Atefeh Zareh Chahoki

dblp:349/0751 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0003-2004-7762ORCID · reported

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

Security and privacy · 1 · 1 first-author · 1 since 2021

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
Malware analysis · 77% Blockchain and cryptocurrency security · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Malware analysis › malware detection
cryptojacking detection
0.812024
CryptojackingTrap: An Evasion Resilient Nature-Inspired Algorithm to Detect Cryptojacking Malware · IEEE Trans. Inf. Forensics Secur. 2024
Blockchain and cryptocurrency security
cryptocurrency mining
0.212024
CryptojackingTrap: An Evasion Resilient Nature-Inspired Algorithm to Detect Cryptojacking Malware · IEEE Trans. Inf. Forensics Secur. 2024

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

nature-inspired algorithm · 0.8memory access tracing · 0.8debugger · 0.8
YearPublicationVenuePosition
2024 CryptojackingTrap: An Evasion Resilient Nature-Inspired Algorithm to Detect Cryptojacking Malware
abstract
The high profitability of mining cryptocurrencies mining, a computationally intensive activity, forms a fertile ecosystem that is enticing not only legitimate investors but also cyber attackers who invest their illicit computational resources in this area. Cryptojacking refers to the surreptitious exploitation of a victim’s computing resources to mine cryptocurrencies on behalf of the cyber-criminal. This malicious behavior is observed in executable files and browser executable codes, including JavaScript and Assembly modules, downloaded from websites to victims’ machines and executed. Although there are numerous botnet detection techniques to stop this malicious activity, attackers can circumvent these protections using a variety of techniques. In this paper, CryptojackingTrap is presented as a novel cryptojacking detection solution designed to resist most malware defense methods. The CryptojackingTrap is armed with a debugger and extensible cryptocurrency listeners and its algorithm is based on the execution of cryptocurrency hash functions: an indispensable behavior of all cryptojacking executors. This algorithm becomes aware of this specific hash execution by correlating the memory access traces of suspicious executables with publicly available cryptocurrency P2P network data. With the advantage of this assembly-level investigation and a natureinspired approach to triggering the detection alarm, CryptojackingTrap provides an accurate, evasion-proof technique for detecting cryptojacking. After experimental evaluation, the false negative and false positive rates are zero, and in addition, the false positive rate is mathematically calculated as 10−20. CryptojackingTrap has an open, extensible architecture and is available to the open-source community.
Atefeh Zareh Chahoki, Hamid Reza Shahriari, Marco Roveri
IEEE Trans. Inf. Forensics Secur.1