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PacketSled names John Keister President and CEO

in News, PacketSled by Christina Patten Comments are off
SAN DIEGO, Oct. 25, 2017 /PRNewswire/ — PacketSled, Inc., a leading provider of cloud-based network visibility, detection, incident response and breach forensics, today named John Keister as its new President and Chief Executive Officer. He steps in at an important time for PacketSled as it aims to build its market position and expand its customer footprint. The company also announced that it raised more than $3.5 million, primarily from existing investors, to continue to fund the company’s growth.

Fred Wilmot, who has served as CTO since June 2016 and as interim CEO since November 2016, will remain as CTO. Wilmot is a former Splunk executive and a security industry expert, serving as an advisor at the Managed Security Services Provider Ravenii and as a principal at AM Cyber. The company’s VP Sales, Jared Ballou, is a former sales executive at the public cybersecurity company Rapid7, and he will continue in his current position. Justin Stottlemyer, an Intuit engineering executive with previous technical leadership roles at Facebook and PayPal, will remain on the board as an outside director.

Keister has previous experience as a co-founder, president and chief operating officer at two technology companies focused on search, online advertising and analytics. These two companies, Go2Net and Marchex, each successfully grew to an annual revenue run rate of more than $100 million and successfully completed the IPO process. Keister’s previous operating responsibilities included oversight of sales, business development, marketing, engineering and technical operations. He also has 10-plus years of experience investing in early-stage software companies and sitting on the boards of directors for several of these companies.

“John’s experience as a founder and operator at both early-stage companies and public companies will amplify PacketSled’s product team as we prepare for the next phase of our growth,” said Wilmot.

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Threat Hunting and Endpoints; A Dr. Stephenson tutorial

in Uncategorized by Christina Patten Comments are off

“Packetsled provides the enrichment that triggers early warnings and proactive action to prevent breaches.”

 

SC Magazine’s Dr. Peter Stephenson analyzes the best threat hunting platforms in cyber security. Read the full blog here

Capture The PCAP challenge

in Events, PacketSled by Fred Wilmot Comments are off
 
PacketSled PacketSled. The Incident Response platform of choice for security experts.
 
Congratulations Seminole Gaming on winning the PCAP Challenge at Black Hat USA 2017! Seminole Gaming won $500 and a free PacketSled license for their team.
Do you have traffic with protocols you don’t think anyone can analyze? Does your environment have industry-specific or standard protocols that Wireshark can’t decode?
Take the PCAP challenge!  Due to popular demand, PacketSled is opening up the PCAP challenge to everyone! We want to encourage research and education for all the things we need to protect and support in the IT and OT universe. 
Submit your hairy, scary PCAP and get free analysis of your PCAP, and a 1:1 session with one of our ensemble detection experts to review the results.*
CAPTURE A $500 GIFT CARD
GET STARTED
Submit your PCAP, if you’re attending BroCon’17 be sure to stop by our booth!
SEE YOU THERE!
ENSEMBLE DETECTION FROM PACKETSLED. YOUR UNFAIR ADVANTAGE IN INCIDENT RESPONSE.
As security practitioners, we know it’s all about people like us. We want to give you the unfair advantage by weaponizing your Incident Response capability and amplifying your security expertise.
 

Hunting for DoublePulsar in your networks

in Incident Response, Network Visibility, Security Research, Threat Detection by Mike Spohn Leave a comment
The recent release of the Equation Group’s (NSA) FuzzBunch software by the Shadow Brokers has caused quite a stir in the security community. The volume of files released, (6,547 in one of the dumps), is an extraordinary collection of malicious software including many zero-day exploits.

One of the binaries that caught my interest was DoublePulsar. This is the main tool used by the Equation Group to compromise Windows hosts using a SMB and RDP zero-day exploit.

Although the attack surface is complicated, my fellow researchers at zerosum0x0.com did a highly competent job of describing it. To me, the most interesting step in the attack is the patching of the function dispatch table of the device driver Srv.sys in memory. Slot 0x20 (14) in this table originally pointed to the SrvTransactionNotImplemented() dispatch function. It is hijacked by the malware.

