An efficient solution to the <i>subset‐sum problem</i> on GPU

Curtis, Vitor Venceslau; Sanches, Carlos Alberto Alonso

doi:10.1002/cpe.3636

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Seeking a more flexible algorithm, we explored combinatorial optimization techniques for analyzing packet volumes on a time series. In particular, we started by applying the original subset sum [20] algorithm to align packet volumes on both client and onion service sides on a per-request granularity. That is, rather than employing our current sliding window algorithm to match packet sizes for the entire flow pair, we first estimated which packets within the flows correspond to individual requests (e.g., an HTTP GET request to a web page), segregated all the requests of each flow, and tried to match a request sent by the client with a request received by the onion service.…”

Section: H Explored Approaches Before Converging On Sumomentioning

confidence: 99%

“…The inspiration for our attack stems from our approach to correlate flow pairs. Instead of relying on deep neural networks as in DeepCorr or DeepCoFFEA, we adapted a variation of the well-known NP-complete decision problem, the subset sum [20], and used the absolute packet arrival times to perform flow correlation. We model the packets received by clients and transmitted by onion services as a bounded time series, which is the reason for SUMo's notable efficacy at identifying patterns.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum

Lopes,

Dong,

Castro

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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Tor is one of the most popular anonymity networks in use today. Its ability to defend against flow correlation attacks is essential for providing strong anonymity guarantees. However, the feasibility of flow correlation attacks against Tor onion services (formerly known as "hidden services") has remained an open challenge. In this paper, we present an effective flow correlation attack that can deanonymize onion service sessions in the Tor network. Our attack is based on a novel distributed technique named Sliding Subset Sum (SUMo), which can be deployed by a group of colluding ISPs worldwide in a federated fashion. These ISPs collect Tor traffic at multiple vantage points in the network, and analyze it through a pipelined architecture based on machine learning classifiers and a novel similarity function based on the classic subset sum decision problem. These classifiers enable SUMo to deanonymize onion service sessions effectively and efficiently. We also analyze possible countermeasures that the Tor community can adopt to hinder the efficacy of these attacks.

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Section: H Explored Approaches Before Converging On Sumomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum

Lopes,

Dong,

Castro

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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“…In one of our most promising attempts so far, we start by fixing the initial and final absolute times of a client request and split the received packets during this time interval into buckets of 500 ms each. We do the same splitting for the packets sent on the OS side and apply an adapted version of the known NP decision problem Subset Sum [10]. Since we apply it on a bounded time series, it is particularly effective at matching patterns between the packets received by the clients and packets sent by the OS.…”

Section: Technical Approachmentioning

confidence: 99%

Poster

Lopes

Medeiros

Dong

et al. 2022

Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security

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Tor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy.

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“…The parallel solving of the subset sum problem was also actively studied in literature (see, e.g., References 6–10). Special attention was paid to GPU implementation for the parallel solving of the subset sum problem (see, e.g., References 11–15). For these reasons, the question of parallel realization of Balsub is of great interest.…”

Section: Introductionmentioning

confidence: 99%