The Department of Defense is increasingly relying on biometric data, such as iris scans, gait recognition, and heart-rate monitoring to protect against both cyber and physical attacks. "Military planners, like their civilian infrastructure and homeland security counterparts, use video-linked 'behavioral recognition analytics,' leveraging base protection and counter-IED operations," according to an article in Defense Systems. Current state-of-the-art approaches do not make it possible to gather biometric data in real-world settings, such as border and airport security checkpoints, where people are in motion. This blog post presents the results of exploratory research conducted by the SEI's Emerging Technology Center to design algorithms that extract heart rate from video of non-stationary subjects in real time.
Blockchain technology was conceived a little over ten years ago. In that short time, it went from being the foundation for a relatively unknown alternative currency to being the "next big thing" in computing, with industries from banking to insurance to defense to government investing billions of dollars in blockchain research and development. This blog post, the first of two posts about the SEI's exploration of DoD applications for blockchain, provides an introduction to this rapidly emerging technology.
The year 2016 witnessed advancements in artificial intelligence in self-driving cars, language translation, and big data. That same time period, however, also witnessed the rise of ransomware, botnets, and attack vectors as popular forms of malware attack, with cybercriminals continually expanding their methods of attack (e.g., attached scripts to phishing emails and randomization), according to Malware Byte's State of Malware report. To complement the skills and capacities of human analysts, organizations are turning to machine learning (ML) in hopes of providing a more forceful deterrent. ABI Research forecasts that "machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021." At the SEI, machine learning has played a critical role across several technologies and practices that we have developed to reduce the opportunity for and limit the damage of cyber attacks. In this post--the first in a series highlighting the application of machine learning across several research projects--I introduce the concept of machine learning, explain how machine learning is applied in practice, and touch on its application to cybersecurity throughout the article.
In the 2016 Cyber Security Intelligence Index, IBM found that 60 percent of all cyber attacks were carried out by insiders. One reason that insider threat remains so problematic is that organizations typically respond to these threats with negative technical incentives, such as practices that monitor employee behavior, detect and punish misbehavior, and otherwise try to force employees to act in the best interest of the organization. In contrast, this blog post highlights results from our recent research that suggests organizations need to take a more holistic approach to mitigating insider threat: one that incorporates human involvement. In particular, positive incentives can produce better balance and security for organizations by complementing traditional practices to insider threat programs. This post also presents three practices to increase positive incentives that organizations can use to reduce insider threat.