Why is this interesting? Because even though the implementation of this attack is quite brilliant, it is trivial to identify this attack in your network.

I refer you to page 426 of (Microsoft’s Common Internet File System (CIFS) Protocol) protocol document:

2.2.6.15 TRANS2_SESSION_SETUP (0x000E)
“This Transaction2 subcommand was introduced in the NT LAN Manager dialect. This subcommand is reserved but not implemented. Clients SHOULD NOT send requests using this command code. Servers receiving requests with this command code SHOULD return STATUS_NOT_IMPLEMENTED (ERRDOS/ERRbadfunc).”
The CIFS/SMB TRANS2_SESSION_SETUP subcommand was never implemented by Microsoft. The standard states that any call to the command by a Windows client should return a STATUS_NOT_IMPLEMENTED reply. Once DoublePulsar redirects the Srv.sys SrvTransactionNotImplemented[] function pointer to its own code injected in memory, any SMB call to a NOT_IMPLEMENTED SMB subcommand will end up calling the DoublePulsar code.

Knowing this, is it possible to identify DoublePulsar in your network by simply looking for SMB requests for NON_IMPLEMENTED subcommands, or even simpler, any SMB STATUS_NOT_IMPLEMENTED response? Yes.

To illustrate this, look at the WireShark screenshot below that shows a SMB call to the TRANS2_SESSION_SETUP (0x000E) subcommmand.

Figure-1: TRANS2_SESSION_SETUP (0x0E) Request

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Analyzing a Spear Phishing Email

in Incident Response by Mike Spohn Leave a comment
About every week or so I receive one of those obvious Phishing Emails telling me a package was not deliverable or some such foolishness. After being very careful not to click on the attachment, I typically permanently delete these Emails. When I got another one of these Emails a few days ago, my curiosity got the best of me, so I decided to figure out how the cyber-punks build these social engineering attacks, and how they work.

I documented my analysis in a Research Paper, “Analyzing a Spear Phishing Email.” You can download the report here.
The findings of my research are summarized below:

1. The appearance of the Phishing Email is very primitive, alleging the postal service could not deliver a package. The from Email address was completely unrelated to the signature line. (k(at) karastel,ru, Eugene Lee.

2. The weaponized payload was a JavaScript file that has “.doc.” in its name, embedded in two zip files. This means the recipient has to open two zip files and click on the JavaScript file for the bad guys to win.

3. The JavaScript file uses Microsoft’s ActiveX framework to create an Object to connect to the Internet and download a malicious dropper JavaScript dropper file. This script is run using an Eval() statement. The script also connects to the Internet and downloads a malicious Window PE file.

4. The miscreants compromise legitimate web sites to host the malicious binaries. The scripts contain multiple dowload URL’s to protect against detection of compromised servers.

5. The Malicious PE file is a Cerber Ransomware binary that encrypts files in the logged on user’s Documents folder, and any attached USB devices.

6. There is a sophisticated web site on the DarkNet that instructs a victim how to obtain BitCoins and pay the ransom.

7. The cyber-punks who send out these Emails bank on economies of scale. If they send out 1 million weaponized Emails, if they have a 5% hit rate – that is 50,000 victims.

8. The ransomware problem is not going away anytime soon. In fact, evidence suggests the cyber-crimnals are getting more aggressive in their tactics. Not only are they encrypting files, they are also wiping out the master boot record (MBR) on compromised systems preventing them from booting.

We are continually adding enhancements to the PacketSled Platform to identify advanced ransomeware compromise techniques.

Packetsled UDP and TCP fallback analyzers

in Incident Response by Leo Linsky Comments are off
There are hundreds of protocols that we need to see in detail to have a clear picture of our customers’ networks, and we have developed a suite of proprietary analyzers to address these. There are hundreds — or even thousands — more relevant protocols and services which we need to identify, but which are not significant enough on their own to warrant their own custom protocol analyzers. Added together, however, these protocols become major obstacles to network visibility and security.

