Since its inception, the ASERT team has been looking into politically motivated DDoS events  and continues to do so as the relationship between geopolitics and the threat landscape evolves . In 2013, ASERT published three situational threat briefs related to unrest in Syria  and Thailand  and threat activity associated with the G20 summit . Recently, other security research teams, security vendors and news agencies have posited connections between “cyber” and geopolitical conflicts in Iraq , Iran , and Ukraine  .
Given the increasing connections being made between security incidents and geopolitical events, I checked Arbor’s ATLAS data to look at DDoS activity in the context of the current conflict between Israel and Hamas. Arbor’s ATLAS initiative receives anonymized traffic and DDoS attack data from over 290 ISPs that have deployed Arbor’s Peakflow SP product around the globe. Currently monitoring a peak of about 90 Tbps of IPv4 traffic, ATLAS see’s a significant portion of Internet traffic, and we can use that to look at reported DDoS attacks sourced from or targeted at various countries.
Israel as a Target of DDoS Attacks
Figure 1 depicts the number of reported DDoS attacks initiated against Israel per Continue reading
As the infosec community waits for the researchers involved to present their Zeus Gameover take down spoils at the next big conference; ASERT wanted to profile a threat actor that uses both Citadel, “a particularly sophisticated and destructive botnet”, and Gameover, “one of the most sophisticated computer viruses in operation today”, to steal banking credentials.
When a threat actor decides that they would like to start a Citadel campaign they: buy the builder software, build the malware, distribute it to the wild, and then, unfortunately, usually profit. A “login key” in Citadel parlance identifies a specific copy of the builder. This key is also copied into the generated binaries so a link between malware builder and malware is formed. Login keys are supposed to be unique, but due to builders being leaked to the public, some aren’t. For all intents and purposes though, malware researchers use login keys to distinguish between distinct Citadel campaigns.
ASERT has the following command and control (C2) URLs linked with that campaign. Most Continue reading
Since publication of the Etumbot blog on Friday, June 6th, we’ve received numerous requests to publish Snort rules for the network indicators described therein. You can find Snort rules for the Etumbot C&C communications on Arbor’s github at
While we are not Snort syntax experts, we have performed basic testing for the Etumbot communications we’ve been able to observe over the wire. Specifically, the first three Snort rules for Etumbot RC4 Key Request, Etumbot Registration Request, and EtumBot Ping all triggered successfully when the corresponding network traffic was observed.
Remember to change the SIDs as appropriate for your environment. We also anticipate these rules will be incorporated into the EmergingThreats Open feed in the very near term.
The Arbor Security Engineering Response Team (ASERT) has released a research paper concerning the Etumbot malware.
Etumbot is a backdoor used in targeted attacks since at least March 2011. Indicators suggest that Etumbot is associated with the Numbered Panda group, also known as IXEHSE, DynCalc, and APT12. Although previous research has covered related malware, little has been publicly discussed regarding Etumbot’s capabilities.
Indicators suggest that the Etumbot dropper is delivered via spear phishing and is contained inside an archive file intended to be of interest to the target. The attackers use the Unicode Right to Left Override technique and document icons to disguise malicious executable content as document files. Once the dropper is executed, the backdoor is activated and a distraction file of interest to the target is opened for viewing. ASERT has observed several Etumbot samples using distraction documents involving Taiwanese and Japanese topics of interest, and has also observed recent development activity which indicates that attack campaigns are ongoing.
Once installed, the backdoor connects to it’s Command & Control server and receives an encryption key. RC4 encryption, along with HTTP transactions intended to blend in with typical traffic are used for backdoor communications. Etumbot’s core functionality Continue reading
By Matt Bing & Dave Loftus
Arbor Networks’ ASERT has recently discovered a new malware family that combines several techniques to steal payment card information. Dubbed Soraya, meaning “rich,” this malware uses memory scraping techniques similar to those found in Dexter to target point-of-sale terminals. Soraya also intercepts form data sent from web browsers, similar to the Zeus family of malware. Neither of these two techniques are new, but we have not seen them used together in the same piece of malware.
Soraya begins by injecting itself as a thread on several system processes, including the Windows Shell
explorer.exe. The malware maintains persistence by writing a copy of itself into the AppData directory with the name
servhost.exe, and setting itself to execute with the registry key
New processes launched from the infected explorer.exe shell, notably web browsers, will have Soraya code injected. The malware does this by hooking calls to the
ntdll.dll!NtResumeThread() function, which is responsible for process initialization. The function
ntdll!NtQueryDirectoryFile() is also hooked to hide displaying the
servhost.exe file. Both of these techniques are similar to functionality found in the Zeus family of malware.
One thread Continue reading
Point of Sale systems that process debit and credit cards are still being attacked with an increasing variety of malware. Over the last several years PoS attack campaigns have evolved from opportunistic attacks involving crude theft of card data with no centralized Command & Control, through memory scraping PoS botnets with centralized C&C and most recently to highly targeted attacks that require a substantial amount of lateral movement and custom malware created to blend in with the target organization.
While contemporary PoS attackers are still successful in using older tools and methodologies that continue to bring results due to poor security, the more ambitious threat actors have moved rapidly, penetrating organizational defenses with targeted attack campaigns. Considering the substantial compromise lifespans within organizations that have active security teams and managed infrastructure, indicators shared herein will be useful to detect active as well as historical compromise.
Organizations of all sizes are encouraged to seriously consider a significant security review of any PoS deployment infrastructure to detect existing compromises as well as to strengthen defenses against an adversary that continues to proliferate and expand attack capabilities.
In addition to recent publications discussing Dexter and Project Hook malware activity, Arbor ASERT is currently Continue reading
ASERT’s malware collection and processing system has automatic heuristics that bubble up potentially new and interesting DDoS malware samples into a “for human analysis” queue. A recent member of this queue was Trojan.Eclipse and this post is my analysis of the malware and its associated campaigns.
Analysis was performed on the sample with an MD5 of 0cdd10cd3393d3fe916a55b946c10ad6.
The name Eclipse comes from two places: a mutex named “eclipseddos” and a hardcoded Cookie value used in the command and control (C2) phone home. We’ll see in the Campaign section below that this threat is also known as: shadowbot, gbot3, eclipsebot, Rhubot, and Trojan-Spy.Win32.Zbot.qgxi.
Based on the C2 domain names, GeoIP of the C2 IP addresses, and a social media profile of the owner of one of the C2 domains, I suspect this malware to be Russian in origin. In addition, Eclipse is written in Delphi and empirically Russian malware coders have a certain fondness for this language.
Command and Control
The analyzed binary has a hardcoded C2 domain string. This string is protected from modification by running it through a simple hashing algorithm and comparing it against a hardcoded hash at certain points of the code. The Continue reading