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
Why Cyber-Risk Is a C-Suite Issue
Marc Wilczek, Digital Strategist & CIO Advisor,  11/12/2019
Unreasonable Security Best Practices vs. Good Risk Management
Jack Freund, Director, Risk Science at RiskLens,  11/13/2019
Breaches Are Inevitable, So Embrace the Chaos
Ariel Zeitlin, Chief Technology Officer & Co-Founder, Guardicore,  11/13/2019
Register for Dark Reading Newsletters
White Papers
Video
Cartoon Contest
Current Issue
Navigating the Deluge of Security Data
In this Tech Digest, Dark Reading shares the experiences of some top security practitioners as they navigate volumes of security data. We examine some examples of how enterprises can cull this data to find the clues they need.
Flash Poll
Rethinking Enterprise Data Defense
Rethinking Enterprise Data Defense
Frustrated with recurring intrusions and breaches, cybersecurity professionals are questioning some of the industrys conventional wisdom. Heres a look at what theyre thinking about.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2019-19010
PUBLISHED: 2019-11-16
Eval injection in the Math plugin of Limnoria (before 2019.11.09) and Supybot (through 2018-05-09) allows remote unprivileged attackers to disclose information or possibly have unspecified other impact via the calc and icalc IRC commands.
CVE-2019-16761
PUBLISHED: 2019-11-15
A specially crafted Bitcoin script can cause a discrepancy between the specified SLP consensus rules and the validation result of the [email protected] npm package. An attacker could create a specially crafted Bitcoin script in order to cause a hard-fork from the SLP consensus. All versions >1.0...
CVE-2019-16762
PUBLISHED: 2019-11-15
A specially crafted Bitcoin script can cause a discrepancy between the specified SLP consensus rules and the validation result of the slpjs npm package. An attacker could create a specially crafted Bitcoin script in order to cause a hard-fork from the SLP consensus. Affected users can upgrade to any...
CVE-2019-13581
PUBLISHED: 2019-11-15
An issue was discovered in Marvell 88W8688 Wi-Fi firmware before version p52, as used on Tesla Model S/X vehicles manufactured before March 2018, via the Parrot Faurecia Automotive FC6050W module. A heap-based buffer overflow allows remote attackers to cause a denial of service or execute arbitrary ...
CVE-2019-13582
PUBLISHED: 2019-11-15
An issue was discovered in Marvell 88W8688 Wi-Fi firmware before version p52, as used on Tesla Model S/X vehicles manufactured before March 2018, via the Parrot Faurecia Automotive FC6050W module. A stack overflow could lead to denial of service or arbitrary code execution.