Automagically extract forensic (https://www.kitploit.com/search/label/Forensic) timeline from volatile memory dumps. Requirements Python 3 Volatility mactime (from SleuthKit) (Developed and tested on Debian (https://www.kitploit.com/search/label/Debian) 9.6 with Volatility 2.6-1 and sleuthkit 4.4.0-5)
How it works AutoTimeline automates this workflow (https://www.andreafortuna.org/dfir/forensic-timeline-creation-my-own-workflow/): Identify correct volatility (https://www.kitploit.com/search/label/Volatility) profile for the memory image. Runs the timeliner plugin (https://www.kitploit.com/search/label/Plugin) against volatile memory dump using volatility. Runs the mftparser volatility plugin, in order to extract $MFT from memory and generate a bodyfile. Runs the shellbags volatility plugin in order to generate a bodyfile of the user activity. (suggested by Matteo Cantoni (https://github.com/mcantoni)). Merges the timeliner, mftparser and shellbags output files into a single bodyfile. Sorts and filters the bodyfile using mactime and exports data as CSV. Installation Simply clone the GitHub repository: git clone https://github.com/andreafortuna/autotimeliner.git Usage Memory dump file -t TIMEFRAME, --timeframe TIMEFRAME Timeframe used to filter the timeline (YYYY-MM-DD ..YYYY-MM-DD) -p CUSTOMPROFILE, --customprofile CUSTOMPROFILE Jump image identification and use a custom memory profile">autotimeline.py [-h] -f IMAGEFILE [-t TIMEFRAME] [-p CUSTOMPROFILE]

optional arguments:
-h, --help show this help message and exit
-f IMAGEFILE, --imagefile IMAGEFILE
Memory dump file
-t TIMEFRAME, --timeframe TIMEFRAME
Timeframe used to filter the timeline (YYYY-MM-DD
..YYYY-MM-DD)
-p CUSTOMPROFILE, --customprofile CUSTOMPROFILE
Jump image identification and use a custom memory
profile
Examples Extract timeline from TargetServerMemory.raw, limited to a timeframe from 2018-10-17 to 2018-10-21: ./autotimeline.py -f TargetServerMemory.raw -t 2018-10-17..2018-10-21 Extract timeline from all images in current directory, limited to a timeframe from 2018-10-17 to 2018-10-21: ./autotimeline.py -f ./*.raw -t 2018-10-17..2018-10-21 Extract timeline from TargetServerMemory.raw, using a custom memory profile: ./autotimeline.py -f TargetServerMemory.raw -p Win2008R2SP1x64 All timelines will be saved as $ORIGINALFILENAME-timeline.csv. TODO Better image identification Better error trapping

Download Autotimeliner (https://github.com/andreafortuna/autotimeliner)

Hacking on Medium
5 Incredible Hacks that you still don’t know for Building in Minecraft!!!

It will be more interesting and easier to discover and learn those tips if you >>CLICK HERE<<

Continue reading on Medium »

Hacking on Medium
ハニーポットに来た600万件の攻撃を紐解く

https://cdn-images-1.medium.com/max/2502/1*goTjddPqW1j4p29LqkxHKQ.png
前書き

Continue reading on Medium »

Hacking on Medium
Hacking Technical Interviewing

https://cdn-images-1.medium.com/max/1352/1*qOZH5br2hVHS90GniqzwaA.png
So you’re looking for a good developer, maybe even a great one to join your team. But what is a great developer?

Continue reading on Melio’s R&D blog »

Hacking on Medium
On My Way To 100K followers

https://cdn-images-1.medium.com/max/777/1*E_K7wzEOAnF_auSwH9uNjg.png
You can go from 1.1K to 99.9K followers really quick (you won’t make money though)!

Continue reading on Medium »

Hacking on Medium
QUAXAR is coming soon.

https://cdn-images-1.medium.com/max/905/1*d7dApNyzCtWOjHKYa0PZqg.jpeg
Our upgraded CTI solution fully equipped with new features will soon be unveiled.

