[BreachExchange] 5 Emerging Trends in Cybercrime

[Destry Winant]

https://www.darkreading.com/endpoint/5-emerging-trends-in-cybercrime/a/d-id/1333363

To manage increasingly distributed and complex networks, organizations
are adopting artificial intelligence (AI) and machine learning to
automate tedious and time-consuming activities that normally require a
high degree of human supervision and intervention. To address this
transformation of the security ecosystem, the cybercriminal community
has now clearly begun moving in the same direction.

My threat predictions, taken from Fortinet's Threat Landscape
Predictions for 2019, reveal five emerging malicious trends:

1. AI Fuzzing: Because they target unknown threat vectors, exploiting
zero-day vulnerabilities is an especially effective cybercrime tactic.
Fortunately, they are also rare because of the time and expertise
needed by cyber adversaries to discover and exploit them. The process
for doing so involves a technique known as fuzzing.

Fuzzing is a sophisticated technique generally used in lab
environments by professional threat researchers to discover
vulnerabilities in hardware and software interfaces and applications.
They do this by injecting invalid, unexpected, or semirandom data into
an interface or program and then monitoring for events such as
crashes, undocumented jumps to debug routines, failing code
assertions, and potential memory leaks. Though using fuzzing to
discover zero-day vulnerabilities has, so far, been beyond the scope
of most cybercriminals, as AI and machine learning models are applied
to this process it will become more efficient and effective. As a
result, the rarity of zero-day exploits will change, which in turn
will have a significant impact on securing network devices and
systems.

2. Continual Zero-Days: While a large library of known exploits exists
in the wild, our cyber adversaries are actually only exploiting less
than 6% of them. However, to be effective, security tools need to be
watching for all of them as there is no way to know which 6% they will
use. Alsok as the volume of potential threats continues to grow,
performance requirements will continue to escalate as the scope of the
potential exploit landscape continues to expand. To keep up, security
will tools need to be increasingly more intelligent about how and what
they look for.

While there are some frameworks like zero-trust environments that may
have a chance at defending against this reality, it is fair to say
that most people are not prepared for the next generation of threats
on the horizon — especially those that AI-based fuzzing techniques
will soon begin to uncover. Traditional security approaches, such as
patching or monitoring for known attacks, will become nearly obsolete
as there will be little way to anticipate which aspect of a device can
be potentially exploited. In an environment with the possibility of
endless and highly commoditized zero-day attacks, even tools such as
sandboxing, which were designed to detect unknown threats, would be
quickly overwhelmed.

3. Swarms-as-a-Service: Advances in swarm-based intelligence
technology are bringing us closer to a reality of swarm-based botnets
that can operate collaboratively and autonomously to overwhelm
existing defenses. These swarm networks will not only raise the bar in
terms of the technologies needed to defend organizations, but, like
zero-day mining, they will also have an impact on the underlying
criminal business model, allowing them to expand their opportunity.

Currently, the criminal ecosystem is very people-driven. Professional
hackers build custom exploits for a fee, and even new advances such as
ransomware-as-a-service requires black-hat engineers to stand up
different resources. But when delivering autonomous, self-learning
swarms-as-a-service, the amount of direct interaction between a
hacker-customer and a black-hat entrepreneur will drop dramatically,
thereby reducing risk while increasing profitability.

4. A la Carte Swarms: Dividing a swarm into multiple tasks to achieve
a desired outcome is very similar to virtualization. In a virtualized
network, resources can spin up or spin down virtual machine as needed
to address particular issues such as bandwidth. Likewise, resources in
a swarm network could be allocated or reallocated to address specific
challenges encountered in an attack chain. In a swarm-as-a-service
environment, criminal entrepreneurs should be able to preprogram a
swarm with a range of analysis tools and exploits, from compromise
strategies to evasion and surreptitious data exfiltration that are all
part of a criminal a la carte menu. And because swarms by design
include self-swarms, they will require nearly no interaction or
feedback from their swarm-master or need to interact with a command
and control center, which is the Achilles' heel of most exploits.

5. Poisoning Machine Learning: One of the most promising cybersecurity
tools is machine learning. Devices and systems can be trained to
perform specific tasks autonomously, such as baselining behavior,
applying behavioral analytics to identify sophisticated threats, or
taking effective countermeasures when facing a sophisticated threat.
Tedious manual tasks, such as tracking and patching devices, can also
be handed over to a properly trained system. However, this process can
also be a two-edged sword. Machine learning has no conscience, so bad
input is processed as readily as good. By targeting and poisoning the
machine learning process, cybercriminals will be able to train devices
or systems to not apply patches or updates to a particular device, to
ignore specific types of applications or behaviors, or to not log
specific traffic to better evade detection.

Preparing for Tomorrow's Threats
Understanding the direction being taken by some of the most
forward-thinking malicious actors requires organizations to rethink
their current security strategy. Given the nature of today's global
threat landscape, organizations must react to threats at machine
speeds. Machine learning and AI can help in this fight. Integrating
machine language and AI across point products deployed throughout the
distributed network, combined with automation and innovation, will
significantly help fight increasingly aggressive cybercrime. It is
just important to remember, however, that these will soon be the same
tools being leveraged against you, and to plan accordingly.