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WWanHC.dll

MD5 Hash: d810bd9b86f6a898b90da43c775f189e
SHA256 Hash: c4ddb134fa1561329b358b8325f060b5fe81f67bda9481aab2b82e8aff8a3082
File size: 56320 bytes (55 KB.)
Last analysis: 01 Aug, 2018 11:50:01

Analysis MD5: d810bd9b86f6a898b90da43c775f189e

Analysis of the file classifies it as a class A (Safe). The file is safe to use. The trust index of this analysis is 70 % (high).

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

Description

WWanHC.dll is part of WWanHC plugin developed by Microsoft Corporation. This file is an important and required part of the Windows Operating system

Filename: WWanHC.dll (WWanHC plugin)
Threat analysis: Safe
Analysis trust:
70%
Recent activity:
First seen: 28 Sep, 2011
Last seen: 17 Jan, 2018
Last analysis: 01 Aug, 2018
Possible infection: Clean

WWanHC.dll WWanHC plugin

Application: Besturingssysteem Microsoft® Windows®
Developer: Microsoft Corporation
Stability:
75%
File version: 8.1.2.0
File size: 56320 bytes (55 KB.)
Recent activity:
Historic activity:
CRC32 hash: 1829944063
MD5 hash: d810bd9b86f6a898b90da43c775f189e
SHA1 hash: ed77b17f9098a8afb398a6ffa880904e2f3c4eb7
SHA256 hash: c4ddb134fa1561329b358b8325f060b5fe81f67bda9481aab2b82e8aff8a3082
B

Signature verification

Unsigned

This file has no digital signature. The publisher of this file could not be verified.

Publisher Microsoft Corporation
Product Microsoft® Windows® Operating System
Description Wireless WAN Helper Class
Signingdate 0000-00-00 00:00:00
C

File entropy

File entropy match: File code

This file contains (executable) code.

| 0 b.56320 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.

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 %
A

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: 768:MgM5yO0Mo qvtoSvuERIZENmgKybTBv WF2XYg iJeZ8gb1Xfr:MgMf0Mo M1veZAg2YXf1JXgb1XD

No match found


A

Online virus scanners

Detection ration:

0 %
A

VirusShare.com

Not 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

Present 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

C

Statistic analysis

Statistic analysis of the file

Deviates from other files with the same name (imitation)
No certificate
Other files with the same name do not have a certificate as well
This is not a common file
B

Neural network analysis

Analysis: Low risk

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.

6%6 %

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