The Myth of the Adversary Advantage
The Adversary Advantage is a illusory myth where it is believed adversaries have an inherent advantage due to needing to succeed only once, while defenders must succeed every time. Dragos aims to challenge the myth of the adversary advantage by providing tools to help defenders shape the landscape in which adversaries must operate.
DYMALLOY
DYMALLOY activity stretches back to 2015 and includes associations with activity into 2011. The activity focuses on intelligence gathering from industrial control system networks with an unknown intent.
Making ICS Training Accessible to the Greater InfoSec Community
Every twelve weeks, Dragos hosts “Assessing, Hunting and Monitoring Industrial Control System Networks,” a five-day course at its Hanover, Maryland headquarters.
ELECTRUM
In December 2016, in Kiev, Ukraine, a significant malware incident blacked out a portion of the city’s electricity for about an hour. ELECTRUM is the activity group responsible for the 2016 power outage event caused by the ICS malware CRASHOVERRIDE.
COVELLITE
COVELLITE compromises networks associated with civilian electric energy worldwide and gathers intelligence on intellectual property and internal industrial operations. COVELLITE lacks an industrial control system (ICS) specific capability at this time.
Indicators and ICS Network Defense
The notion that omitting IOCs from a report – whether public or private – may actively harm or disadvantage defenders really hits home, but in responding to this assertion I feel we can arrive at an even better understanding of the limitations of an IOC approach.
XENOTIME
XENOTIME is easily the most dangerous threat activity publicly known. It is the only activity group intentionally compromising and disrupting industrial safety instrumented systems, which can lead to scenarios involving loss of life and environmental damage.
Supply Chain Threats to Industrial Control: Third-Party Compromise
Adversaries possess multiple options for attacking an organization via third-party compromise: Network pivoting, spear phishing, weaponized installs and certificate/credential theft.
CHRYSENE
The CHRYSENE activity group developed from long-running cyberespionage activity that first came into the general public’s consciousness after a destructive cyberattack in 2012 impacting Saudi Aramco.
ALLANITE
ALLANITE accesses business and industrial control (ICS) networks, conducts reconnaissance, and gathers intelligence in United States and United Kingdom electric utility sectors.
The Current Industrial Threat Landscape: Reality Above Theory
The current industrial threat landscape is very concerning. All of our intelligence suggests industrial security entering a massive growth of threat activity which will likely last at least the next decade.
Query Focused Datasets
Query focused datasets (QFDs) provide analysts with powerful tools for both proactive threat hunts and investigations.
Dragos at RSAC 2018
Dragos made the annual cybersecurity pilgrimage to San Francisco, California on April 16-20 where each year thousands of people from the security industry gather for a week at the RSA Conference.
Investigation Playbooks in the Dragos Platform
In the most stressful situations, effective training and well-documented processes and procedures are absolutely essential to reliable and uniform response.
The Disappearing IT-IoT Divide and the Malware Poised to Take Advantage
By adopting a whole-network, defense-in-depth approach, asset owners and defenders can reduce their threat surface from such attacks.
Threat Hunting With Python Part 4
This week, we will look at a protocol commonly overlooked by many but crucial to control system operation: The Tabular Data Stream (TDS) protocol.
Threat Analytics and Activity Groups
In developing an analytic, the resulting detection methodology should not focus on a specific implementation of a behavior, but rather seek to cover multiple implementations of the behavior type.
Transferring Knowledge to Customers Through Software Technology
At Dragos, Inc., what we pride ourselves on, use as our technology differentiation, and offer as our most valued asset to our customers is knowledge transfer.
Threat Hunting With Python Part 3
This week we will focus on the Server Message Block (SMB) protocol that enabled Wannacry, Petya, and Bad Rabbit attacks to be lethal at the global level and what defenders can do to hunt within this protocol.
Threat Hunting With Python Part 2
This week we will move away from hard-coded indicators and begin to look at behavioral indicators. Behavioral indicators allow identification of scanning in an environment beyond just that of Nmap.
Threat Hunting With Python Part 1
Over the next few weeks, we will look at basic analytic approaches that can be taken to examine some of the most common protocols found on typical networks. This week we will get started with basic HTTP analysis using Python and Jupyter notebooks.
Threat Hunting Part 2: Hunting on ICS Networks
In this edition of the Dragos Threat Hunting on ICS network series, we will compare threat hunting on industrial networks with concepts from the wider threat hunting community. We will also look at how the unique characteristics of industrial networks can be used to an advantage as network defense professionals
Threat Hunting Part 1: Improving Through Hunting
This post is a first in series that will describe hunting, discuss best practices and explain our approach and lessons. Because hunting in industrial infrastructure is important to all of us and with focus and effort we can accomplish it.
Stop Breaches, Safeguard Civilization
Today Crowdstrike and Dragos issued a joint press release to finally announce the partnership we’ve developed over the course of the last year.
Analyzing CRASHOVERRIDE
This webcast explores what is known and not known about the CRASHOVERRIDE framework and how it affects our understanding of how grid operations can be impacted.
Project MIMICS - Stage One
What can the community learn in terms of realistic metrics and data points around malware in modern industrial control systems (MIMICS) from completely public datasets? That’s what project MIMICS sets out to do.
