McAfee forecasts developments in adversarial machine learning, ransomware, serverless apps, connected home privacy, and privacy of child-generated content
- McAfee Labs predicts an adversarial machine learning “arms race” between attackers and defenders
- Ransomware to evolve from traditional PC extortion to IoT, high net-worth users, and corporate disruption
- Serverless Apps to create attack opportunities targeting privileges, app dependencies, and data transfers
- Connected home devices to surrender consumer privacy to corporate marketers
- Consumer apps collection of children’s content to pose long-term reputation risk
McAfee Inc. today released its McAfee Labs 2018 Threats Predictions Report, which identifies five key trends to watch in 2018. This year’s report focuses on the evolution of ransomware from traditional to new applications, the cybersecurity implications of serverless apps, the consumer privacy implications of corporations monitoring consumers in their own homes, long-term implications of corporations gathering children’s user-generated content, and the emergence of a machine learning innovation race between defenders and adversaries.
“The evolution of ransomware in 2017 should remind us of how aggressively a threat can reinvent itself as attackers dramatically innovate and adjust to the successful efforts of defenders,” said Steve Grobman, Chief Technology Officer for McAfee, LLC. “We must recognise that although technologies such as machine learning, deep learning, and artificial intelligence will be cornerstones of tomorrow’s cyber defences, our adversaries are working just as furiously to implement and innovate around them. As is so often the case in cybersecurity, human intelligence amplified by technology will be the winning factor in the ‘arms race’ between attackers and defenders.”
The report reflects the informed opinions of dozens of McAfee thought leaders from McAfee Labs, McAfee Advanced Threat Research, and members of McAfee’s Office of the CTO. It examines current trends in cybercrime and IT evolution, and anticipates what the future may hold for organisations working to take advantage of new technologies to both advance their businesses and provide better security protection:
1..An adversarial machine learning “arms race” will develop between defenders and attackers.
Machine learning can process massive quantities of data and perform operations at great scale to detect and correct known vulnerabilities, suspicious behaviour, and zero-day attacks. But adversaries will certainly employ machine learning themselves to support their attacks, learning from defensive responses, seeking to disrupt detection models, and exploiting newly discovered vulnerabilities faster than defenders can patch them.
To win this arms race, organisations must effectively augment machine judgment and the speed of orchestrated responses with human strategic intellect. Only then will organisations be able to understand and anticipate the patterns of how attacks might play out, even if they have never been seen before.
2..Ransomware will pivot from traditional extortion to new targets, technologies, and objectives.
The profitability of traditional ransomware campaigns will continue to decline as vendor defences, user education, and industry strategies improve to counter them. Attackers will adjust to target less traditional, more profitable ransomware targets, including high net-worth individuals, connected devices, and businesses.
The pivot from the traditional will see ransomware technologies applied beyond the objective of extortion of individuals, to cyber sabotage and disruption of organisations. This drive among adversaries for greater damage, disruption, and the threat of greater financial impact will not only spawn new variations of cybercrime “business models,” but also begin to seriously drive the expansion of the cyber insurance market.
3..Serverless apps will save time and reduce costs, but they will also increase attack surfaces for organisations implementing them.
Serverless apps enable greater granularity, such as faster billing for services. But they are vulnerable to attacks exploiting privilege escalation and application dependencies. They are also vulnerable to attacks on data in transit across a network, and potentially to brute-force denial of service attacks, in which the serverless architecture fails to scale and incurs expensive service disruptions.
Function development and deployment processes must include the necessary security processes, scalability capabilities must be made available, and traffic must be appropriately protected by VPNs or encryption.
4..Connected home device manufacturers and service providers will seek to overcome thin profit margins by gathering more of our personal data—with or without our agreement—turning the home into a corporate store front.
Corporate marketers will have powerful incentives to observe consumer behaviour in order to understand the buying needs and preferences of the device owners. Because customers rarely read privacy agreements, corporations will be tempted to frequently change them after the devices and services are deployed to capture more information and revenue.
McAfee believes that there will be regulatory consequences for corporations that make the calculation to break existing laws, pay fines, and continue such practices, thinking they can do so profitably.
5..Corporations collecting children’s digital content will pose long-term reputation risks.
In their pursuit of user app “stickiness,” corporations will become more aggressive in enabling and gathering user-generated content from younger users. In 2018, parents will become aware of notable corporate abuses of digital content generated by children, and consider the potential long-term implications of these practices for their own children.
McAfee believes many future adults will suffer from negative “digital baggage,” user content developed in a user-app environment where socially appropriate guidelines are not yet well defined or enforced, and where the user interface is so personally engaging that children and their parents do not consider the consequences of creating content that corporations could use and potentially abuse in the future.
In a competitive app environment where “stickiness” easily becomes “unstuck,” the most enterprising, forward-looking apps and services will recognise the brand-building value of making themselves a partner with parents in this education effort.
In this article
- adversarial machine
- adversarial machine learning
- adversarial machine learning arms
- adversarial machine learning arms race
- arms race
- arms race between attackers
- attackers and defenders
- connected home
- evolution of ransomware
- implications of corporations
- learning arms race
- machine learning
- machine learning arms
- machine learning arms race
- mcafee labs
- pose long-term reputation
- race between attackers
- race between attackers and defenders
- serverless apps
- vulnerable to attacks