一位网友告诉笔者可以下载一些精彩电影，但是网友并没有告诉笔者电影的下载地址，而是用QQ传来一个只有20KB大小的torrent类型文件，让笔者用BitTorrent下载。笔者看着这个torrent文件顿时傻了眼：难道这就是我要的电影？什么又是BitTorrent？

最“变态”的下载：BitTorrent

We introduce a new sublinear space data structure--the count-min sketch--for summarizing data streams. Our sketch allows fundamental queries in data stream summarization such as point, range, and inner product queries to be approximately answered very quickly; in addition, it can be applied to solve several important problems in data streams such as finding quantiles, frequent items, etc. The time and space bounds we show for using the CM sketch to solve these problems significantly improve those previously known--typically from 1/e2 to 1/e in factor.

An improved data stream summary: The Count-Min Sketch and its applications

We present CCF, a framework to build permissioned confidential blockchains. CCF provides a simple programming model of a highly-available data store and a universally-verifiable log that implements a ledger abstraction. CCF leverages trust in a consortium of governing members and in a network of replicated hardware-protected execution environments to achieve high throughput, low latency, strong integrity and strong confidentiality for application data and code executing on the ledger. CCF embeds consensus protocols with Byzantine and crash faulttolerant configurations. All configurations support strong service integrity based on the ledger contents. Even if some replicas are corrupt or their keys are compromised, they can be blamed based on their signed evidence of malicious activity recorded in the ledger. CCF supports transparent, programmable governance where the power of the consortium members is tunable and their activity is similarly recorded in the ledger for full auditability. We are developing an open-source implementation of CCF based on SGX-enabled Azure Confidential Compute, built on top of the Open Enclave SDK. Experimental results show that this implementation achieves throughput/latency tradeoffs up to 3 orders of magnitude better than previous confidential blockchain designs. Its code and documentation are available at https://github.com/Microsoft/CCF.

CCF: A Framework for Building Confidential Verifiable Replicated Services

Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone -- the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and application features. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models.

Keystone: An Open Framework for Architecting TEEs

Amplification Distributed Denial of Service (DDoS) attacks' traffic and harm are at an all-time high. To defend against such attacks, distributed attack mitigation platforms, such as traffic scrubbing centers that operate in peering locations, e.g., Internet Exchange Points (IXP), have been deployed in the Internet over the years. These attack mitigation platforms apply sophisticated techniques to detect attacks and drop attack traffic locally, thus, act as sensors of attacks. However, it has not yet been systematically evaluated and reported to what extent coordination of these views by different platforms can lead to more effective mitigation of amplification DDoS attacks. In this paper, we ask the question: "Is it possible to mitigate more amplification attacks and drop more attack traffic when distributed attack mitigation platforms collaborate?" To answer this question, we collaborate with eleven IXPs that operate in three different regions. These IXPs have more than 2,120 network members that exchange traffic at the rate of more than 11 Terabits per second. We collect network data over six months and analyze more than 120k amplification DDoS attacks. To our surprise, more than 80% of the amplification DDoS are not detected locally, although the majority of the attacks are visible by at least three IXPs. A closer investigation points to the shortcomings, such as the multi-protocol profile of modern amplification attacks, the duration of the attacks, and the difficulty of setting appropriate local attack traffic thresholds that will trigger mitigation. To overcome these limitations, we design and evaluate a collaborative architecture that allows participant mitigation platforms to exchange information about ongoing amplification attacks. Our evaluation shows that it is possible to collaboratively detect and mitigate the majority of attacks with limited exchange of information and drop as much as 90% more attack traffic locally.

United We Stand: Collaborative Detection and Mitigation of Amplification DDoS Attacks at Scale

In light of ever-increasing scale and sophistication of modern distributed denial-of-service (DDoS) attacks, recent proposals show that in-network filtering of DDoS traffic at a handful of transit networks can handle volumetric attacks effectively. In this paper, we identify a subtle but important security risk in existing in-network filtering proposals. That is, a transit network may use the in-network filtering services as an excuse for any arbitrary packet drops made for its own benefit. For example, a malicious transit network may execute any filtering rules to discriminate against some of its neighboring networks based on its business preference while claiming that it is for the purpose of DDoS defense. We argue that this is due to the lack of verifiable filtering—i.e., no single party can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, we propose a verifiable in-network filtering system, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighboring networks. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the good candidates to be early adopters of our verifiable filters due to their central locations and flexible software-defined architecture. Our large-scale simulations of two realistic attacks (i.e., DNS amplification, Mirai-based flooding) show that adopting VIF filtering service at only a small number (e.g., 5-25) of large IXPs is sufficient to handle the majority (e.g., up to 80-90%) of DDoS traffic.

Practical Verifiable In-network Filtering for DDoS Defense

In light of ever-increasing scale and sophistication of modern DDoS attacks, it is time to revisit in-network filtering or the idea of empowering DDoS victims to install in-network traffic filters in the upstream transit networks. Recent proposals show that filtering DDoS traffic at a handful of large transit networks can handle volumetric DDoS attacks effectively. However, the innetwork filtering primitive can also be misused. Transit networks can use the in-network filtering service as an excuse for any arbitrary packet drops made for their own benefit. For example, transit networks may intentionally execute filtering services poorly or unfairly to discriminate their competing neighbor ASes while claiming that they drop packets for the sake of DDoS defense. We argue that it is due to the lack of verifiable filtering - i.e., no one can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, in this paper, we propose a verifiable in-network filtering, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighbor ASes. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can easily scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the ideal candidates for the early adopters of our verifiable filters due to their central locations and flexible software-defined architecture.

Practical Verifiable In-network Filtering for DDoS defense

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

Robust P2P Primitives Using SGX Enclaves

In Proceedings of IEEE International Conference on Distributed Computing Systems (IEEE ICDCS), July 2019

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widelystudied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages. In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Second, we present new protocols that improve the communication complexity of two fundamental primitives — reliable broadcast and common random coins (or beacons) — over the best-known results in the synchronous general-omission model, by utilizing SGX features. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

Deli Gong

Papers

Practical Verifiable In-network Filtering for DDoS Defense

Practical Verifiable In-network Filtering for DDoS defense

Robust P2P Primitives Using SGX Enclaves

Practical Verifiable In-network Filtering for DDoS defense

Robust Synchronous P2P Primitives Using SGX Enclaves.