chertogun / lora

LoRa

Collecting & Hunting for Indicators of Compromise (IOC) The two specialiced scanners LOKI and Rastrea2r have been merged into a new generic IOC scanner called LoRa. By using a client/server RESTful API, it can also hunt for IOCs on disk and memory across multiple systems using YARA rules. The server is the one responsible for finding, downloading the IOCs and then serving them as yara rules to the clients. The clients recieve the data and proceeds to scan its system and produce the reports of the result. The server side is inside the server folder(lora_server.py) and the client side inside win32 folder(lora_win32.py).

Detection is based on four detection methods:

1. File Name IOC Regex match on full file path/name
2. Yara Rule Check Yara signature match on file data and process memory
3. Hash check Compares known malicious hashes (MD5, SHA1, SHA256) with scanned files
4. C2 Back Connect Check Compares process connection endpoints with C2 IOCs

Additional Checks:

1. Regin filesystem check (via --reginfs)
2. Process anomaly check (based on Sysforensics)
3. SWF decompressed scan
4. SAM dump check
5. DoublePulsar check - tries to detect DoublePulsar backdoor on port 445/tcp and 3389/tcp

How-To Run LoRa and Analyse the Reports

Run

• Run it once to retrieve the latest signature base repository
• Provide the folder to a target system that should be scanned: removable media, network share, folder on target system
• Open a command line "cmd.exe" as Administrator and run it from there (you can also run LoRa without administrative privileges but some checks will be disabled and relevant objects on disk will not be accessible)

Reports

• The resulting report will show a GREEN, YELLOW or RED result line.
• Please analyse the findings yourself by:
  1. uploading non-confidential samples to Virustotal.com
  2. Search the web for the filename
  3. Search the web for keywords from the rule name (e.g. EQUATIONGroupMalware_1 > search for "Equation Group")
  4. Search the web for the MD5 hash of the sample

Update

LoRa includes a separate updater tool named lora-upgrader.py which downloads from hardcoded repositories different file types (txt, pdf, csv, yara, xlx, xlsx, ioc) and extracts from them with the help of ioc_parser the indicators we are interested in such as MD5s, SHA256s, URLs, Filenames, Hosts and then proceeds to build the corresponding yara rules. The updater can be called through the server with the --update flag.

usage: lora-upgrader.py [-h] [-l log-file] [--nolog] [--debug]

optional arguments:
  -h, --help   show this help message and exit
  -l log-file  Log file
  --nolog      Don't write a local log file
  --debug      Debug output

Dependencies

See the dependencies of the two tools.

cherrypy(server)

openpyxl

lxml

easy_install winpaths

Client

We can give to the client arguments for the operation we want it to do. For example we can tell it to use the rules we want to check separated by comma or just write 'all' for all the available rules we have. We can also do a one time check or make it run scans with time intervals.

The available checks are {mem-scan, disk-scan, mem-dump, prefetch, triage, web-hist}

mem-scan: scan the memory

disk-scan: scan the disk from a path and down(excludes taken into consideration)

mem-dump: dumps the current memory to a image file for further investigation

prefetch:

Info:

Each time you turn on your computer, Windows keeps track of the way your computer starts and which programs you commonly open. Windows saves this information as a number of small files in the prefetch folder. The next time you turn on your computer, Windows refers to these files to help speed the start process.

With this we can identify the processes that start with booting our system and see if there is anything malicious. The output format is csv and the results are located in \LoRa\win32\w1000\data\prefetch-{name of client pc}{folder with latest time stamp}

triage: Collects triage information from the endpoint by checking Sysinternal / third-party / BATCH files Checks if Mcafee epo agent is installed and if not an exception is raised which is normal.

web-hist: Collects the web history from various internet browsers and outputs it in a different csv file for each user. Currently tested only for microsoft edge 10. The output has the columns below. URL,Title,Visit Time,Visit Count,Visited From,Visit Type,Web Browser,User Profile,Browser Profile,URL Length,Typed Count

Server

The server is responsible for updating the signature base, delivering the rules asked by the client and returing in a dictionary form ioc data such as filenames, ips, hosts, hashes. The server running is cherrypy and it offers parallelism of requests. It produces a unique log file for each client.

Loki vs Rastrea2r

The two tools had some code blocks with the same functionality. In order to merge them in one tool I had to choose which specific parts of both are going to survive to the final product. Through comparison and checking I concluded to the following:

disk-scan:

rastrea2r only does rule match - loki filename and hash check

mem-scan:

loki checks processes connections

loki checks Sysforensics

loki does not check if working size bigger than a default due to performance

PDF files Problems

On Windows:

The only problem encountered is with some protected pdf files which the pdfminer could not open to parse. Solution: The one responsible for the server must transform the problematic pdf files to xps or oxps(h12756-wp-shell-crew had size zero when tranformed to .xps while was normal in .oxps) and back to pdf. That way the protection is removed and we can parse the documents. Until now the problematic pdfs are:

1. Alerts DL-2011 Alerts-A-2011-02-18-01 Night Dragon Attachment 1
2. Ashmore - Impact of Alleged Russian Cyber Attacks
3. Dark_Seoul_Cyberattack
4. DTL-06282015-01
5. energy-at-risk
6. h12756-wp-shell-crew To transform them to xps one must choose to print them and then save them as .xps files. There is only one corrupted file within all the repositories from which we build our signature-base and that is DTL-06282015-01.pdf. As for now these pdfs are manually transformed and parsed from the folder problematicPDFs.

Added features

threatexpert

Searching and parsing iocs from the site threatexpert. Scanning the registries for indicators that malware creates as well as mutexes, apart from hashes and file names etc.

ClamAV

Also I extract signatures from the ClamAV databases. The process to do it was to install it on windows first and from the folder conf_examples of the installation directory, copy the file freshclam.conf.sample to the installation directory and rename it freshclam.conf in order for the freshclam.exe to be able to start downloading the databases. The command given was freshclam.exe --datadir=C:\Users{User Name}\Desktop\clamAV or any other folder path could be given. Then, in order to extract the sigs from the .cvd files, I used e.g. sigtool -u main.cvd but I had to delete or rename the folder COPYING because it threw an error. From ClamAV 0.99 we can use the clamav_to_yara.py file to convert the clam signatures into yara rules. Once you have the .ndb file from the sigtool operation you can proceed to converting as follows: $ python clamav_to_yara.py -f {signature name}.ndb -o {Output File Name}.yara I have not included the necessary files in the project because as a vendor they will have to be provided and not force the users to do this job.

For the unofficial signatures of clamav I had to use an Ubuntu system and follow the instructions in this repo. It is not noted that inside the .conf files the lines user_configuration_complete flags will have to be uncommented and set to "yes" when done.