Interestingly enough, this problem is not unique to networking — it’s a statistical phenomenon with numerous socioeconomic implications. For example, the disease advocacy group Global Genes has estimated that more than 300 million people worldwide are living with one of the 7,000 diseases they define as rare in the United States. That’s almost 5% of the world population living with a “rare” disease. And yet, treatment for each is wildly different, so it’s less productive and less lucrative to investigate each of these rare diseases.

Unfortunately, there isn’t a universal ‘rare disease treatment,’ but at Packetsled, we *have* developed a ‘universal treatment’ for unidentified UDP/TCP protocols and services. For flows in which we haven’t associated a high level of metadata, we capture key excerpts from the exchange to allow for analysis by Bro scripts or our platform, which, if interesting, can be used to pull the rest of the flow from our network data recorder.

The entries we find are frequently plaintext, or provide protocol preambles that make their nature obvious, such as the potential WinRM vulnerabilities discussed here: https://packetsled.com/analyzing-unknown-protocols-finding-a-piece-of-hay-in-a-hay-stack/

Here’s an example of a UDP log showing a JSON protocol header (note that excerpt sizes are variable, and we could capture the entire JSON object here):
#fields ts      uid     id.orig_h       id.orig_p       id.resp_h       id.resp_p       excerpt excerpt_size    payload_size
#types  time    string  addr    port    addr    port    string  count   count
1486600189.506264       589bb7fd0000000000000003        172.16.0.118    17500   255.255.255.255 17500   {"host_int": 59724991715298692108921997512624305849, "version":         64      365
This is especially useful for IoT protocols, many of which we have fleshed out into more detailed, dedicated analyzers and many of which are facilitated in healthcare. Recent announcements from leading healthcare device manufacturers indicate that IoT is now being used with fetal monitors, electrocardiograms, glucose monitors, and tracking vital health information. We’ve even heard of pressure sensors being used to determine the ratio of empty hospital beds during disasters, such as floods and fires.

However, the thing to remember is IoT is still in a sort of infancy. Some established standards have been developed, but are not widely adopted. This includes communication protocols and methods of properly handling sensitive data. As personally identifiable data could be transferred using protocols such as HL7, it is highly recommended that a close eye be kept on these valuable data streams.

An example shown below:
MSH|^~\&|PIEDMONT||PriorityHealth||||ORU^R01|Q479004375T431430612|P|2.3|
PID|||001677980||SMITH^JOHN||19680219|M||||||||||929645156318|123456789|
PD1||||1234567890^LAST^FIRST^M^^^^^NPI|
OBR|1|341856649^HNAM_ORDERID|000002006326002362|648088^Basic                            Metabolic Panel|||20061122151600|||||||||1620^Hooker^Robert^L||||||20061122154733|||F|||||||||||20061122140000|
OBX|1|NM|GLU^Glucose Lvl|59|mg/dL|65-99^65^99|L|||F|||20061122154733|
As IoT breaks away from traditional data networks, and ways of engaging patients, organizations will find value in how PacketSled dissects these streams of data. This will aid in understanding the risk the organization is accepting with the adoption and implementation of IoT initiatives.

IoT, like Cloud, is extremely disruptive and often noisy and misunderstood. This makes it even tougher to be a Security Professional and unfortunately, executives and board members generally care very little about that. We do.

With the PacketSled platform, it is our goal to perform the following:
  • Reduce the efforts associated with non-contextual, traditional logs by applying Machine Learning and well-researched attack models.
  • Detect multi-staged attacks within ordinary-looking data flows, regardless of protocol type. As most advanced attackers aren’t facilitating 0-days, you need modeling and adaptive baselines that can spot attacks in YOUR network. “One Size Fits All” doesn’t really fit anyone, any longer.
  • Enable you to perform Incident Response at scale. Finding the piece of hay in the haystack is a tough grind and we’re here to help, with event correlation and applied threat data.
It’s time for us to break out of the same problematically applied daily grind, where you are attacked, then respond, then mop up the disaster, then you start all over again.

It’s time to talk to PacketSled.



