Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1904.01692

Method of Counteraction in Social Engineering on Information Activity Objectives

Volodymyr Yu. Sokolov·Davyd M. Kurbanmuradov
View original source ↗Read source document ↗Export metadata ↓

Abstract

The article presents a study using attacks such as a fake access point and a phishing page. The previous publications on social engineering have been reviewed, statistics of break-ups are analyzed and directions and mechanism of realization of attacks having elements of social engineering are analyzed. The data from the research in three different places were collected and analyzed and the content statistics were provided. For comparison, three categories of higher education institutions were chosen: technical, humanitarian and mixed profiles. Since the research was conducted in educational institutions during the week, most students in the experiment and graduate students took part in the experiment. For each educational institution, a registration form template was created that mimicked the design of the main pages. Examples of hardware and software implementation of a typical stand for attack, data collection and analysis are given. In order to construct a test stand, widely available components were chosen to show how easy it is to carry out attacks of this kind without significant initial costs and special skills. The article provides statistics on the number of connections, permission to use the address of the e-mail and password, as well as permission to automatically transfer service data to the browser (cookies). The statistics are processed using specially written algorithms. The proposed approaches to solving the problem of socio-technical attacks can be used and implemented for operation on any objects of information activity. As a result of the experiments, it is clear that the awareness of users of even technical specialties is not enough, so one needs to pay particular attention to the development of methods for raising awareness of users and reducing the number of potential attacks on objects of information activity.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Volodymyr Yu. Sokolov, Davyd M. Kurbanmuradov. 2019-04-02. Method of Counteraction in Social Engineering on Information Activity Objectives. https://doi.org/10.28925/2663-4023.2018.1.616

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↗