Continue reading on S2W BLOG »

Hacking on Medium
10 BEST Ethical Hacking Books [2022 Edition]

https://cdn-images-1.medium.com/max/600/0*igxa_rJHlE2QNl_L.jpg
Ethical hacking is the process of detecting flaws in computer systems/networks and devising countermeasures to protect such flaws. Before…

Continue reading on Medium »

Hacking on Medium
Identity and Access Management (IAM): Simplified

https://cdn-images-1.medium.com/max/710/0*vTAUezdEDDb5OqCu.jpg
Cloud security is crucial in today’s era as we see the increasing rate of cybercrime. Businesses are rapidly migrating to cloud to prevent…

Continue reading on Medium »

Hacking on Medium
Why Zero Trust is the Future of Corporate Cybersecurity

https://cdn-images-1.medium.com/max/1000/0*TLyvYxsmGJ45lewz
In a world driven by digital business, enterprise security needs to be continuously monitored and improved to keep up with evolving…

Continue reading on Netacea »

KitPloit - PenTest Tools!
Autotimeliner - Automagically Extract Forensic Timeline From Volatile Memory Dump

http://2.bp.blogspot.com/-W2Z-SKv-dq4/Yd0eVCla67I/AAAAAAAA8jE/S77Wdb-UiW0Fb_NqRh5F6q-rgMTxAVwDQCK4BGAYYCw/w640-h376/autotimeliner_1-771906.gif
Automagically extract forensic timeline from volatile memory dumps.

Requirements

* Python 3
* Volatility
* mactime (from SleuthKit)

(Developed and tested on Debian 9.6 with Volatility 2.6-1 and sleuthkit 4.4.0-5)
How it works

AutoTimeline automates this workflow:

* Identify correct volatility profile for the memory image.
* Runs the timeliner plugin against volatile memory dump using volatility.
* Runs the mftparser volatility plugin, in order to extract $MFT from memory and generate a bodyfile.
* Runs the shellbags volatility plugin in order to generate a bodyfile of the user activity. (suggested by Matteo Cantoni).
* Merges the timeliner, mftparser and shellbags output files into a single bodyfile.
* Sorts and filters the bodyfile using mactime and exports data as CSV.

Installation

Simply clone the GitHub repository:

git clone https://github.com/andreafortuna/autotimeliner.git

Usage

Memory dump file -t TIMEFRAME, --timeframe TIMEFRAME Timeframe used to filter the timeline (YYYY-MM-DD ..YYYY-MM-DD) -p CUSTOMPROFILE, --customprofile CUSTOMPROFILE Jump image identification and use a custom memory profile">autotimeline.py [-h] -f IMAGEFILE [-t TIMEFRAME] [-p CUSTOMPROFILE]

optional arguments:
-h, --help show this help message and exit
-f IMAGEFILE, --imagefile IMAGEFILE
Memory dump file
-t TIMEFRAME, --timeframe TIMEFRAME
Timeframe used to filter the timeline (YYYY-MM-DD
..YYYY-MM-DD)
-p CUSTOMPROFILE, --customprofile CUSTOMPROFILE
Jump image identification and use a custom memory
profile


Examples

Extract timeline from TargetServerMemory.raw, limited to a timeframe from 2018-10-17 to 2018-10-21:

./autotimeline.py -f TargetServerMemory.raw -t 2018-10-17..2018-10-21

Extract timeline from all images in current directory, limited to a timeframe from 2018-10-17 to 2018-10-21:

./autotimeline.py -f ./*.raw -t 2018-10-17..2018-10-21

Extract timeline from TargetServerMemory.raw, using a custom memory profile:

./autotimeline.py -f TargetServerMemory.raw -p Win2008R2SP1x64

All timelines will be saved as $ORIGINALFILENAME-timeline.csv.

TODO

* Better image identification
* Better error trapping
Download Autotimeliner

Continuing with Computer engineering degree VS online WGU degree ?
https://www.reddit.com/r/Pentesting/comments/ss9pk5/continuing_with_computer_engineering_degree_vs/

<!-- SC_OFF -->So i am studying computer engineering, i have like 1.5 years left to graduate and i hate everything about it. i can drop out easily and join an online cyber security school like WGU and prolly finish it in 6 months, but i need solid advice as this is a big step for me, is a cyber security degree from WGU valuable on the resume just like an engineering degree would ? i heard that employers wont be impressed by a degree you can get in 6 months, so is it worth the transition ? <!-- SC_ON --> submitted by /u/watermelonSoundsNice (https://www.reddit.com/user/watermelonSoundsNice)
[link] (https://www.reddit.com/r/Pentesting/comments/ss9pk5/continuing_with_computer_engineering_degree_vs/) [comments] (https://www.reddit.com/r/Pentesting/comments/ss9pk5/continuing_with_computer_engineering_degree_vs/)

