In timing-sensitive blockchain applications, such as decentralized finance (DeFi), achieving first-come-first-served (FCFS) transaction ordering among decentralized nodes is critical to prevent frontrunning attacks. Themis [CCS'23], a state-of-the-art decentralized FCFS ordering system, has become a key reference point for high-throughput fair ordering systems for real-world blockchain applications, such as rollup chains and decentralized sequencing, and has influenced the design of several subsequent proposals. In this paper, we critically analyze its core system property of practical batch-order fairness and evaluate the frontrunning resistance claim of Themis. We present the Ambush attack, a new frontrunning technique that achieves nearly 100% success against the practical batch-order fair system with only a single malicious node and negligible attack costs. This attack causes a subtle temporary information asymmetry among nodes, which is allowed due to the heavily optimized communication model of the system. A fundamental trade-off we identify is a challenge in balancing security and performance in these systems; namely, enforcing timely dissemination of transaction information among nodes (to mitigate frontrunning) can easily lead to non-negligible network overheads (thus, degrading overall throughput performance). We show that it is yet possible to balance these two by delaying transaction dissemination to a certain tolerable level for frontrunning mitigation while maintaining high throughput. Our evaluation demonstrates that the proposed delayed gossiping mechanism can be seamlessly integrated into existing systems with only minimal changes.

Blockchain networks allow multiple client implementations of the same consensus algorithm by different developers to coexist in the same system. Ensuring correct implementations among these heterogeneous clients is crucial, as even slight semantic discrepancies in their implementations can lead to safety failures. While existing fuzzing frameworks have discovered implementation flaws in blockchain, they suffer from several challenges in testing them with sequences of conflicting blocks, called forks. Existing tools fail to adequately assess the forkhandling processes in blockchain implementations when relying on traditional code coverage feedback, which lacks the granularity needed to navigate the diverse and complex fork-handling scenarios. This paper introduces FORKY, a fork state-aware differential fuzzing framework designed to detect implementation discrepancies within the critical fork-handling process with its novel fork-aware mutation and fork-diversifying feedback mechanisms. We test FORKY on the two most influential blockchain projects: Bitcoin and Ethereum, which are the representatives of the two major blockchain consensus algorithm families, Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms.

The widespread adoption of video conferencing platforms has raised privacy concerns. Recent studies have shown that users express various concerns, such as reluctance toward mandatory camera-on policies, but these findings remain coarse-grained, lacking details on specific features and social relationships. This paper investigates how users perceive privacy with respect to various features in video conferencing platforms. Using the framework of contextual integrity, we analyze information flows across diverse scenarios, such as business meetings and online classes. Our findings reveal nuanced privacy perceptions regarding features that have been discontinued (e.g., attention tracking) or adjusted (e.g., meeting recording), suggesting that the handling of these features could have aligned better with users’ privacy expectations. Additionally, we identify emerging privacy concerns about the pinning and spotlighting features, as users often feel great discomfort when their video is pinned or spotlighted by others in specific contexts. These insights provide a deeper understanding of privacy in video conferencing, highlighting the need for more refined privacy controls and a proactive approach to feature development.

In cellular networks, authorities may need to physically locate user devices to track criminals or illegal equipment. This process involves authorized agents tracing devices by monitoring uplink signals with cellular operator assistance. However, tracking uncooperative uplink signal sources remains challenging, even for operators and authorities. Three key challenges persist for fine-grained localization: i) devices must generate sufficient, consistent uplink traffic over time, ii) target devices may transmit uplink signals at very low power, and iii) signals from cellular repeaters may hinder localization of the target device. While these challenges pose significant practical obstacles to localization, they have been largely overlooked in existing research.

