5f076e8fcf6ed495_Filesample.dll

MD5 Hash: 009b3e698de7eee86110877722c76369
SHA256 Hash: 5f076e8fcf6ed495931b8eb2ff92c9e7958eb10b7bc57b44b4d689514532786c
File size: 189340 bytes (185 KB.)
Last analysis: 06 Nov, 2019 00:11:53

Analysis MD5: 009b3e698de7eee86110877722c76369

Analysis of the file classifies it as a class F (Malicious). The file is malicious, do not use it. The trust index of this analysis is 100 % (certain).

A
B
C
D+
D
D-
E+
E
E-
F

Description

Filename: 5f076e8fcf6ed495_Filesample.dll (Trojan:Win32/Wisdomeyes.FM)
Threat analysis: Malicious
Analysis trust:
100%
Recent activity:
First seen: 06 Mar, 2018
Last seen: 02 Apr, 2018
Last analysis: 06 Nov, 2019
Possible infection: Trojan:Win32/Wisdomeyes.FM

5f076e8fcf6ed495_Filesample.dll Trojan:Win32/Wisdomeyes.FM

Application: Trojan:Win32/Wisdomeyes.FM
Developer: Unknown
Stability:
75%
File version: 0.0.0.0
File size: 189340 bytes (185 KB.)
Recent activity:
Historic activity:
CRC32 hash: 3c59136c
MD5 hash: 009b3e698de7eee86110877722c76369
SHA1 hash: ef569b68258923773724480101f991bb8fea8a2f
SHA256 hash: 5f076e8fcf6ed495931b8eb2ff92c9e7958eb10b7bc57b44b4d689514532786c
D+

File entropy

File entropy match: Encrypted

Parts of this file are encrypted. The reasons might be benign but it makes the analysis more difficult.

| 0 b.189340 b. |
Plain Data Text Code Compressed Encrypted Random

File signature

Dynamic Link Library

Dynamic-link library, or DLL, is Microsoft's implementation of the shared library concept in the Microsoft Windows and OS/2 operating systems.

File header First 32 bytes of this file

4D 5A 90 00 03 00 00 00 04 00 00 00 FF FF 00 00 B8 00 00 00 00 00 00 00 40 00 00 00 00 00 00 00

The determination of a file type is done with a signature or magic-numbers. Files are identified using by comparing the first set of bytes in the file header. Using this method type of files are recognised no matter the extension used. This information is useful to for example recognise executable files cloaked as images or movies.


A

Malicious code scan

No malicious code found

Agics makes een analysis of the source code of the file. We look for comparisons with known malicious source code. This is a good way to detect new malicious files which are in fact variations of existing, and known malicious files.

Scan results:

0 %
E+

Fuzzy hash a.k.a. Context Triggered Piecewise Hashing

SSDEEP

Context Triggered Piecewise Hashing, also called Fuzzy Hashing, can match inputs that have homologies. Such inputs have sequences of identical bytes in the same order, although bytes in between these sequences may be different in both content and length. Comparing a fuzzyhash is a good way to detect morphing malware. Malware which include random code in every copy to change its properties. Agics uses ssdeep to make create a fuzzyhash.

SSDEEP: 3072:HxIBtQnE7OhssdWJ5jy392aCmCbBq/XIbeYql7ccXgqKVMuxCbE451y:Mqvhssdu5jyYaCmCQ/XI8ycXg3V5CIh

Match found

F
SHA256 :b6c800efae36575a01c6b5cb918b3c0f8659954e537c40ccecdab3b5498f5153
SSDEEP :3072:HGIBtQnE7OhssdWJ5jy392aCmCbBqT/6BKHt/Sbrs4YXTbwYHGnJ/mZNtg9N8OV3:Hqvhssdu5jyYaCmCQT6Bgwr+DsYm4/yl

58% Match58% Match
Show filereport

E-

Online virus scanners

Detection ration:

84 %
E-

VirusShare.com

Available on virusshare.com

VirusShare.com is a repository of malware samples to provide security researchers, incident responders, forensic analysts, and the morbidly curious access to samples of live malicious code. Presence of the sample on this site indicates that the file is (Once considered) being malicious.
Website: virusshare.com
B

National Software Reference Library

Not on the nsrl list

The NSRL contains a collection of digital signatures of known, traceable software applications. There are application hash values in the hash set which may be considered malicious, i.e. steganography tools and hacking scripts.
Website: www.nsrl.nist.gov

F

Behaviour

Sandbox behaviour analysis:

The file is executed in a safe environment to track its behaviour. The behaviour analysis can help with detecting new malware which is not recognized by virusscanners yet. However it has a high chance on a false-positive, especially with installers, uninstallers and virusscanners.

Detects Avast Antivirus through the presence of a library

Creates and runs a batch file to remove the original binary

The file dropped a dangerous file

Detects the presence of Wine emulator

Yara hit: Advapi_Hash_API - Looks for advapi API functions

Repeatedly searches for a not-found process.

Yara hit: CRC32_poly_Constant - Look for CRC32 [poly]

Checks for the presence of known windows from debuggers and forensic tools

Creates a suspicious process

One or more processes crashed

One or more potentially interesting buffers were extracted, these generally contain injected code, configuration data, etc.

Executed a process and injected code into it, probably while unpacking

Checks amount of memory in system, this can be used to detect virtual machines that have a low amount of memory available

Allocates read-write-execute memory (usually to unpack itself)

Performs some HTTP requests

Connects to an IP address that is no longer responding to requests (legitimate services will remain up-and-running usually)

Network activity

Connects to safe servers

Host not responding
Host Port
51.143.22.23980Dead

TCP
Host Port
52.109.88.3449176

52.109.88.3849174

DNS
Domain Result Record type
watson.microsoft.com51.143.22.239A

Dropped files


F

Import hashing

Imphash 65e9607e6f28a7852bb41a6e2e439a92

Fingerprinting files can be done in various way. One way is to make a hash of the PE Imports. PE Imports are relative unique and this is a great way to find new variants of existing malware. The chance of false-positives is relative high. The resulting hash is often called an imphash.

98% Match98% Match
A

Statistic analysis

Statistic analysis of the file

Similar to other files with the same name
File version is 0.0.0.0
The certificate can not be determined
This is a very common file
F

Neural network analysis

Analysis: Malicious

A neural network is a type of artificial intelligence. It recognized patterns nog clear for a human viewer. Our neural network is surprisingly accurate in recognizing dangerous files. The value below is the predicted chance the file is malicious.

96%96 %

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