arXiv subjects

Nikita Borisov

Publications and source records attributed to Nikita Borisov.

At least 19 recordsLinked to original sources

arXiv2026

Prezta: Provable Remote Execution of Zero-Trust Authorization using SNARKs

Modernizing the security of operational technology systems that control critical infrastructure has become a pressing challenge. Because edge devices have limited capabilities, modernization has relied on application gateways that interface with identity management systems and enforce access policies. These gateways are powerful enough to perform complex authorization decisions and support zero-trust architectures, but they create major deployment and management burdens: they must be collocated with remote, distributed edge devices, kept up to date with security patches, and managed with minimal downtime. We propose Provable Remote Execution of Zero-Trust Authorization (Prezta), an architecture that eliminates these gateways by evaluating policies within a zero-knowledge virtual machine (zkVM) running on the client. The zkVM produces a succinct proof of authorization that edge devices can verify efficiently, extending the zero-trust security envelope to the edge. Policies and identity management schemes can evolve without updating edge devices. To demonstrate the feasibility of Prezta, we implement a prototype built using the RISC Zero zkVM that supports XACML 3.0 policies and JWT identity claims. While zkVMs introduce substantial proof overhead, we mitigate this overhead by compiling policies to Rust code and precompiling regular expressions. Combined with optimized signature verification and JWT parsing, these measures reduce prover time by more than an order of magnitude. Our compiler correctly implements 83\% of the XACML 3.0 conformance suite, with proof generation completing in tens of seconds on a desktop. Verification, by contrast, takes only tens of milliseconds, which is fast enough for resource-constrained edge devices.

cs.CR↗

arXiv2025

Monomial stability of Frobenius images

We study representation stability in the sense of Church, Ellenberg, and Farb \cite{FI-module} through the lens of symmetric function theory and the different symmetric function bases. We show that a sequence, $(F_n)_n$, where $F_n$ is a homogeneous symmetric function of degree $n$, has stabilizing Schur coefficients if and only if it has stabilizing monomial coefficients. More generally, we develop a framework for checking when stabilizing coefficients transfer from one symmetric function basis to another. We also see how one may compute representation stable ranges from the monomial expansions of the $F_n$.\parspace As applications, we reprove and refine the representation stability of diagonal coinvariant algebras, $DR_n$. We also observe new representation stability phenomena of the Garsia-Haiman modules. This establishes certain stability properties of the modified Macdonald polynomials, $\tilde{H}_{μ^{(n)}}[X;q,t]$ and the modified $q,t$-Kostka numbers, $\tilde{K}_{μ^{(n)},ν[n]}(q,t)$, for arbitrary sequences of partitions with $μ^{(n)}\vdash n$ and $μ^{(n)}\subseteq μ^{(n+1)}$.

math.CO↗

arXiv2024

$\mathbb{A}^1$-Brouwer degrees in Macaulay2

We describe the Macaulay2 package "A1BrouwerDegrees" for computing local and global $\mathbb{A}^1$-Brouwer degrees and studying symmetric bilinear forms over the complex numbers, the real numbers, the rational numbers, and finite fields of characteristic not equal to 2.

math.AG↗

arXiv2022

DiProber: Using Dual Probing to Estimate Tor Relay Capacities in Underloaded Networks

Tor is the most popular anonymous communication network. It has millions of daily users seeking privacy while browsing the internet. It has thousands of relays to route and anonymize the source and destinations of the users packets. To create a path, Tor authorities generate a probability distribution over relays based on the estimates of the capacities of the relays. An incoming user will then sample this probability distribution and choose three relays for their paths. The estimates are based on the bandwidths of observation probes the authority assigns to each relay in the network. Thus, in order to achieve better load balancing between users, accurate estimates are necessary. Unfortunately, the currently implemented estimation algorithm generate inaccurate estimates causing the network to be under utilized and its capacities unfairly distributed between the users paths. We propose DiProber, a new relay capacity estimation algorithm. The algorithm proposes a new measurement scheme in Tor consisting of two probes per relay and uses maximum likelihood to estimate their capacities. We show that the new technique works better in the case of under-utilized networks where users tend to have very low demand on the Tor network.