The recently proposed DNS-over-HTTPS (DoH) protocol is becoming increasingly popular in addressing the privacy concerns of exchanging plain-text DNS messages over potentially malicious transit networks (e.g., mass surveillance at ISPs). By employing HTTPS to encrypt DNS communications, DoH traffic inherently becomes indistinguishable from regular encrypted Web traffic, rendering active disruption (e.g., downgrading to the plain-text DNS) by transit networks extremely hard. In this work, we investigate whether DoH traffic is indeed indistinguishable from encrypted Web traffic. To this end, we collect several DoH traffic traces corresponding to 25 resolvers (including major ones, e.g., Google and Cloudftare) by visiting thousands of domains in Alexa's list of top-ranked websites at different geographical locations and environments. Based on the collected traffic, we train a machine learning model to classify HTTPS traffic as either Web or DoH. With our DoH identification model in place, we show that an authoritarian ISP can identify ∼97.4% (∼90%) of the DoH packets correctly in a closed-world (open-world) setting while only misclassifying 1 in 10,000 Web packets. To counter this DoH identification model, we propose an effective mitigation technique, making the identification model impractical for ISPs to filter and consequently downgrade DoH to plain-text DNS communications.

Existing in-network heavy-hitter detection algorithms suffer from several shortcomings. On the one hand, most of the algorithms perform monitoring in intervals and reset the data structures in between; consequently, a notable amount of heavy hitters (HH) spanning across the intervals go undetected. On the other hand, the algorithms consume substantial hardware resources, potentially hindering other data plane functionalities to be integrated on the same device.In this work, we revisit the state-of-the-art in-network approaches in this regard and identify that they fall short in over-coming the aforementioned issues. In particular, we investigate whether it is possible to design a heavy-hitter detection algorithm that provides high accuracy without consuming substantial re-sources, thereby making it feasible to integrate with concurrent applications. To this end, we propose dSketch, a time-decaying algorithm for in-network heavy-hitter detection. Trace-driven simulations and evaluations on the Intel Tofino-based commodity switches show that dSketch significantly improves the detection rate of HHs by 5–10% while being resource- and operation-efficient in contrast to state-of-the-art approaches. Moreover, we show that dSketch can be integrated with standard switch functionalities such as switch. p4 with additional resources spared, offering itself as a compelling solution for switch data plane designers.

With each new DDoS attack potentially becoming a higher intensity attack than the previous ones, current ISP measures of over-provisioning or employing a scrubbing service are becoming ineffective and inefficient. We argue that we need an in-network solution (i.e., entirely in the data plane), to detect DDoS attacks, identify the corresponding traffic and mitigate promptly. In this paper, we propose the first distributed in-network defense architecture, DIDA, to cope with the sophisticated amplified reflection DDoS (AR-DDoS) attacks. We leverage programmable stateful data planes and efficient data structures and show that it is possible to keep track of per-user connections in an automated and distributed manner without overwhelming the network controller. Building on top of this data, DIDA can easily detect if unsolicited attack packets are sent towards a victim within an ISP network. Once an attack is detected, the routers at the network edge automatically block the malicious sources. We prototype DIDA in P4. Our preliminary experiments show that DIDA can detect and mitigate 99.8% of amplification attacks containing 7, 000 different sources while requiring less than 1% of the memory of current programmable switches.

