Cyber Collaborative Research Alliance

The Cyber Collaborative Research Alliance is an initiative aimed at advancing the theoretical foundations of cyber science within Army networks. This program brings together academia, industry, and government researchers to develop a fundamental understanding of cyber phenomena. The main objective is to establish laws, theories, and models that are both theoretically grounded and empirically validated, applicable across various Army domains and environments.

Research within the program focuses on three key areas: identifying vulnerabilities and risks in cyber networks, detecting and analyzing malicious activities, and implementing agile cyber maneuvers to counteract threats. The goals include reducing the adversary's return on investment when targeting Army networks and minimizing the impact on network performance due to cybersecurity measures.

The program seeks to create a dynamic framework that integrates knowledge of cyber assets with potential adversary capabilities. This framework aims to provide adaptable defense mechanisms that respond to changes in mission requirements, asset vulnerabilities, and defense strategies. The work aligns with priorities set by the Under Secretary of Defense for Research and Engineering and supports the Army Modernization Strategy.

In recent years, efforts have concentrated on developing capabilities for rapid adaptation of cyber tools to assess risks, detect hostile activities, and support agile maneuvers in cyberspace. This includes foundational research into innovative theories and methods for understanding and exploiting Windows of Superiority (WoS) in cyberspace. The goal is to achieve operational advantages through synchronization across multiple domains by predicting emerging WoS and employing techniques such as cyber resilience and deception.

Looking ahead, the program plans to further explore methodologies for identifying and exploiting information from networks to assess their state and characterize WoS. It will also focus on developing techniques for detecting adversarial deception in cyberspace while enhancing machine learning algorithms used for intrusion detection. These efforts aim to counter adversarial attacks effectively while considering uncertainties in machine learning applications for network defenses.