Cyber Law and Espionage Law as Communicating Vessels

Professor Lubin\u27s contribution is Cyber Law and Espionage Law as Communicating Vessels, pp. 203-225. Existing legal literature would have us assume that espionage operations and “below-the-threshold” cyber operations are doctrinally distinct. Whereas one is subject to the scant, amorphous, and under-developed legal framework of espionage law, the other is subject to an emerging, ever-evolving body of legal rules, known cumulatively as cyber law. This dichotomy, however, is erroneous and misleading. In practice, espionage and cyber law function as communicating vessels, and so are better conceived as two elements of a complex system, Information Warfare (IW). This paper therefore first draws attention to the similarities between the practices – the fact that the actors, technologies, and targets are interchangeable, as are the knee-jerk legal reactions of the international community. In light of the convergence between peacetime Low-Intensity Cyber Operations (LICOs) and peacetime Espionage Operations (EOs) the two should be subjected to a single regulatory framework, one which recognizes the role intelligence plays in our public world order and which adopts a contextual and consequential method of inquiry. The paper proceeds in the following order: Part 2 provides a descriptive account of the unique symbiotic relationship between espionage and cyber law, and further explains the reasons for this dynamic. Part 3 places the discussion surrounding this relationship within the broader discourse on IW, making the claim that the convergence between EOs and LICOs, as described in Part 2, could further be explained by an even larger convergence across all the various elements of the informational environment. Parts 2 and 3 then serve as the backdrop for Part 4, which details the attempt of the drafters of the Tallinn Manual 2.0 to compartmentalize espionage law and cyber law, and the deficits of their approach. The paper concludes by proposing an alternative holistic understanding of espionage law, grounded in general principles of law, which is more practically transferable to the cyber realmhttps://www.repository.law.indiana.edu/facbooks/1220/thumbnail.jp

Developing Robust Models, Algorithms, Databases and Tools With Applications to Cybersecurity and Healthcare

As society and technology becomes increasingly interconnected, so does the threat landscape. Once isolated threats now pose serious concerns to highly interdependent systems, highlighting the fundamental need for robust machine learning. This dissertation contributes novel tools, algorithms, databases, and models—through the lens of robust machine learning—in a research effort to solve large-scale societal problems affecting millions of people in the areas of cybersecurity and healthcare. (1) Tools: We develop TIGER, the first comprehensive graph robustness toolbox; and our ROBUSTNESS SURVEY identifies critical yet missing areas of graph robustness research. (2) Algorithms: Our survey and toolbox reveal existing work has overlooked lateral attacks on computer authentication networks. We develop D2M, the first algorithmic framework to quantify and mitigate network vulnerability to lateral attacks by modeling lateral attack movement from a graph theoretic perspective. (3) Databases: To prevent lateral attacks altogether, we develop MALNET-GRAPH, the world’s largest cybersecurity graph database—containing over 1.2M graphs across 696 classes—and show the first large-scale results demonstrating the effectiveness of malware detection through a graph medium. We extend MALNET-GRAPH by constructing the largest binary-image cybersecurity database—containing 1.2M images, 133×more images than the only other public database—enabling new discoveries in malware detection and classification research restricted to a few industry labs (MALNET-IMAGE). (4) Models: To protect systems from adversarial attacks, we develop UNMASK, the first model that flags semantic incoherence in computer vision systems, which detects up to 96.75% of attacks, and defends the model by correctly classifying up to 93% of attacks. Inspired by UNMASK’s ability to protect computer visions systems from adversarial attack, we develop REST, which creates noise robust models through a novel combination of adversarial training, spectral regularization, and sparsity regularization. In the presence of noise, our method improves state-of-the-art sleep stage scoring by 71%—allowing us to diagnose sleep disorders earlier on and in the home environment—while using 19× less parameters and 15×less MFLOPS. Our work has made significant impact to industry and society: the UNMASK framework laid the foundation for a multi-million dollar DARPA GARD award; the TIGER toolbox for graph robustness analysis is a part of the Nvidia Data Science Teaching Kit, available to educators around the world; we released MALNET, the world’s largest graph classification database with 1.2M graphs; and the D2M framework has had major impact to Microsoft products, inspiring changes to the product’s approach to lateral attack detection.Ph.D