Autotimeliner - Automagically Extract Forensic Timeline From Volatile Memory Dump

Automagically extract forensic timeline from volatile memory dumps. Requirements Python 3 Volatility mactime (from SleuthKit) (Developed and tested on Debian 9.6 with Volatility 2.6-1 and sleuthkit 4.4.0-5) How it works AutoTimeline automates this workflow: Identify correct volatility profile for the memory image. Runs the timeliner plugin against volatile memory dump using volatility. Runs the mftparser volatility plugin, in order to extract $MFT from memory and generate a bodyfile. Runs the shellbags volatility plugin in order to generate a bodyfile of the user activity. (suggested by Matteo Cantoni). Merges the timeliner, mftparser and shellbags output files into a single bodyfile. Sorts and filters the bodyfile using mactime and exports data as CSV. Installation Simply clone the GitHub repository: git clone https://github.com/andreafortuna/autotimeliner.git Usage Memory dump file -t TIMEFRAME, --timeframe TIMEFRAME Timeframe used to filter the timeline (YYYY-MM-DD ..YYYY-MM-DD) -p CUSTOMPROFILE, --customprofile CUSTOMPROFILE Jump image identification and use a custom memory profile">autotimeline.py -h -f IMAGEFILE -t TIMEFRAME -p CUSTOMPROFILEoptional arguments: -h, --help show this help message and exit -f IMAGEFILE, --imagefile IMAGEFILE Memory dump file -t TIMEFRAME, --timeframe TIMEFRAME Timeframe used to filter the timeline (YYYY-MM-DD ..YYYY-MM-DD) -p CUSTOMPROFILE, --customprofile CUSTOMPROFILE Jump image identification and use a custom memory profile Examples Extract timeline from TargetServerMemory.raw, limited to a timeframe from 2018-10-17 to 2018-10-21: ./autotimeline.py -f TargetServerMemory.raw -t 2018-10-17..2018-10-21 Extract timeline from all images in current directory, limited to a timeframe from 2018-10-17 to 2018-10-21: ./autotimeline.py -f ./*.raw -t 2018-10-17..2018-10-21 Extract timeline from TargetServerMemory.raw, using a custom memory profile: ./autotimeline.py -f TargetServerMemory.raw -p Win2008R2SP1x64 All timelines will be saved as $ORIGINALFILENAME-timeline.csv. TODO Better image identification Better error trapping Download Autotimeliner
Read more...

How to exploit 'gateway.php' for a CTF?
https://www.reddit.com/r/Pentesting/comments/ss9tls/how_to_exploit_gatewayphp_for_a_ctf/

<!-- SC_OFF -->I am trying to solve a CTF which is supposed to be basic. It has this login page called 'gateway.php'. I am not able to figure out how to exploit it. I understand that a "Authentication Bypass" attack is what I need to do, but I don't know how to. All help will be highly appreciated. <!-- SC_ON --> submitted by /u/elpasolol (https://www.reddit.com/user/elpasolol)
[link] (https://www.reddit.com/r/Pentesting/comments/ss9tls/how_to_exploit_gatewayphp_for_a_ctf/) [comments] (https://www.reddit.com/r/Pentesting/comments/ss9tls/how_to_exploit_gatewayphp_for_a_ctf/)

hacking: security in practice
BlackByte Ransomware Group Attacked San Francisco 49ers Ahead of Super Bowl

https://external-preview.redd.it/mMo9GNNPx3e8q99tHRDb9tTKNpuZThWbOqBp94Akt3w.jpg?width=640&crop=smart&auto=webp&s=f159bb24fc5ad0b3393f1a6482614d48c4802fbb submitted by /u/djuyera
[link] [comments]

___________________________
@hacking_Attack
@Hacking_Video

hacking: security in practice
How safe my MAC 2015 webcam is?

