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
Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
AI Is Everywhere, but Don't Ignore the Basics
Howie Xu, Vice President of AI and Machine Learning at Zscaler,  9/10/2019
Fed Kaspersky Ban Made Permanent by New Rules
Dark Reading Staff 9/11/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Current Issue
7 Threats & Disruptive Forces Changing the Face of Cybersecurity
This Dark Reading Tech Digest gives an in-depth look at the biggest emerging threats and disruptive forces that are changing the face of cybersecurity today.
Flash Poll
The State of IT Operations and Cybersecurity Operations
The State of IT Operations and Cybersecurity Operations
Your enterprise's cyber risk may depend upon the relationship between the IT team and the security team. Heres some insight on what's working and what isn't in the data center.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-4147
PUBLISHED: 2019-09-16
IBM Sterling File Gateway 2.2.0.0 through 6.0.1.0 is vulnerable to SQL injection. A remote attacker could send specially-crafted SQL statements, which could allow the attacker to view, add, modify or delete information in the back-end database. IBM X-Force ID: 158413.
CVE-2019-5481
PUBLISHED: 2019-09-16
Double-free vulnerability in the FTP-kerberos code in cURL 7.52.0 to 7.65.3.
CVE-2019-5482
PUBLISHED: 2019-09-16
Heap buffer overflow in the TFTP protocol handler in cURL 7.19.4 to 7.65.3.
CVE-2019-15741
PUBLISHED: 2019-09-16
An issue was discovered in GitLab Omnibus 7.4 through 12.2.1. An unsafe interaction with logrotate could result in a privilege escalation
CVE-2019-16370
PUBLISHED: 2019-09-16
The PGP signing plugin in Gradle before 6.0 relies on the SHA-1 algorithm, which might allow an attacker to replace an artifact with a different one that has the same SHA-1 message digest, a related issue to CVE-2005-4900.