Network adversaries, such as malicious transit autonomous systems (ASes), have been shown to be capable of partitioning the Bitcoin's peer-to-peer network via routing-level attacks; e.g., a network adversary exploits a BGP vulnerability and performs a prefix hijacking attack (viz. Apostolaki et al. [3]). Due to the nature of BGP operation, such a hijacking is globally observable and thus enables immediate detection of the attack and the identification of the perpetrator. In this paper, we present a stealthier attack, which we call the EREBUS attack, that partitions the Bitcoin network without any routing manipulations, which makes the attack undetectable to control-plane and even to data-plane detectors. The novel aspect of EREBUS is that it makes the adversary AS a natural man-in-the-middle network of all the peer connections of one or more targeted Bitcoin nodes by patiently influencing the targeted nodes' peering decision. We show that affecting the peering decision of a Bitcoin node, which is believed to be infeasible after a series of bug patches against the earlier Eclipse attack [29], is possible for the network adversary that can use abundant network address resources (e.g., spoofing millions of IP addresses in many other ASes) reliably for an extended period of time at a negligible cost. The EREBUS attack is readily available for large ASes, such as Tier-1 and large Tier-2 ASes, against the vast majority of 10K public Bitcoin nodes with only about 520 bit/s of attack traffic rate per targeted Bitcoin node and a modest (e.g., 5-6 weeks) attack execution period. The EREBUS attack can be mounted by nation-state adversaries who would be willing to execute sophisticated attack strategies patiently to compromise cryptocurrencies (e.g., control the consensus, take down a cryptocurrency, censor transactions). As the attack exploits the topological advantage of being a network adversary but not the specific vulnerabilities of Bitcoin core, no quick patches seem to be available. We discuss that some naive solutions (e.g., whitelisting, rate-limiting) are ineffective and third-party proxy solutions may worsen the Bitcoin's centralization problem. We provide some suggested modifications to the Bitcoin core and show that they effectively make the EREBUS attack significantly harder; yet, their non-trivial changes to the Bitcoin's network operation (e.g., peering dynamics, propagation delays) should be examined thoroughly before their wide deployment.

Efficient and highly available packet classification is fundamental for various security primitives. In this paper, we evaluate whether the de facto Tuple Space Search (TSS) packet classification algorithm used in popular software networking stacks such as the Open vSwitch is robust against low-rate denial-of-service attacks. We present the Tuple Space Explosion (TSE) attack that exploits the fundamental space/time complexity of the TSS algorithm.

In light of ever-increasing scale and sophistication of modern distributed denial-of-service (DDoS) attacks, recent proposals show that in-network filtering of DDoS traffic at a handful of transit networks can handle volumetric attacks effectively. In this paper, we identify a subtle but important security risk in existing in-network filtering proposals. That is, a transit network may use the in-network filtering services as an excuse for any arbitrary packet drops made for its own benefit. For example, a malicious transit network may execute any filtering rules to discriminate against some of its neighboring networks based on its business preference while claiming that it is for the purpose of DDoS defense. We argue that this is due to the lack of verifiable filtering-i.e., no single party can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, we propose a verifiable in-network filtering system, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighboring networks. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the good candidates to be early adopters of our verifiable filters due to their central locations and flexible software-defined architecture. Our large-scale simulations of two realistic attacks (i.e., DNS amplification, Mirai-based flooding) show that adopting VIF filtering service at only a small number (e.g., 5-25) of large IXPs is sufficient to handle the majority (e.g., up to 80-90%) of DDoS traffic.

Large botnet-based flooding attacks have recently demonstrated unprecedented damage. However, the best-known end-to-end availability guarantees against flooding attacks require costly global-scale coordination among autonomous systems (ASes). A recent proposal called routing around congestion (or RAC) attempts to offer strong end-to-end availability to a selected critical flow by dynamically rerouting it to an uncongested detour path without requiring any inter-AS coordination. This paper presents an in-depth analysis of the (in)feasibility of the RAC defense and points out that its rerouting approach, though intriguing, cannot possibly solve the challenging flooding problem. An effective RAC solution should find an inter-domain detour path for its critical flow with the two following desired properties: (1) it guarantees the establishment of an arbitrary detour path of its choice, and (2) it isolates the established detour path from non-critical flows so that the path is used exclusively for its critical flow. However, we show a fundamental trade-off between the two desired properties, and as a result, only one of them can be achieved but not both. Worse yet, we show that failing to achieve either of the two properties makes the RAC defense not just ineffective but nearly unusable. When the newly established detour path is not isolated, a new adaptive adversary can detect it in real time and immediately congest the path, defeating the goals of the RAC defense. Conversely, when the establishment of an arbitrary detour path is not guaranteed, more than 80% of critical flows we test have only a small number (e.g., three or less) of detour paths that can actually be established and disjoint from each other, which significantly restricts the available options for the reliable RAC operation. The first lesson of this study is that BGP-based rerouting solutions in the current inter-domain infrastructure seem to be impractical due to implicit assumptions (e.g., the invisibility of poisoning messages) that are unattainable in BGP's current practice. Second, we learn that the analysis of protocol specifications alone is insufficient for the feasibility study of any new defense proposal and, thus, additional rigorous security analysis and various network evaluations, including real-world testing, are required. Finally, our findings in this paper agree well with the conclusion of the major literature about end-to-end guarantees; that is, strong end-to-end availability should be a security feature of the Internet routing by design, not an ad hoc feature obtained via exploiting current routing protocols.