Hey guys,

I had a intimate situation last night, watching porn etc. I totally forgot to put a tape cover over my camera lenses and now I cant stop obsessing if someone caught me or not. My light on camera haven't lit up or anything. There was a Apple statement how only way your camera can be turned on is with it's light.

submitted by /u/racunarski
[link] [comments]

___________________________
@hacking_Attack
@Hacking_Video

Research on Clickjacking & Network Sniffing- Cyber Sapiens Internship Task-14

Hello guys👋👋 ,Prajit here from the BUG XS Team and Cyber Sapiens United LLP Cybersecurity and Red Team Intern, in this I am regularly…Continue reading on Medium »
Read more...

Kali Linux Tutorials
Haptyc : Test Generation Framework

Haptyc is a python library which was built to add payload position support and Sniper/Clusterbomb/Batteringram/Pitchfork attack types into Turbo Intruder. While Haptyc accomplishes these goals fairly well it also introduces a simpler way to express test sequences in general. While this library was meant to target Turbo Intruder it has no hard dependencies on Turbo Intruder and can be used anywhere one requires test generation in a Python context. Unfortunately at this time since Haptyc was built for a jython interpreter it only supports Python 2.7 (however future changes will fix this). What are Haptyc tags?

Haptyc tags are tags which a tester can use to annotate an original input payload. A tester can use multiple tags to surround key pieces of data in an HTTP request to wrap it as a positional payload. When tests are being generated Haptyc will parse all the tags in the original payload and generate tests in accordance to the functions associated with the tag names. When Haptyc evaluates a Haptyc tag it will execute the associated tag function (this is called a Haptyc Transform) for a test payload to place in position of the associated tag in the request. Every tag function will receive a data argument and a state argument. The data argument may contain the inner data of the tag or may contain some other test payload sequence. The state argument is a state object associated with the tag where state can be stored between test iterations. Let’s review an example. Example 1: Simple list iteration

Original Payload

GET /animal/[+GuessAnimal]dog[+end] HTTP/1.1

Haptyc Class & Haptyc Transform

from haptyc import *
original = “GET /animal/[+GuessAnimal]dog[+end] HTTP/1.1”
class TestLogic(Transform):
@ApplyList([“snake”,”cat”,”owl”,”lion”])
def test_GuessAnimal(self, data, state):
return data + “?original=” + self.inner() + “&attempt=” + str(state.iter)
TestFactory = TestLogic(original)
for test in TestFactory:
print(test)

Tests Generated

GET /animal/snake?original=dog&attempt=0 HTTP/1.1
GET /animal/cat?original=dog&attempt=1 HTTP/1.1
GET /animal/owl?original=dog&attempt=2 HTTP/1.1
GET /animal/lion?original=dog&attempt=3 HTTP/1.1

In the example above we how one can express tests in a simple way using Haptyc. First the Haptyc library is imported. Second we have defined the original data with our Haptyc tag annotations (GuessAnimal). Next the TestLogic class is defined and extended as a Transform class. Inside this class every method that starts with test_will get registered as a Haptyc tag for evaluation in the original payload. We use a logic decorator to apply the state logic for this Haptyc transform. In this case we use the @ApplyList(list)decorator to tell Haptyc to generate a test for every item in the specified list and place that item into the Haptyc transform as the data argument. Inside the transform we return a mutated version of the data to insert back into the position of the tag. In this case the mutation is the list item as data concatenated with the data surrounded by the tag (dog) and then concatentated with the iter value in the state object. Lastly the remaining python shows the TestFactory object being created and all tests being generated in a for loop iterator. This is an example of a standard sniper-style attack which targets a single payload position. Next lets look at other style of attacks. Example 2: Clusterbomb

Original Payload

GET /animal?type=[%type]dog[%end]&name=[%name]fido[%end] HTTP/1.1

Haptyc Class & Haptyc Transform

from haptyc import *
original = “GET /animal?type=[%type]dog[%end]&name=[%name]fido[%end] HTTP/1.1”
class TestLogic(Transform):
@ApplyList(“snake”,”cat”,”owl”,”lion”)
def test_type(self, data, state):
return data
@ApplyList(“Frank”, “Lisa”, “Jin”, “Tooth”)
def test_name(self, data, [...]

___________________________
@hacking_Attack
@Hacking_Video