TF-MAL-apk.dendroid
📛 Threat Title
Malware family: Dendroid
Description
ThreatFox malware family `apk.dendroid`. Printable name: Dendroid.
Indicators of Compromise (1)
Each indicator is enriched from the IOC database, threat-intel feed corroboration (Threat Hunt) and VirusTotal. Click one to expand.
domain
apk.dendroid
VT: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/apk.dendroid
IOC database
- Type
- domain
- Value
apk.dendroid- First seen
- Last seen
- Attached to this threat
- Appears in
- 1 threat
- Description
- Extracted from Threat TF-MAL-apk.dendroid
Threat Hunt — feed corroboration
Not present in any configured threat-intel feed.
Details From VirusTotal
VirusTotal: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/apk.dendroid
References (1)
- ThreatFox: IOCs for this family ThreatFox Malwares
Remediations (10)
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web:arxiv.org
Abstract—Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection ...
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web:dl.acm.org
This situation has stimulated research in intelligent instruments to automate parts of the malware analysis process. In this paper, we introduce Dendroid , a system based on text mining and information retrieval techniques for this task.
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web:doczz.net
A high level overview of Dendroid's main building blocks and salient applications is provided in Fig. 1. During the modeling phase, all different code structures are extracted from a dataset of provided malware samples. A vector space model is then used to associate a unique feature vector with each malware sample and family .
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web:e-archivo.uc3m.es
This situation has stimulated research in intelligent instruments to automate parts of the malware analysis process. In this paper, we introduce DENDROID , a system based on text mining and information retrieval techniques for this task.
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web:github.com
In this project, we focus on the Android platform and aim to systematize or characterize existing Android malware . Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their ...
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web:malpedia.caad.fkie.fraunhofer.de
Details for the Dendroid malware family including references, samples and yara signatures.
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web:suarez-tangil.networks.imdea.org
A high level overview of Dendroid's main building blocks and salient applications is provided in Fig. 1. During the modeling phase, all di er-ent code structures are extracted from a dataset of provided malware samples. A vector space model is then used to associate a unique feature vector with each malware sample and family .
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web:www.datalearner.com
•We analyze the statistical features of the code structures of Android malware.•We describe Dendroid , a text mining approach to classify and analyze Android malware.•Dendograms derived from hierarchical clustering reveal evolutionary relationships.•Experiments show that Dendroid is an accurate and scalable support tool for analysts.
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web:www.researchgate.net
Dendroid [5] developed a text mining approach for analyzing the program code architecture of Android malware and categorizing it into families based on code architecture similarity.
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web:www.sciencedirect.com
The availability of reuse-oriented development methodologies and automated malware production tools makes exceedingly easy to produce new specimens. As a result, market operators and malware analysts are increasingly overwhelmed by the amount of newly discovered samples that must be analyzed.
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