Digital certificates are crucial for securing Internet communications. Certificates issued by trusted Certificate Authorities (CAs) can be validated by following the chain of trust, consisting of leaf, intermediate, and root certificates. However, such certificate chain structure may not be followed by issuers who are not subject to public monitoring and auditing. This paper takes a first look at certificate chains involving certificates issued by issuers that do not appear in public databases (e.g., major browsers' root stores and CCADB). Utilizing a year's worth of TLS traffic collected from a campus network, we dissect the certificate chain structures and analyze their usage in TLS connections. While we observe positive acts such as the logging of certificates that are issued by issuers outside public databases and anchored to trust roots into Certificate Transparency (CT) logs, we also identify potential misconfigurations by servers where unnecessary certificates are included in the certificate chains, which may lead to validation and connection failures.

Internet protocol specifications, published as Requests for Comments (RFCs) by the IETF organization, are essential to ensuring the interoperability, security, and reliability of the Internet. However, ambiguities in these specifications, particularly logical ambiguities such as inconsistencies and under-specifications, can lead to critical misinterpretations and implementation errors. Unfortunately, such ambiguities remain largely overlooked and challenging to detect with existing tools.In this paper, we present the first systematic study of verified technical errata from Standards Track RFCs over the past 11 years, identifying seven distinct subtypes of logical ambiguities. Building on these insights, we introduce RFCScope, the first scalable framework for detecting logical ambiguities in RFCs. RFCScope employs large language models (LLMs) through a modular pipeline that constructs targeted cross-document context, partitions specifications to preserve semantic integrity, applies bug-type-aware prompts for detection, and filters out false positives using structured reasoning validation.RFCScope uncovers 31 new logical ambiguities spanning all seven subtypes across 14 recent RFCs. Eight of these have been confirmed by RFC authors, with three officially verified as technical errata. Our results demonstrate that RFCScope offers a practical solution for improving the clarity, consistency, and reliability of protocol standards through ambiguity detection.

The SCION Next-Generation Network (NGN) architecture has expanded steadily since 2017, with today 20+ ISPs offering SCION connectivity. In production, IP-to-SCION-to-IP translation by SCION-IP-Gateways (SIGs) is used, such that applications are unaware of the NGN communication. To accelerate innovation and deployments, our aim is to increase the number of native SCION use cases, where the application is fully SCION-aware and optimizes communication across all path choices offered by the network. We set out to achieve two core objectives: (1) facilitating simple native connectivity for applications, and (2) enhancing the scalability of SCION deployment at academic sites.

Transport Layer Security (TLS) is widely recognized as the essential protocol for securing Internet communications. While numerous studies have focused on investigating server certificates used in TLS connections, our study delves into the less explored territory of mutual TLS (mTLS) where both parties need to provide certificates to each other. By utilizing TLS connection logs collected from a large campus network over 23 months, we identify over 2.2 million unique server certificates and over 3.4 million unique client certificates used in over 1.2 billion mutual TLS connections. By jointly analyzing TLS connection data (e.g., port numbers) and certificate data (e.g., issuers for server/client certificates), we quantify the prevalent use of untrusted certificates and uncover potential security concerns resulting from misconfigured certificates, sharing of certificates between servers and clients, and long-expired certificates. Furthermore, we present the first in-depth study on the wide range of information included in CommonName (CN) and Subject Alternative Name (SAN), drawing comparison between client and server certificates, as well as revealing sensitive information.

The DNS HTTPS resource record is a new DNS record type designed for the delivery of configuration information and parameters required to initiate connections to HTTPS network services. In addition, it is a key enabler for TLS Encrypted ClientHello (ECH) by providing the cryptographic keying material needed to encrypt the initial exchange. To understand the adoption of this new DNS HTTPS record, we perform a longitudinal study on the server-side deployment of DNS HTTPS for Tranco top million domains, as well as an analysis of the client-side support for DNS HTTPS through snapshots from major browsers. To the best of our knowledge, our work is the first longitudinal study on DNS HTTPS server deployment, and the first known study on client-side support for DNS HTTPS. Despite the rapidly growing trend of DNS HTTPS adoption, our study highlights challenges and concerns in the deployment by both servers and clients, such as the complexity in properly maintaining HTTPS records and connection failure in browsers when the HTTPS record is not properly configured.

We present a new approach to effectively detect and prioritize malicious beaconing activities in large campus networks by profiling the server activities through aggregated signals across multiple traffic protocols and networks. Key components of our system include a novel time-series analysis algorithm that uncovers hidden periodicity in aggregated signals, and a ranking-based detection pipeline that utilizes self-training and active-learning techniques. We evaluate our detection system on 10 months of real-world traffic collected at two large campus networks, comprising over 75 billion connections. On a daily average, we detect 43% more periodic domains by aggregating signals across multiple networks compared to single-network analysis. Furthermore, our ranking pipeline successfully identifies 1,387 unique malicious domains, out of which 781 (56%) were unknown to the major online threat intelligence platform, VirusTotal, at the time of our detection.

IoT devices are increasingly used in consumer homes. Despite recent works in characterizing IoT TLS usage for a limited number of in-lab devices, there exists a gap in quantitatively understanding TLS behaviors from devices in the wild and server-side certificate management.

