University of Oregon and Computer and Information Science department were selected as one of the first partners for Ripple’s University Blockchain Research Initiative (UBRI). Ripple's philanthropic gift (2018-2023) provides scholarships, faculty fellowships, research support, industry engagement, and supports the Oregon Blockchain Student Club. This year, the initiative recognised three CIS Faculty, Jun Li, Ramakrishnan Durairajan and Lei Jiao with a one-year Ripple Fellowship. The title and summary of the proposed research projects by these Ripple Fellows are as follows.
Cryptocurrency Risk and Tracing Analysis (Jun Li)
This research targets the detection of cyber crimes toward cryptocurrency such as scams (e.g., Ponzi schemes) and money laundering. Cryptocurrencies, such as Bitcoin, Ethereum, Ripple XRP, Monero, Zcash, Dash, have become popular for digital transactions on the Internet. In 2019, 18% of all Americans purchased cryptocurrency and retailers such as Amazon and Starbucks now accept payments in Bitcoin. Unfortunately, cryptocurrencies have also quickly become both a target and tool of cyber crimes. As cyber criminals follow the money, they now see an opportunity in the largely unregulated and insecure world of ;cryptocurrency, and study has found that criminals netted $4.3 billion from digital currency exchanges, investors, and users in 2019. Cyber criminals also resort to using cryptocurrencies for money laundering; they can stay unidentified and transfer cryptocurrencies across borders without close monitoring and detection. For example, in 2018, Europe witnessed $5.5 billion in money laundering via cryptocurrencies. Sponsored by the Ripple faculty fellowship, this research aims to develop a novel approach to addressing critical missing gaps in detecting cyber crimes toward cryptocurrency. This includes detecting cryptocurrency scam accounts from cryptocurrency transactions and smart contracts, as well as detecting money laundering activities from transaction graphs derived using various transaction tracing techniques. The team further includes Prof. Yingjiu (Joe) Li from CIS, Prof. Bryce Newell (School of Journalism and Communication), and Leo Howell who is the Chief Information Security Officer of UO, all affiliated with the Center for Cyber Security and Privacy (CCSP). Both undergraduate and graduate students who are interested in this research are more than welcome to contact Prof. Jun Li at email@example.com. The team is recruiting.
Optics-enabled Network Defenses for Extreme Terabit DDoS Attacks (Ramakrishnan Durairajan)
Distributed denial-of-service (DDoS) attacks are on the rise. For example, a leading cryptocurrency exchange, Mt. Gox, saw its market share fall due to 34 DDoS attacks. On several occasions, DDoS attacks have effected the value of bitcoin following the incident. Of course, DDoS defense is not a new problem and prior work has made significant progress in devising mitigation strategies to tackle DDoS attacks including packet scrubbing solutions, in-network filtering, to more recent SDN/NFV-based elastic defenses. Despite these advances, the rise of new-age extreme terabit attacks mandates a critical rethinking of DDoS defense strategies. In this project, we pursue a new opportunity for bolstering our DDoS defense arsenal by leveraging recent advances in the optical networking community. A specific technology push we propose to exploit here is the ability to program the optical layer: by connecting the optical layer to DDoS defense, we seek to (a) enhance the performance of normal traffic during large-scale attacks by opportunistic reconfiguration of wavelengths, and (b) defend against advanced/future infrastructure attacks by creating new dynamic topology adaptation capabilities. This is a joint work with Prof. Vyas Sekar from CMU.
Securing Enterprise Digital Infrastructures via Online Coordinated Cloud-Edge Traffic Scrubbing (Lei Jiao)
An effective approach to mitigate volumetric traffic attacks, traffic scrubbing refers to routing the suspicious traffic to dedicated scrubbing centers for investigation, where the malicious traffic is blocked or dropped and the legitimate traffic is injected back to the network and continues to flow as normal. This project studies the scheduling problem of dynamically scrubbing large-scale distributed suspicious traffic to protect enterprise digital infrastructures through both the powerful cloud scrubbing centers which reside in remote locations and the often-limited edge scrubbing facilities in closer proximity to victims. The goal of this project is to design, analyze, implement, and evaluate a key set of intelligent control algorithms in order to automate the process of making the scheduling decisions of traffic scrubbing from the victim’s perspective, while providing rigorous performance guarantees provable via the mathematics of optimization. The project aims to deliver readily-deployable algorithm implementations, and will demonstrate the feasibility, the efficacy, and the cost benefit of joint cloud and edge scrubbing that are used in an informed and strategic manner. The project will have an immediate and direct impact on enterprises that use or consider using scrubbing centers to mitigate malicious traffic, and can help them test, adopt, and tune the proposed solutions to secure their infrastructures. The novel methods, algorithms, and analytics that are developed in this project will be rooted in fundamental theories and expected to be applicable to a wider range of problems further beyond the enterprise security scenario, including relevant problems in telecommunication networks and services. This research is currently projected to involve one or multiple international collaborators, such as students and researchers from the Chinese Academy of Sciences, among others.