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TF-MAL-apk.catelites

📛 Threat Title

Malware family: Catelites

Category: Catelites First seen: Last updated: Source: ThreatFox Malwares

Description

ThreatFox malware family `apk.catelites`. Printable name: Catelites.

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.catelites VT: VT base fetch failed: HTTPError: 429 Client Error: Too Many Requests for url: https://www.virustotal.com/api/v3/domains/apk.catelites

IOC database

Type
domain
Value
apk.catelites
First seen
Last seen
Attached to this threat
Appears in
1 threat
Description
Extracted from Threat TF-MAL-apk.catelites

Open the full IOC page →

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.catelites

References (1)

Remediations (10)

  • web:arxiv.org

    The goal of malware family classification is to assign malware family labels to known- malware examples to better understand specimen be-havior [2]. Malware authors actively generate new specimens to evade detection and to introduce novel threats, resulting in variations within existing malware families or evolution of new/novel families.

  • web:malpedia.caad.fkie.fraunhofer.de

    Catelites Bot (identified by Avast and SfyLabs in December 2017) is an Android trojan, with ties to CronBot. Once the malicious app is installed, attackers use social engineering tricks and window overlays to get credit card details from the victim. The distribution vector seems to be fake apps from third-party app stores (not Google Play) or via malvertisement. After installation and ...

  • web:sen-chen.github.io

    Understanding these behaviors is crucial for enhancing detection techniques and remediation efforts. Family classification [7] categorizes malware into specific families to facilitate behavioral analysis. Researchers can use the classification results to dig into subsequent malicious be-haviors, analyze attack chains, and devise defensive measures.

  • web:windowsforum.com

    The emergence of RESURGE signals more than just another entry in a long line of malware threats. According to CISA, RESURGE contains advanced persistence features inherited from the SPAWNCHIMERA malware family—a group notorious for its ability to survive system reboots and avoid simplistic remediation .

  • web:www.boozallen.com

    The ability to quickly pin down the family of malware used during a cyber attack can be a massive boon to an incident responder. Not only does family classification provide immediate insights about the characteristics and behaviors of a malware sample, but it is a core part of the triage, remediation , and attribution efforts.

  • web:www.cisa.gov

    This joint advisory is the result of a collaborative research effort by the cybersecurity authorities of five nations: Australia, Canada, New Zealand, the United Kingdom, and the United States.1 It highlights technical approaches to uncovering malicious activity and includes mitigation steps according to best practices.

  • web:www.first.org

    Introduction Overview The Malware Analysis Framework, developed by FIRSTs Malware Analysis Special Interest Group (SIG), is a document aimed to help CSIRTs establish their own malware analysis workflow (s). It provides step-by-step guidance in all workflow phases on how to develop malware analysis capabilities within CSIRTs. This document also lists supporting resources that can further assist ...

  • web:www.nature.com

    This study proposes a hierarchical deep learning framework for Portable Executable (PE) malware detection and family categorization, underpinned by a novel Doubly Regularized Binary Cross-Entropy ...

  • web:www.ncsc.gov.uk

    How to defend organisations against malware or ransomware attacks.

  • web:www.sciencedirect.com

    This, in turn, helps reverse engineers discern the intent of a malware sample and understand the attackers' objectives. This survey classifies and compares the main findings in malware classification and composition analyses. We also discuss malware evasion techniques and feature extraction methods.

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