The proliferation of surveillance cameras has greatly improved the physical security of many security-critical properties including buildings, stores, and homes. However, recent surveillance camera looping attacks demonstrate new security threats --- adversaries can replay a seemingly benign video feed of a place of interest while trespassing or stealing valuables without getting caught. Unfortunately, such attacks are extremely difficult to detect in real-time due to cost and implementation constraints. In this paper, we propose SurFi to detect these attacks in real-time by utilizing commonly available Wi-Fi signals. In particular, we leverage that channel state information (CSI) from Wi-Fi signals also perceives human activities in the place of interest in addition to surveillance cameras. SurFi processes and correlates the live video feeds and the Wi-Fi CSI signals to detect any mismatches that would identify the presence of the surveillance camera looping attacks. SurFi does not require the deployment of additional infrastructure because Wi-Fi transceivers are easily found in the urban indoor environment. We design and implement the SurFi system and evaluate its effectiveness in detecting surveillance camera looping attacks. Our evaluation demonstrates that SurFi effectively identifies attacks with up to an attack detection accuracy of 98.8% and 0.1% false positive rate.

Bitcoin provides only pseudo-anonymous transactions, which can be exploited to link payers and payees -- defeating the goal of anonymous payments. To thwart such attacks, several Bitcoin mixers have been proposed, with the objective of providing unlinkability between payers and payees. However, existing Bitcoin mixers can be regarded as either insecure or inefficient.

How pervasive is the vulnerability to link-flooding attacks that degrade connectivity of thousands of Internet hosts? Are some geographic regions more vulnerable than others? Do practical countermeasures exist? To answer these questions, we introduce the notion of the routing bottlenecks and show that it is a fundamental property of Internet design; i.e., it is a consequence of route-cost minimizations. We illustrate the pervasiveness of routing bottlenecks in an experiment comprising 15 countries and 15 cities distributed around the world, and measure their susceptibility to scalable link-flooding attacks. We present the key characteristics of routing bottlenecks, including size, link type, and distance from host destinations, and suggest specific structural and operational countermeasures to link-flooding attacks. These countermeasures can be deployed by network operators without needing major Internet redesign.

Large-scale botnet attacks against Internet links using low-rate flows cannot be effectively countered by any of the traditional rate-limiting and flow-filtering mechanisms deployed in individual routers. In this paper, we present a collaborative defense mechanism, called CoDef, which enables routers to distinguish low-rate attack flows from legitimate flows, and protect legitimate traffic during botnet attacks. CoDef enables autonomous domains that are uncontaminated by bots to collaborate during link flooding attacks and reroute their customers' legitimate traffic in response to requests from congested routers. Collaborative defense using multi-path routing favors legitimate traffic while limiting the bandwidth available to attack traffic at a congested link. We present CoDef's design and evaluate its effectiveness by exploring the domain-level path-diversity of the Internet and performing simulations under various traffic conditions.