Co-written by Leo Linsky and Patrick Kelley

Lowering The Poverty Line Of Incident Response

in Incident Response, Security Research by Patrick Kelley Comments are off

Over the years I’ve been part of monumental projects using several forms of technology, including SIEMs, in attempts to offset the talent gaps that plague the Information Security industry and shorten the “dwell” timeframe of attacks.  Taking a moment to define the anticipated goals and define the problem space, I find it necessary to state that properly launching a SIEM and similar technologies isn’t trivial and gaining a meaningful return on the investment is a significant challenge. Personally, I determine that return on investment to be a reduction in MTTD (Mean Time To Detection) and MTTR (Mean Time To Remediate).

The ultimate goal is revealing actionable intelligence that takes into consideration the true risk posed to the business and weights the alerts in that light.  This task cannot be achieved by leveraging and applying “threat intelligence” feeds as the primary form of correlation. Instead, it requires developing context around the core assets, networks, and layers of security controls that comprise the network.

In and of itself, this isn’t an easy problem to solve as businesses and their networks develop organically, as opposed to the more traditional networks created in the past.  This matters as demand for an urgent business needs drives the need for additional computing resources, leaving security as an afterthought.  This makes building contextual solutions around security problems a near impossible feat for most organizations.

Coincidentally, this is where SIEM implementations often fail. The concept of the SIEM is sound, but most implementations make decisions based on single, atomic events that are universally weighted.  This could be the match of an IP address or similar. This fails as security incidents are not singular, but more often contextual and long-playing.  Often I’ve been asked, “If we had signatures for Pass-The-Hash attack binaries and we used UAC, why were we breached? We had controls!”. 

Simply stated. This is a significant problem to solve.

The tough truth is the evolution of our networks and the way organizations communicate accelerate faster that the limited amount of resources the organization has to apply. So, just hire more professionals, right?

Here’s the harsh truth. Developing a proper Security Operations Center takes well over a year on average, and that’s just to nail down the basics.  Once developed, you will need a minimum of 8 well-trained engineers and a Sr. Security Architect to maintain the ability to respond 24/7/365, with ever-evolving breach detections. 

So, what’s the solution?  

In fairness, there isn’t a single one-shot answer for this.  However, I can offer the following advice that can greatly improve the security posture of an organization. 

Maintain a clear understanding that a threat is not just the presence of events, but also the absence of events. A phishing email with a malicious binary is certainly interesting, but so is the failed synchronization of an HA or failure of vMotion Snapshots.

Acknowledging that breaches can remain active for over 100 days, a platform that collects artifacts at the lowest level of ground truth and hold that evidence for hundreds of days, is becoming a requirement.

Threat Intelligence feeds are not the glue that binds together a security platform.  They are simply atomic indicators that can be applied to network flows. That’s not to say there’s no value in these sources of data, but a need to correlate those indicators with other sources.  Additionally, more Threat Feeds isn’t the answer.  It is far more valuable to choose the proper feeds for your environment and prevent as much overlap as possible.

Leveraging platforms, such as PacketSled, that apply multiple sources of enrichment to wire-level data, can provide greater confidence in incident relevance and the reduction of false-positives. For example, a Threat Intelligence match for an Apache attack methodology likely doesn’t represent a true threat when leveraged against a Windows environment.

We need to rely less on signatures and leaning more on User and Entity-based behavioral analytics. This isn’t an easily applied solution, but one that establishes dynamic baselines on observed behaviors with entities, allowing it to determine anomalies.  Anomalies could be determined as user accounts conducting database queries after midnight, which could not symbolize a threat, but an indicator worth additional investigation.

A means of properly prioritizing cases in a meaningful and actionable way. This is a tough problem, but one that will maximize the limited resources an organization has available. 

What’s the overall message in this?  Security is hard. It’s less the process of efficiently identifying the probable state of all enterprise platforms and users, but more so with identifying and understanding the applied risk of all possible operational states of systems and people. 

 

Reach out today and learn what we are doing to help you close the gaps and lower the poverty line in Incident Response.

