Kai Li (Syracuse University), Jiaqi Chen (Syracuse University), Xianghong Liu (Syracuse University), Yuzhe Tang (Syracuse University), XiaoFeng Wang (Indiana University Bloomington), Xiapu Luo (Hong Kong Polytechnic University)

Modern blockchains have evolved from cryptocurrency substrates to trust-decentralization platforms, supporting a wider variety of decentralized applications known as DApps. Blockchain remote procedure call (RPC) services emerge as an intermediary connecting the DApps to a blockchain network. In this work, we identify the free contract-execution capabilities that widely exist in blockchain RPCs as a vulnerability of denial of service (DoS) and present the DoERS attack, a Denial of Ethereum RPC service that incurs zero Ether cost to the attacker.

To understand the DoERS exploitability in the wild, we conduct a systematic measurement study on nine real-world RPC services which control most DApp clients' connection to the Ethereum mainnet. In particular, we propose a novel measurement technique based on orphan transactions to discover the previously unknown behaviors inside the blackbox RPC services, including load balancing and gas limiting. Further DoERS strategies are proposed to evade the protection intended by these behaviors.

We evaluate the effectiveness of DoERS attacks on deployed RPC services with minimal service interruption. The result shows that all the nine services tested (as of Apr. 2020) are vulnerable to DoERS attacks that can result in the service latency increased by $2.1Xsim{}50X$. Some of these attacks require only a single request. In addition, on a local Ethereum node protected by a very restrictive limit of $0.65$ block gas, sending 150 DoERS requests per second can slow down the block synchronization of the victim node by $91%$.

We propose mitigation techniques against DoERS without dropping service usability, via unpredictable load balancing, performance anomaly detection, and others. These techniques can be integrated into a RPC service transparently to its clients.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 47 ) ) ) [post__not_in] => Array ( [0] => 6888 ) )

Towards Understanding and Detecting Cyberbullying in Real-world Images

Nishant Vishwamitra (University at Buffalo), Hongxin Hu (University at Buffalo), Feng Luo (Clemson University), Long Cheng (Clemson University)

Read More

GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural...

Qiao Zhang (Old Dominion University), Chunsheng Xin (Old Dominion University), Hongyi Wu (Old Dominion University)

Read More

Hunting the Haunter — Efficient Relational Symbolic Execution for...

Lesly-Ann Daniel (CEA, List, France), Sébastien Bardin (CEA, List, France), Tamara Rezk (Inria, France)

Read More

Deceptive Deletions for Protecting Withdrawn Posts on Social Media...

Mohsen Minaei (Visa Research), S Chandra Mouli (Purdue University), Mainack Mondal (IIT Kharagpur), Bruno Ribeiro (Purdue University), Aniket Kate (Purdue University)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)