Alerts reported by intrusion detection systems (IDSes) are often the starting points for attack campaign discovery and response procedures. However, the sheer number of alerts compared to the number of real attacks, along with the complexity of alert investigations, poses a challenge to achieving effective alert triage with limited computational resources. Automated procedures and human analysts could suffer from the burden of analyzing floods of alerts, and fail to respond to critical alerts promptly.

Analyzing the DNS traffic of Internet hosts has been a successful technique to counter cyberattacks and identify connections to malicious domains. However, recent stealthy attacks hide malicious activities within seemingly legitimate connections to popular web services made by benign programs. Traditional DNS monitoring and signature-based detection techniques are ineffective against such attacks. To tackle this challenge, we present a new program-level approach that can effectively detect such stealthy attacks. Our method builds a fine-grained Program-DNS profile for each benign program that characterizes what should be the “expected” DNS behavior. We find that malware-injected processes have DNS activities which significantly deviate from the Program-DNS profile of the benign program. We then develop six novel features based on the Program-DNS profile, and evaluate the features on a dataset of over 130 million DNS requests collected from a real-world enterprise and 8 million requests from malware-samples executed in a sandbox environment. We compare our detection results with that of previously-proposed features and demonstrate that our new features successfully detect 190 malware-injected processes which fail to be detected by previously-proposed features. Overall, our study demonstrates that fine-grained Program-DNS profiles can provide meaningful and effective features in building detectors for attack campaigns that bypass existing detection systems.

Abstract Recent work has shown that Tor is vulnerable to attacks that manipulate inter-domain routing to compromise user privacy. Proposed solutions such as Counter-RAPTOR [29] attempt to ameliorate this issue by favoring Tor entry relays that have high resilience to these attacks. However, because these defenses bias Tor path selection on the identity of the client, they invariably leak probabilistic information about client identities. In this work, we make the following contributions. First, we identify a novel means to quantify privacy leakage in guard selection algorithms using the metric of Max-Divergence. Max-Divergence ensures that probabilistic privacy loss is within strict bounds while also providing composability over time. Second, we utilize Max-Divergence and multiple notions of entropy to understand privacy loss in the worst-case for Counter-RAPTOR. Our worst-case analysis provides a fresh perspective to the field, as prior work such as Counter-RAPTOR only analyzed average case-privacy loss. Third, we propose modifications to Counter-RAPTOR that incorporate worst-case Max-Divergence in its design. Specifically, we utilize the exponential mechanism (a mechanism for differential privacy) to guarantee a worst-case bound on Max-Divergence/privacy loss. For the quality function used in the exponential mechanism, we show that a Monte-Carlo sampling-based method for stochastic optimization can be used to improve multi-dimensional trade-offs between security, privacy, and performance. Finally, we demonstrate that compared to Counter-RAPTOR, our approach achieves an 83% decrease in Max-Divergence after one guard selection and a 245% increase in worst-case Shannon entropy after 5 guard selections. Notably, experimental evaluations using the Shadow emulator shows that our approach provides these privacy benefits with minimal impact on system performance.

Abstract Many recent proposals for anonymous communication omit from their security analyses a consideration of the effects of time on important system components. In practice, many components of anonymity systems, such as the client location and network structure, exhibit changes and patterns over time. In this paper, we focus on the effect of such temporal dynamics on the security of anonymity networks. We present Tempest, a suite of novel attacks based on (1) client mobility, (2) usage patterns, and (3) changes in the underlying network routing. Using experimental analysis on real-world datasets, we demonstrate that these temporal attacks degrade user privacy across a wide range of anonymity networks, including deployed systems such as Tor; pathselection protocols for Tor such as DeNASA, TAPS, and Counter-RAPTOR; and network-layer anonymity protocols for Internet routing such as Dovetail and HORNET. The degradation is in some cases surprisingly severe. For example, a single host failure or network route change could quickly and with high certainty identify the client’s ISP to a malicious host or ISP. The adversary behind each attack is relatively weak – generally passive and in control of one network location or a small number of hosts. Our findings suggest that designers of anonymity systems should rigorously consider the impact of temporal dynamics when analyzing anonymity.

Tor is vulnerable to network-level adversaries who can observe both ends of the communication to deanonymize users. Recent work has shown that Tor is susceptible to the previously unknown active BGP routing attacks, called RAPTOR attacks, which expose Tor users to more network-level adversaries. In this paper, we aim to mitigate and detect such active routing attacks against Tor. First, we present a new measurement study on the resilience of the Tor network to active BGP prefix attacks. We show that ASes with high Tor bandwidth can be less resilient to attacks than other ASes. Second, we present a new Tor guard relay selection algorithm that incorporates resilience of relays into consideration to proactively mitigate such attacks. We show that the algorithm successfully improves the security for Tor clients by up to 36% on average (up to 166% for certain clients). Finally, we build a live BGP monitoring system that can detect routing anomalies on the Tor network in real time by performing an AS origin check and novel detection analytics. Our monitoring system successfully detects simulated attacks that are modeled after multiple known attack types as well as a real-world hijack attack (performed by us), while having low false positive rates.