How your Refrigerator is a Threat and Why you Should Care: Attacks on IoT

in Security Research by Patrick Kelley Comments are off
Last week, Wikileaks dropped an explosive number of documents related to their surveillance and hacking capabilities.  Much of this information included strategies related to IoT and household devices, such as the Samsung TV.  To be clear, the leak of CIA documents and data didn’t readily include usable source or exploit code for leveraged attacks, but was more of an internal wiki which provided information about available attacks and strategies used by the agency.   

In our research, it appeared that most of the exploits reference full-on remote access vulnerabilities, some of which were already known. Many of the documents outlined planned strategies or half-developed exploits, many of which requiring physical access to the device or the supply line.

WikiLeaks, in a statement, was vague about its source. “The archive appears to have been circulated among former US government hackers and contractors in an unauthorized manner, one of whom has provided WikiLeaks with portions of the archive,” the organization said.  

As the data dump is quite lengthy, let’s start with “Weeping Angel”.  This is an attack methodology that was recently published by Wikileaks and outlines that a Samsung TV can be hijacked using vulnerable firmware.  This was developed during a joint workshop between MI5 and BTSS (British Secret Service).  The recent reports disclose how to make the television appear to be powered off but in reality was being used to monitor targets. It describes the television as being in “Fake Off” mode.  Some key things to note is that when the television is compromised, it provides the attacker with a ported and modified TinyShell to provide shell, command execution, and file transfer capabilities.   

This functionality allows the television to be used as a monitoring device, as well as a pivot point for further attacks against devices on the network (persistence).  

The current versions of vulnerable firmware provide the following technical details: 
  • Video capture / Video snapshots
  • Max possible storage usage is 700MB (of 1.6GB).
  • The installation is similar to installing a standard Samsung application.
  • empDownload is the binary that downloads other apps or adverts and is executed by the system.
  • It appears to connect to Dreamhost and supports Telnet and FTP.
  • It has native WPA and iw wireless network capabilities. 
As a longtime security researcher, I tend to believe that these capabilities extend much farther than televisions.

So, you might be asking, “when is he going to tell me about my refrigerator trying to kill me?”. 
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Modeling Multistep Attack Scenarios for Detection

in Incident Response by Troy Molsberry Comments are off
Many incidents that impact an organization’s security involve multiple steps. For example, an alert that a malicious email was transferred over the network is of concern, but there can be many thousands of these per day in a typical environment, and vetting each one out individually is prohibitive. Of more interest to the defender would be information about a malicious email being delivered, followed by a user clicking on a link contained in the email, followed by any downloads initiated by that user from blacklisted servers. In this example, we have an indicator (malicious email) followed by an action (clicked a link), followed by another action (download). In general, a “behavior” or “attack” consists of a sequence of causally related activities. Vetting these complex behaviors out by hand can be tedious at best, and intractable in most cases. You have to manually implement an algorithm known as “forward chaining”. Start with the first step in the sequence, and use attributes from the sensor data to perform a query for the second step using results from the previous query, and continue through the sequence until either a result is found or the trail goes cold. One interesting aspect of the “forward chaining” algorithm is that it explodes in both data and time. Performing this task by hand is more or less impossible, yet we commonly refer to this practice as “incident response”. These “incident responders” rely on a tremendous amount of experience, domain knowledge, and expertise to extract out behaviors that could potentially be security incidents. At Packetsled, we chose to capture that knowledge in a repeatable way.

Capturing knowledge from domain experts into models is a broad research topic, but I think we would all agree that at some point you will need a designer, e.g., a method for users to build models of attacks, so let’s start there. We chose a graphical notation for our models. Domain experts can visually model the causal relationships between queries, and those queries can contain forward- and backward- chaining references, e.g., those queries can depend on the results of previous or future queries. This is the magic.

Let’s create an example model for the example of a user clicking a malicious email link followed by an infection.