cs.NI↗

arXiv2020

Detecting AI Trojans Using Meta Neural Analysis

In machine learning Trojan attacks, an adversary trains a corrupted model that obtains good performance on normal data but behaves maliciously on data samples with certain trigger patterns. Several approaches have been proposed to detect such attacks, but they make undesirable assumptions about the attack strategies or require direct access to the trained models, which restricts their utility in practice. This paper addresses these challenges by introducing a Meta Neural Trojan Detection (MNTD) pipeline that does not make assumptions on the attack strategies and only needs black-box access to models. The strategy is to train a meta-classifier that predicts whether a given target model is Trojaned. To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution. We then dynamically optimize a query set together with the meta-classifier to distinguish between Trojaned and benign models. We evaluate MNTD with experiments on vision, speech, tabular data and natural language text datasets, and against different Trojan attacks such as data poisoning attack, model manipulation attack, and latent attack. We show that MNTD achieves 97% detection AUC score and significantly outperforms existing detection approaches. In addition, MNTD generalizes well and achieves high detection performance against unforeseen attacks. We also propose a robust MNTD pipeline which achieves 90% detection AUC even when the attacker aims to evade the detection with full knowledge of the system.

cs.AI↗

arXiv2020

Assessing the Privacy Benefits of Domain Name Encryption

As Internet users have become more savvy about the potential for their Internet communication to be observed, the use of network traffic encryption technologies (e.g., HTTPS/TLS) is on the rise. However, even when encryption is enabled, users leak information about the domains they visit via DNS queries and via the Server Name Indication (SNI) extension of TLS. Two recent proposals to ameliorate this issue are DNS over HTTPS/TLS (DoH/DoT) and Encrypted SNI (ESNI). In this paper we aim to assess the privacy benefits of these proposals by considering the relationship between hostnames and IP addresses, the latter of which are still exposed. We perform DNS queries from nine vantage points around the globe to characterize this relationship. We quantify the privacy gain offered by ESNI for different hosting and CDN providers using two different metrics, the k-anonymity degree due to co-hosting and the dynamics of IP address changes. We find that 20% of the domains studied will not gain any privacy benefit since they have a one-to-one mapping between their hostname and IP address. On the other hand, 30% will gain a significant privacy benefit with a k value greater than 100, since these domains are co-hosted with more than 100 other domains. Domains whose visitors' privacy will meaningfully improve are far less popular, while for popular domains the benefit is not significant. Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7% of them change their hosting IP addresses on a daily basis. We conclude by discussing potential approaches for website owners and hosting/CDN providers for maximizing the privacy benefits of ESNI.

cs.CR↗

arXiv2018

Differentially Private Data Generative Models

Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. However, the large-scale data collections required for deep learning often contain sensitive information, therefore raising many privacy concerns. Prior research has shown several successful attacks in inferring sensitive training data information, such as model inversion, membership inference, and generative adversarial networks (GAN) based leakage attacks against collaborative deep learning. In this paper, to enable learning efficiency as well as to generate data with privacy guarantees and high utility, we propose a differentially private autoencoder-based generative model (DP-AuGM) and a differentially private variational autoencoder-based generative model (DP-VaeGM). We evaluate the robustness of two proposed models. We show that DP-AuGM can effectively defend against the model inversion, membership inference, and GAN-based attacks. We also show that DP-VaeGM is robust against the membership inference attack. We conjecture that the key to defend against the model inversion and GAN-based attacks is not due to differential privacy but the perturbation of training data. Finally, we demonstrate that both DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning applications, such as machine learning as a service and federated learning, which are otherwise threatened by the membership inference attack and the GAN-based attack, respectively.

cs.CR↗

arXiv2016

Smartphone Fingerprinting Via Motion Sensors: Analyzing Feasibility at Large-Scale and Studying Real Usage Patterns

Advertisers are increasingly turning to fingerprinting techniques to track users across the web. As web browsing activity shifts to mobile platforms, traditional browser fingerprinting techniques become less effective; however, device fingerprinting using built-in sensors offers a new avenue for attack. We study the feasibility of using motion sensors to perform device fingerprinting at scale, and explore countermeasures that can be used to protect privacy. We perform a large-scale user study to demonstrate that motion sensor fingerprinting is effective with even 500 users. We also develop a model to estimate prediction accuracy for larger user populations; our model provides a conservative estimate of at least 12% classification accuracy with 100000 users. We then investigate the use of motion sensors on the web and find, distressingly, that many sites send motion sensor data to servers for storage and analysis, paving the way to potential fingerprinting. Finally, we consider the problem of developing fingerprinting countermeasures; we evaluate a previously proposed obfuscation technique and a newly developed quantization technique via a user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.

