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Emanuele Vineti

dblp:227/8872 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Web and mobile security · 50% Malware analysis · 25% Network security · 25%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Malware analysis › malware
cryptojacking
0.312018
MineSweeper: An In-depth Look into Drive-by Cryptocurrency Mining and Its Defense · CCS 2018
Network security › intrusion detection and prevention › intrusion detection › malicious traffic detection
cryptomining detection
0.312018
MineSweeper: An In-depth Look into Drive-by Cryptocurrency Mining and Its Defense · CCS 2018
Web and mobile security › web security
malicious website detection
0.312018
MineSweeper: An In-depth Look into Drive-by Cryptocurrency Mining and Its Defense · CCS 2018

Methods — techniques the papers use, named apart from their topics

static analysis of mining code · 0.3blacklisting · 0.3
YearPublicationVenuePosition
2018 MineSweeper: An In-depth Look into Drive-by Cryptocurrency Mining and Its Defense
abstract
A wave of alternative coins that can be effectively mined without specialized hardware, and a surge in cryptocurrencies' market value has led to the development of cryptocurrency mining ( cryptomining ) services, such as Coinhive, which can be easily integrated into websites to monetize the computational power of their visitors. While legitimate website operators are exploring these services as an alternative to advertisements, they have also drawn the attention of cybercriminals: drive-by mining (also known as cryptojacking ) is a new web-based attack, in which an infected website secretly executes JavaScript code and/or a WebAssembly module in the user's browser to mine cryptocurrencies without her consent. In this paper, we perform a comprehensive analysis on Alexa's Top 1 Million websites to shed light on the prevalence and profitability of this attack. We study the websites affected by drive-by mining to understand the techniques being used to evade detection, and the latest web technologies being exploited to efficiently mine cryptocurrency. As a result of our study, which covers 28 Coinhive-like services that are widely being used by drive-by mining websites, we identified 20 active cryptomining campaigns. Motivated by our findings, we investigate possible countermeasures against this type of attack. We discuss how current blacklisting approaches and heuristics based on CPU usage are insufficient, and present MineSweeper, a novel detection technique that is based on the intrinsic characteristics of cryptomining code, and, thus, is resilient to obfuscation. Our approach could be integrated into browsers to warn users about silent cryptomining when visiting websites that do not ask for their consent.
Radhesh Krishnan Konoth, Emanuele Vineti, Veelasha Moonsamy, Martina Lindorfer, Christopher Krügel, Herbert Bos, Giovanni Vigna
CCS2