Zhizheng Lv

dblp:259/0023 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 since 2021
YearPublicationVenuePosition
2022 PACMem: Enforcing Spatial and Temporal Memory Safety via ARM Pointer Authentication
abstract
Memory safety is a key security property that stops memory corruption vulnerabilities. Different types of memory safety enforcement solutions have been proposed and adopted by sanitizers or mitigations to catch and stop such bugs, at the development or deployment phase. However, existing solutions either provide partial memory safety or have overwhelmingly high performance overheads.
Yuan Li 0061, Wende Tan, Zhizheng Lv, Songtao Yang 0001, Mathias Payer, Ying Liu 0024, Chao Zhang 0008
CCS3
2021 Deterrence of Intelligent DDoS via Multi-Hop Traffic Divergence
abstract
We devise a simple, provably effective, and readily usable deterrence against intelligent, unknown DDoS threats: Demotivate adversaries to launch attacks via multi-hop traffic divergence. This new strategy is motivated by the fact that existing defenses almost always lag behind numerous emerging DDoS threats and evolving intelligent attack strategies. The root cause is if adversaries are smart and adaptive, no single-hop defenses (including optimal ones) can perfectly differentiate unknown DDoS and legitimate traffic. Instead, we formulate intelligent DDoS as a game between attackers and defenders, and prove how multi-hop traffic divergence helps bypass this dilemma by reversing the asymmetry between attackers and defenders. This insight results in EID, an Economical Intelligent DDoS Demotivation protocol. EID combines local weak (yet divergent) filters to provably null attack gains without knowing exploited vulnerabilities or attack strategies. It incentivizes multi-hop defenders to cooperate with boosted local service availability. EID is resilient to traffic dynamics and manipulations. It is readily deployable with random-drop filters in real networks today. Our experiments over a 49.8 TB dataset from a department at the Tsinghua campus network validate EID's viability against rational and irrational DDoS with negligible costs.
Yuanjie Li, Hewu Li, Zhizheng Lv, Xingkun Yao, Qianru Li 0002
CCS3