cs.CY↗

arXiv2015

Exploring Ways To Mitigate Sensor-Based Smartphone Fingerprinting

Modern smartphones contain motion sensors, such as accelerometers and gyroscopes. These sensors have many useful applications; however, they can also be used to uniquely identify a phone by measuring anomalies in the signals, which are a result from manufacturing imperfections. Such measurements can be conducted surreptitiously in the browser and can be used to track users across applications, websites, and visits. We analyze techniques to mitigate such device fingerprinting either by calibrating the sensors to eliminate the signal anomalies, or by adding noise that obfuscates the anomalies. To do this, we first develop a highly accurate fingerprinting mechanism that combines multiple motion sensors and makes use of (inaudible) audio stimulation to improve detection. We then collect measurements from a large collection of smartphones and evaluate the impact of calibration and obfuscation techniques on the classifier accuracy.

cs.CR↗

arXiv2015

Defending Tor from Network Adversaries: A Case Study of Network Path Prediction

The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems and Internet exchanges, which can observe different overlay hops belonging to the same circuit. We aim to determine whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by running traceroutes from Tor relays to destinations around the Internet. We use the data to evaluate the accuracy of the autonomous systems and Internet exchanges that are predicted to appear on the path using state-of-the-art path inference techniques; we also consider the impact that prediction errors have on Tor security, and whether it is possible to produce a useful overestimate that does not miss important threats. Finally, we evaluate the possibility of using these predictions to actively avoid AS and IX adversaries and the challenges this creates for the design of Tor.

cs.CR↗

arXiv2014

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy.

cs.CR↗

arXiv2012

Securing Tor Tunnels under the Selective-DoS Attack

Anonymous communication systems are subject to selective denial-of-service (DoS) attacks. Selective DoS attacks lower anonymity as they force paths to be rebuilt multiple times to ensure delivery which increases the opportunity for more attack. In this paper we present a detection algorithm that filters out compromised communication channels for one of the most widely used anonymity networks, Tor. Our detection algorithm uses two levels of probing to filter out potentially compromised tunnels. We perform probabilistic analysis and extensive simulation to show the robustness of our detection algorithm. We also analyze the overhead of our detection algorithm and show that we can achieve satisfactory security guarantee for reasonable communication overhead (5% of the total available Tor bandwidth in the worst case). Real world experiments reveal that our detection algorithm provides good defense against selective DoS attack.

cs.CR↗

arXiv2012

IP over Voice-over-IP for censorship circumvention

Open communication over the Internet poses a serious threat to countries with repressive regimes, leading them to develop and deploy network-based censorship mechanisms within their networks. Existing censorship circumvention systems face different difficulties in providing unobservable communication with their clients; this limits their availability and poses threats to their users. To provide the required unobservability, several recent circumvention systems suggest modifying Internet routers running outside the censored region to intercept and redirect packets to censored destinations. However, these approaches require modifications to ISP networks, and hence requires cooperation from ISP operators and/or network equipment vendors, presenting a substantial deployment challenge. In this report we propose a deployable and unobservable censorship-resistant infrastructure, called FreeWave. FreeWave works by modulating a client's Internet connections into acoustic signals that are carried over VoIP connections. Such VoIP connections are targeted to a server, FreeWave server, that extracts the tunneled traffic of clients and proxies them to the uncensored Internet. The use of actual VoIP connections, as opposed to traffic morphing, allows FreeWave to relay its VoIP connections through oblivious VoIP nodes, hence keeping itself unblockable from censors that perform IP address blocking. Also, the use of end-to-end encryption prevents censors from identifying FreeWave's VoIP connections using packet content filtering technologies, like deep-packet inspection. We prototype the designed FreeWave system over the popular VoIP system of Skype. We show that FreeWave is able to reliably achieve communication bandwidths that are sufficient for web browsing, even when clients are far distanced from the FreeWave server.

cs.CR↗

arXiv2012

SWEET: Serving the Web by Exploiting Email Tunnels

Open communication over the Internet poses a serious threat to countries with repressive regimes, leading them to develop and deploy censorship mechanisms within their networks. Unfortunately, existing censorship circumvention systems do not provide high availability guarantees to their users, as censors can identify, hence disrupt, the traffic belonging to these systems using today's advanced censorship technologies. In this paper we propose SWEET, a highly available censorship-resistant infrastructure. SWEET works by encapsulating a censored user's traffic to a proxy server inside email messages that are carried over by public email service providers, like Gmail and Yahoo Mail. As the operation of SWEET is not bound to specific email providers we argue that a censor will need to block all email communications in order to disrupt SWEET, which is infeasible as email constitutes an important part of today's Internet. Through experiments with a prototype of our system we find that SWEET's performance is sufficient for web traffic. In particular, regular websites are downloaded within couple of seconds.