We present the Crossfire attack -- a powerful attack that degrades and often cuts off network connections to a variety of selected server targets (e.g., servers of an enterprise, a city, a state, or a small country) by flooding only a few network links. In Crossfire, a small set of bots directs low intensity flows to a large number of publicly accessible servers. The concentration of these flows on the small set of carefully chosen links floods these links and effectively disconnects selected target servers from the Internet. The sources of the Crossfire attack are undetectable by any targeted servers, since they no longer receive any messages, and by network routers, since they receive only low-intensity, individual flows that are indistinguishable from legitimate flows. The attack persistence can be extended virtually indefinitely by changing the set of bots, publicly accessible servers, and target links while maintaining the same disconnection targets. We demonstrate the attack feasibility using Internet experiments, show its effects on a variety of chosen targets (e.g., servers of universities, US states, East and West Coasts of the US), and explore several countermeasures.

In today's cellular networks, user equipment (UE) have suffered from low spectral efficiency at cell-edge region due to high interference from adjacent base stations (BSs), which share the same spectral radio resources. In the recently proposed cooperative cellular networks, geographically separated multiple BSs cooperate on transmission in order to improve the UE's signal-to-interference-plus-noise-ratio (SINR) at cell-edge region. The service provider of the system dynamically assigns the cluster of BSs to achieve higher SINR for the UE while optimizing the use of system radio resources. Although it is the service provider that makes the the clustering decision for the UE, the service provider relies on the UE's input to the decision; i.e., the channel states from the adjacent BSs to the UE. In essence, the operation of the cooperative cellular netwokrs heavily relies on the trust in the UEs. In this paper, we propose a new selfish attack against the cooperative cellular networks; an adversary reprograms her UE to report fabricated channel information to cause the service provider to make a decision that benefits the adversary while wasting its system resources. We evaluate the proposed attack in a cooperative cellular network having various performance goals on the simulation-based experiments and show that the adversary can trick the service provider into expending 3.7 times more radio resources for the adversary and, accordingly, the adversary achieves up to 16 dB SINR gain. Finally, we propose a threshold-based countermeasure for the service provider to detect the attack with approximately 90% of accuracy.

In this paper, we propose a cognitive p-persistent carrier sense multiple access (CpCSMA) scheme for spectrum sharing based cognitive radio (CR) networks. In order to guarantee the quality of service of the primary (licensed) network, secondary users are allowed to transmit their data as long as the interference received at a primary receiver is limited by the predetermined level. In the proposed CpCSMA scheme, secondary users adaptively control the transmit power with ON/OFF fashion according to whether the interference constraint at primary receivers is violated or not. We show that the proposed CpCSMA scheme can be mathematically analyzed by adopting the scaling factor to the conventional p-persistent CSMA analysis framework. The numerical examples illustrate that the analytical results match well to the simulation results and the throughput of the proposed CpCSMA scheme approaches to that of the conventional p- persistent CSMA scheme for high input load. Moreover, the proposed CpCSMA scheme is backward compatible and, thus, can easily be implemented with little modification of the conventional CSMA scheme.

In this paper, we propose an adaptive sub-band exclusion (ASE) scheme as a means of intercell interference coordination (ICIC) technique to improve the performance of wireless cellular systems in a decentralized manner. Since ICIC is performed at each mobile station (MS), the proposed ASE scheme can be classified as a decentralized ICIC scheme. Compared to conventional centralized ICIC schemes, our proposed decentralized ASE scheme has the following advantages: cell and user coordinations are easier in the proposed ASE scheme, the complexity of ICIC at base stations (BSs) is reduced, and frequency selectivity characteristics of interference channels to neighboring base stations can be utilized in ICIC. Using the ASE scheme, each MS first finds which neighboring BS suffers most from the interference induced by the MS. Then, it determines an exclusion ratio alpha according to how much intercell interference the MS induces to the selected BS. Finally, it excludes alpha of orthogonal frequency division multiplexing (OFDM) sub-bands under the consideration of the frequency characteristics of the channel between the selected BS and itself. Through extensive computer simulations, we show that the proposed ASE scheme is effective in improving cell edge user performance by eight times at a reasonable degradation of average user performance.