Mind The Gap: Can Air-Gaps Keep Your Private Data Secure?

Mordechai Guri

Home/Archive/arXiv

arXiv · 2409.04190

Mind The Gap: Can Air-Gaps Keep Your Private Data Secure?

Mordechai Guri

View original source ↗Read source document ↗Export metadata ↓

Abstract

Personal data has become one of the most valuable assets and lucrative targets for attackers in the modern digital world. This includes personal identification information (PII), medical records, legal information, biometric data, and private communications. To protect it from hackers, 'air-gap' measures might be employed. This protective strategy keeps sensitive data in networks entirely isolated (physically and logically) from the Internet. Creating a physical 'air gap' between internal networks and the outside world safeguards sensitive data from theft and online threats. Air-gap networks are relevant today to governmental organizations, healthcare industries, finance sectors, intellectual property and legal firms, and others. In this paper, we dive deep into air-gap security in light of modern cyberattacks and data privacy. Despite this level of protection, publicized incidents from the last decade show that even air-gap networks are not immune to breaches. Motivated and capable adversaries can use sophisticated attack vectors to penetrate the air-gapped networks, leaking sensitive data outward. We focus on different aspects of air gap security. First, we overview cyber incidents that target air-gap networks, including infamous ones such Agent.btz. Second, we introduce the adversarial attack model and different attack vectors attackers may use to compromise air-gap networks. Third, we present the techniques attackers can apply to leak data out of air-gap networks and introduce more innovative ones based on our recent research. Finally, we propose the necessary countermeasures to protect the data, both defensive and preventive.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mordechai Guri. 2024-09-06. Mind The Gap: Can Air-Gaps Keep Your Private Data Secure?. https://arxiv.org/abs/2409.04190

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗

arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗

arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