cs.CR↗

arXiv2012

Pisces: Anonymous Communication Using Social Networks

The architectures of deployed anonymity systems such as Tor suffer from two key problems that limit user's trust in these systems. First, paths for anonymous communication are built without considering trust relationships between users and relays in the system. Second, the network architecture relies on a set of centralized servers. In this paper, we propose Pisces, a decentralized protocol for anonymous communications that leverages users' social links to build circuits for onion routing. We argue that such an approach greatly improves the system's resilience to attackers. A fundamental challenge in this setting is the design of a secure process to discover peers for use in a user's circuit. All existing solutions for secure peer discovery leverage structured topologies and cannot be applied to unstructured social network topologies. In Pisces, we discover peers by using random walks in the social network graph with a bias away from highly connected nodes to prevent a few nodes from dominating the circuit creation process. To secure the random walks, we leverage the reciprocal neighbor policy: if malicious nodes try to exclude honest nodes during peer discovery so as to improve the chance of being selected, then honest nodes can use a tit-for-tat approach and reciprocally exclude the malicious nodes from their routing tables. We describe a fully decentralized protocol for enforcing this policy, and use it to build the Pisces anonymity system. Using theoretical modeling and experiments on real-world social network topologies, we show that (a) the reciprocal neighbor policy mitigates active attacks that an adversary can perform, (b) our decentralized protocol to enforce this policy is secure and has low overhead, and (c) the overall anonymity provided by our system significantly outperforms existing approaches.

cs.CR↗

arXiv2012

PIRATTE: Proxy-based Immediate Revocation of ATTribute-based Encryption

Access control to data in traditional enterprises is typically enforced through reference monitors. However, as more and more enterprise data is outsourced, trusting third party storage servers is getting challenging. As a result, cryptography, specifically Attribute-based encryption (ABE) is getting popular for its expressiveness. The challenge of ABE is revocation. To address this challenge, we propose PIRATTE, an architecture that supports fine-grained access control policies and dynamic group membership. PIRATTE is built using attribute-based encryption; a key and novel feature of our architecture, however, is that it is possible to remove access from a user without issuing new keys to other users or re-encrypting existing ciphertexts. We achieve this by introducing a proxy that participates in the decryption process and enforces revocation constraints. The proxy is minimally trusted and cannot decrypt ciphertexts or provide access to previously revoked users. We describe the PIRATTE construction and provide a security analysis along with performance evaluation.We also describe an architecture for online social network that can use PIRATTE, and prototype application of PIRATTE on Facebook.

cs.CR↗

arXiv2012

Octopus: A Secure and Anonymous DHT Lookup

Distributed Hash Table (DHT) lookup is a core technique in structured peer-to-peer (P2P) networks. Its decentralized nature introduces security and privacy vulnerabilities for applications built on top of them; we thus set out to design a lookup mechanism achieving both security and anonymity, heretofore an open problem. We present Octopus, a novel DHT lookup which provides strong guarantees for both security and anonymity. Octopus uses attacker identification mechanisms to discover and remove malicious nodes, severely limiting an adversary's ability to carry out active attacks, and splits lookup queries over separate anonymous paths and introduces dummy queries to achieve high levels of anonymity. We analyze the security of Octopus by developing an event-based simulator to show that the attacker discovery mechanisms can rapidly identify malicious nodes with low error rate. We calculate the anonymity of Octopus using probabilistic modeling and show that Octopus can achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab with 207 nodes and show that Octopus has reasonable lookup latency and manageable communication overhead.

cs.CR↗

arXiv2012

Multi-Flow Attacks Against Network Flow Watermarks: Analysis and Countermeasures

In this paper, we analyze several recent schemes for watermarking network flows that are based on splitting the flow into timing intervals. We show that this approach creates time-dependent correlations that enable an attack that combines multiple watermarked flows. Such an attack can easily be mounted in nearly all applications of network flow watermarking, both in anonymous communication and stepping stone detection. The attack can be used to detect the presence of a watermark, recover the secret parameters, and remove the watermark from a flow. The attack can be effective even if different flows are marked with different values of a watermark. We analyze the efficacy of our attack using a probabilistic model and a Markov-Modulated Poisson Process (MMPP) model of interactive traffic. We also implement our attack and test it using both synthetic and real-world traces, showing that our attack is effective with as few as 10 watermarked flows. Finally, we propose possible countermeasures to defeat the multi-flow attack.

cs.CR↗