Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Threat Intelligence

5/11/2017
10:30 AM
Derek Manky
Derek Manky
Commentary
Connect Directly
LinkedIn
RSS
E-Mail vvv
100%
0%

Artificial Intelligence: Cybersecurity Friend or Foe?

The next generation of situation-aware malware will use AI to behave like a human attacker: performing reconnaissance, identifying targets, choosing methods of attack, and intelligently evading detection.

Second of a two-part series.

Just as organizations can use artificial intelligence to enhance their security posture, cybercriminals may begin to use it to build smarter malware. This is precisely why a security fabric approach is needed — security solutions for network, endpoint, application, data center, cloud and access working together as an integrated and collaborative whole — combined with actionable intelligence to hold a strong position on autonomous security and automated defense.

In the future, we will have attacker/defender AI scenarios play out. At first, they will employ simple mechanics. Later, they will play out as intricate scenarios with millions of data points to analyze and address. However, at the end of the day, there is only one output: whether a compromise occurred or not.

Threats are getting smarter and are increasingly able to operate autonomously. In the coming year, we expect to see malware designed with adaptive, success-based learning to improve the success and efficacy of attacks. This new generation of malware will be situation-aware, meaning that it will understand the environment it is in and make calculated decisions about what to do next. In many ways, malware will begin to behave like a human attacker: performing reconnaissance, identifying targets, choosing methods of attack, and intelligently evading detection.

This next generation of malware uses code that is a precursor to artificial intelligence, replacing traditional “if not this, then that” code logic with more complex decision-making trees. Autonomous malware operates much like branch prediction technology, which is designed to guess which branch of a decision tree a transaction will take before it is executed. A branch predictor keeps track of whether or not a branch is taken, so when it encounters a conditional jump that it has seen before, it makes a prediction so that over time, the software becomes more efficient.

Autonomous malware, as with intelligent defensive solutions, is guided by the collection and analysis of offensive intelligence, such as types of devices deployed in a network segment, traffic flow, applications being used, transaction details, or time of day transactions occur. The longer a threat can persist inside a host, it will be that much better able to operate independently, blend into its environment, select tools based on the platform it is targeting and, eventually, take counter-measures based on the security tools in place.

A New Threat: Transformers
We as an industry also will see the growth of cross-platform autonomous malware designed to operate on and between a variety of mobile devices. These cross-platform tools, or “transformers,” include a variety of exploit and payload tools that can operate across different environments. This new variant of autonomous malware includes a learning component that gathers offensive intelligence about where it has been deployed, including the platform on which it has been loaded, then selects, assembles and executes an attack against its target using the appropriate payload.

Transformer malware is being used to target cross-platform applications with the goal of infecting and spreading across multiple platforms, thereby expanding the threat surface and making detection and resolution more difficult. Once a vulnerable target has been identified, these tools can also cause code failure and then exploit that vulnerability to inject code, collect data and persist undetected.

The Big Picture
Autonomous malware, including transformers that are designed to proactively spread between platforms, can have a devastating effect on our increasing reliance on connected devices to automate and perform everyday tasks. Efforts to analyze data for competitive business insights will be hampered. Overcoming these challenges will require highly integrated and intelligent security technologies that can see across platforms, correlate threat intelligence and automatically synchronize a coordinated response. Artificial intelligence and machine learning will prove invaluable in this role, ultimately enabling the vision of Intent-Based Network Security (IBNS) that can automatically translate business requirements and apply them to the entire infrastructure.

In part one of the series, Extreme Makeover: AI & Network Cybersecurity, Derek describes how artificial intellegience and machine learning are playing a vital role in the way security professionals consume and analyze data.

Related Content:

 

Derek Manky formulates security strategy with more than 15 years of cyber security experience behind him. His ultimate goal to make a positive impact in the global war on cybercrime. Manky provides thought leadership to industry, and has presented research and strategy ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
NSA Appoints Rob Joyce as Cyber Director
Dark Reading Staff 1/15/2021
Vulnerability Management Has a Data Problem
Tal Morgenstern, Co-Founder & Chief Product Officer, Vulcan Cyber,  1/14/2021
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
2020: The Year in Security
Download this Tech Digest for a look at the biggest security stories that - so far - have shaped a very strange and stressful year.
Flash Poll
Assessing Cybersecurity Risk in Today's Enterprises
Assessing Cybersecurity Risk in Today's Enterprises
COVID-19 has created a new IT paradigm in the enterprise -- and a new level of cybersecurity risk. This report offers a look at how enterprises are assessing and managing cyber-risk under the new normal.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-11997
PUBLISHED: 2021-01-19
Apache Guacamole 1.2.0 and earlier do not consistently restrict access to connection history based on user visibility. If multiple users share access to the same connection, those users may be able to see which other users have accessed that connection, as well as the IP addresses from which that co...
CVE-2020-27266
PUBLISHED: 2021-01-19
In SOOIL Developments Co., Ltd Diabecare RS, AnyDana-i and AnyDana-A, a client-side control vulnerability in the insulin pump and its AnyDana-i and AnyDana-A mobile applications allows physically proximate attackers to bypass user authentication checks via Bluetooth Low Energy.
CVE-2020-27268
PUBLISHED: 2021-01-19
In SOOIL Developments Co., Ltd Diabecare RS, AnyDana-i and AnyDana-A, a client-side control vulnerability in the insulin pump and its AnyDana-i and AnyDana-A mobile applications allows physically proximate attackers to bypass checks for default PINs via Bluetooth Low Energy.
CVE-2020-27269
PUBLISHED: 2021-01-19
In SOOIL Developments Co., Ltd Diabecare RS, AnyDana-i and AnyDana-A, the communication protocol of the insulin pump and its AnyDana-i and AnyDana-A mobile applications lacks replay protection measures, which allows unauthenticated, physically proximate attackers to replay communication sequences vi...
CVE-2020-28707
PUBLISHED: 2021-01-19
The Stockdio Historical Chart plugin before 2.8.1 for WordPress is affected by Cross Site Scripting (XSS) via stockdio_chart_historical-wp.js in wp-content/plugins/stockdio-historical-chart/assets/ because the origin of a postMessage() event is not validated. The stockdio_eventer function listens fo...