes. This stems from the lack of visibility into route performance at the network layer. While this is an old problem, we argue that recent advances in programmable hardware finally open up the possibility of performance-aware routing in a deployable, BGP-compatibl… ▽ More Internet routing can often be sub-optimal, with the chosen routes providing worse performance than other available policy-compliant routes. This stems from the lack of visibility into route performance at the network layer. While this is an old problem, we argue that recent advances in programmable hardware finally open up the possibility of performance-aware routing in a deployable, BGP-compatible manner. We introduce ROUTESCOUT, a hybrid hardware/software system supporting performance-based routing at ISP scale. In the data plane, ROUTESCOUT leverages P4-enabled hardware to monitor performance across policy-compliant route choices for each destination, at line-rate and with a small memory footprint. ROUTESCOUT's control plane then asynchronously pulls aggregated performance metrics to synthesize a performance-aware forwarding policy. We show that ROUTESCOUT can monitor performance across most of an ISP's traffic, using only 4 MB of memory. Further, its control can flexibly satisfy a variety of operator objectives, with sub-second operating times. △ Less

Submitted 18 November, 2020; v1 submitted 21 January, 2020; originally announced January 2020.

SABRE: Protecting Bitcoin against Routing Attacks

Authors: Maria Apostolaki, Gian Marti, Jan Müller, Laurent Vanbever

Abstract: Routing attacks remain practically effective in the Internet today as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communication to reach consensus. In particular, Bitcoin -the most widely-used cryptocurrency- can be split in half by any AS-level adver… ▽ More Routing attacks remain practically effective in the Internet today as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communication to reach consensus. In particular, Bitcoin -the most widely-used cryptocurrency- can be split in half by any AS-level adversary using BGP hijacking. In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks. We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in locations that are inherently protected against routing attacks; and (ii) on paths that are economically preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients. We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments as small as 6 nodes. △ Less

Submitted 19 August, 2018; originally announced August 2018.

Hijacking Bitcoin: Routing Attacks on Cryptocurrencies

Authors: Maria Apostolaki, Aviv Zohar, Laurent Vanbever

Abstract: As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) c… ▽ More As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate ~50% of the mining power---even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately. △ Less

Submitted 24 March, 2017; v1 submitted 24 May, 2016; originally announced May 2016.

Comments: To appear in the 38th IEEE Symposium on Security and Privacy, May 2017