Jinwoo Kim (KAIST), Eduard Marin (Telefonica Research (Spain)), Mauro Conti (University of Padua), Seungwon Shin (KAIST)

Path tracing tools, such as traceroute, are simple yet fundamental network debugging tools for network operators to detect and fix network failures. Unfortunately, adversaries can also use such tools to retrieve previously unknown network topology information which is key to realizing sophisticated Denial-of-Service attacks, such as Link Flooding Attacks (LFAs), more efficiently. Over the last few years, several network obfuscation defenses have been proposed to proactively mitigate LFAs by exposing virtual (fake) topologies that conceal potential bottleneck network links from adversaries. However, to date there has been no comprehensive and systematic analysis of the level of security and utility their virtual topologies offer. A critical analysis is thus a necessary step towards better understanding their limitations and building stronger and more practical defenses against LFAs.

In this paper, we first conduct a security analysis of the three state-of-the-art network obfuscation defenses. Our analysis reveals four important, common limitations that can significantly decrease the security and utility of their virtual topologies. Motivated by our findings, we present EqualNet, a secure and practical proactive defense for long-term network topology obfuscation that alleviates LFAs within a network domain. EqualNet aims to equalize tracing flow distributions over nodes and links so that adversaries are unable to distinguish which of them are the most important ones, thus significantly increasing the cost of performing LFAs. Meanwhile, EqualNet preserves subnet information, helping network operators who use path tracing tools to debug their networks. To demonstrate its feasibility, we implement a full prototype of it using Software-Defined Networking (SDN) and perform extensive evaluations both in software and hardware. Our results show that EqualNet is effective at equalizing the tracing flow distributions of small, medium and large networks even when only a small number of routers within the network support SDN. Finally, we analyze the security of EqualNet against a wide variety of attacks.

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] => 55 ) ) ) [post__not_in] => Array ( [0] => 8486 ) )

WIP: Interrupt Attack on TEE-protected Robotic Vehicles

Mulong Luo (Cornell University) and G. Edward Suh (Cornell University)

Read More

Uncovering Cross-Context Inconsistent Access Control Enforcement in Android

Hao Zhou (The Hong Kong Polytechnic University), Haoyu Wang (Beijing University of Posts and Telecommunications), Xiapu Luo (The Hong Kong Polytechnic University), Ting Chen (University of Electronic Science and Technology of China), Yajin Zhou (Zhejiang University), Ting Wang (Pennsylvania State University)

Read More

Phishing awareness and education – When to best remind?

Benjamin Maximilian Berens (SECUSO, Karlsruhe Institute of Technology), Katerina Dimitrova, Mattia Mossano (SECUSO, Karlsruhe Institute of Technology), Melanie Volkamer (SECUSO, Karlsruhe Institute of Technology)

Read More

Cross-Language Attacks

Samuel Mergendahl (MIT Lincoln Laboratory), Nathan Burow (MIT Lincoln Laboratory), Hamed Okhravi (MIT Lincoln Laboratory)

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)