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Using Bro to Explore Your Networks, like an AWS WordPress Blog

in Security Research by Leo Linsky Comments are off
My personal WordPress blog was hacked a few weeks ago. I hadn’t checked my simple AWS micro instance in a while, and fortunately, I happened to look at the site just a day after it was compromised. WordPress makes it easy to stand up a pretty website if you don’t want to spend time crafting a front-end yourself, but security is a major question mark. If you keep it up to date and don’t load it with tons of third party content, it’s probably OK for a personal blog. However, I had several WP plugins and themes that were a few weeks behind the last update, as was WP Core. This is likely how the attacker was able to gain write-access to my one and only post on my blog and change the links to point at some sketchy-looking foreign domain websites to improve his SEO. Interestingly enough, the attacker also made some edits in the article, including grammatical fixes and clauses that were consistent with the content (environmental regulations).

Regardless, I updated everything, removed unnecessary plugins, added a plugin to record more detailed WordPress history, secured my active accounts, and reverted the changes (except for one grammatical improvement – thank you attacker). Good WordPress security and statistics plugins are difficult to come by, and they most likely increase the attack surface of the website, so I decided to opt for the tool I use and develop every day: Bro IDS. The visibility provided by Bro goes beyond threat detection and can be used as a more powerful version of netflow, showing what’s running on a system in significant detail.

Monitoring a single web server instance facing external http requests behind a pretty limiting firewall (inbound tcp/80 only, or tcp/22 from my static IP address only) is not our typical deployment for Bro here at Packetsled — limited to basic web requests, and with no honeypot, I didn’t expect a whole lot of interesting attack vectors. However, there were some takeaways after letting Bro run for a few weeks, mostly regarding how my network operates and what kind of requests my site typically sees.

The blog uses basic unverified HTTP (I’m too lazy and cheap to set it up right), so I was unsure why I had at least one ssl log entry every hour:
#fields id.orig_h id.orig_p id.resp_h id.resp_p version cipher curve server_name resumed last_alert next_protocol established cert_chain_fuids client_cert_chain_fuids subject issuer client_subject client_issuer validation_status

172.31.40.119 59718 52.201.183.155 443 TLSv12 TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 secp256r1 - F - - F Fb0mOL2796DAoCkRa3 (empty) - - - - -
172.31.40.119 41499 66.155.40.189 443 TLSv12 TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 secp256r1 - F - http/1.1 F Fr4ISl3di8C0feHNC4,F2sfO14ZkvhwHhaNb4,F10O3d278YkNUhURTf (empty) - - - - -
Ah of course — after opening up the corresponding x509 certificate log, it’s clear that my device is reaching out to WordPress about twice a day, looking for updates from “CN=*.wordpress.org,OU=Domain Control Validated CN=Go Daddy Secure Certificate Authority – G2,OU=http://certs.godaddy.com/repository/,O=GoDaddy.com.” More surprisingly though, my instance periodically was reaching out to an Amazon-based address every hour on my exposed network (as opposed to the back channels AWS uses to control instances):
pi@raspberrypi:~ $ whois 52.201.183.155
NetRange: 52.192.0.0 - 52.223.255.255 
CIDR: 52.192.0.0/11 
... 
Organization: Amazon Technologies Inc. (AT-88-Z) 
x509.log
#fields certificate.version certificate.serial certificate.subject certificate.issuer certificate.not_valid_before certificate.not_valid_after certificate.key_alg certificate.sig_alg certificate.key_type certificate.key_length certificate.exponent certificate.curve san.dns san.uri san.email san.ip basic_constraints.ca basic_constraints.path_len
3 07 emailAddress=info@bitrock.com,CN=stats.bitnami.com,O=BitRock Update Services,L=Seville,ST=Seville,C=ES emailAddress=info@bitrock.com,CN=BitRock Update Services,O=BitRock Update Services,L=Seville,ST=Seville,C=ES 1284478570.000000 1599838570.000000 rsaEncryption sha1WithRSAEncryption rsa 1024 65537 - - - - - F -
3 280392F6950C4C CN=*.wordpress.org,OU=Domain Control Validated CN=Go Daddy Secure Certificate Authority - G2,OU=http://certs.godaddy.com/repository/,O=GoDaddy.com\\, Inc.,L=Scottsdale,ST=Arizona,C=US 1417824798.000000 1513368681.000000 rsaEncryption sha256WithRSAEncryption rsa 2048 65537 - *.wordpress.org,wordpress.org - - - F -
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