Towards Transparent Spam Detection: SentinelCall – A Distributed Ledger Solution for Call Filtering

RESEARCH CREW
20:19 08/07/2024

The proliferation of connectivity through modern telecommunications has led to increased unwanted and disruptive calls. Such communications negatively impact user experience and trust in platforms. Currently, call filtering relies on centralized architectures that aggregate vast troves of sensitive user data within single entities, compromising privacy and ownership. Users have limited visibility into how inputs inform labeling, challenging autonomy and oversight. We present the SentinelCall Platform, a novel blockchain-powered decentralized framework to mitigate unsolicited calls. It establishes a permissionless blockchain tailored for immutable storage of call logs and community rules. A Proof-of-Spam consensus facilitates transparent flagging of suspicious numbers through democratic participation. An initial prototype demonstrates authenticating calls while preserving anonymity. By removing centralized data flow and governance models, our solution aims to restore transparency, autonomy and trust. The modular framework integrates applications and consensus optimization. Evaluations indicate ability to handle throughput loads. This decentralized alternative enhances user protection against disruptive communications through distributed, open solutions with implications for blockchain application across data sovereignty domains.

TIN LIÊN QUAN
Malware threatens cybersecurity by enabling data theft, unauthorized access, and extortion. Traditional malware detection systems (MDS) struggle with the increasing volume and complexity of malware. While machine learning (ML) and deep learning (DL) offer promising solutions, they remain vulnerable to adversarial attacks that evade detection. Recent research focuses on developing...