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new conference paper “Efficient Processing of Streaming Data using Multiple Abstractions” at IEEE Cloud

We have published a new paper “Efficient Processing of Streaming Data using Multiple Abstractions” at the IEEE Cloud 2021 conference. (to be available at https://conferences.computer.org/cloud/2021/)

We show that one framework can efficiently support multiple abstractions. We provide three abstractions of Block, Windowed, and Stateful streaming and demonstrate that many application classes can be developed with ease, correctness, and low processing latency.

From the abstract of our paper:

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions—these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

This conference paper is joint work of Abdul Qadeer and  John Heidemann from USC/ISI.

Plumb is open source software and will be available at: https://ant.isi.edu/software/plumb/index.html

Update 2021-09-26: This paper was given a “special paper award” at IEEE Conference on Cloud Computing 2021! Congratulations, Abdul!

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new journal paper “Detecting IoT Devices in the Internet” in IEEE/ACM Transactions on Networking

We have published a new journal paper “Detecting IoT Devices in the Internet” in IEEE/ACM Transactions on Networking, available at https://www.isi.edu/~johnh/PAPERS/Guo20c.pdf

Figure 5 from [Guo20c] showing per-device-type AS penetrations from 2013 to 2018 for 16 of the 23 device types we studies (omitting 7 device types appearing in less than10 ASes)

From the abstract of our journal paper:

Distributed Denial-of-Service (DDoS) attacks launched from compromised Internet-of-Things (IoT) devices have shown how vulnerable the Internet is to largescale DDoS attacks. To understand the risks of these attacks requires learning about these IoT devices: where are they? how many are there? how are they changing? This paper describes three new methods to find IoT devices on the Internet: server IP addresses in traffic, server names in DNS queries, and manufacturer information in TLS certificates. Our primary methods (IP addresses and DNS names) use knowledge of servers run by the manufacturers of these devices. Our third method uses TLS certificates obtained by active scanning. We have applied our algorithms to a number of observations. With our IP-based algorithm, we report detections from a university campus over 4 months and from traffic transiting an IXP over 10 days. We apply our DNS-based algorithm to traffic from 8 root DNS servers from 2013 to 2018 to study AS-level IoT deployment. We find substantial growth (about 3.5×) in AS penetration for 23 types of IoT devices and modest increase in device type density for ASes detected with these device types (at most 2 device types in 80% of these ASes in 2018). DNS also shows substantial growth in IoT deployment in residential households from 2013 to 2017. Our certificate-based algorithm finds 254k IP cameras and network video recorders from 199 countries around the world.

We make operational traffic we captured from 10 IoT devices we own public at https://ant.isi.edu/datasets/iot/. We also use operational traffic of 21 IoT devices shared by University of New South Wales at http://149.171.189.1/.

This journal paper is joint work of Hang Guo and  John Heidemann from USC/ISI.

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new workshop paper “Privacy Principles for Sharing Cyber Security Data” in IWPE 15

The paper “Privacy Principles for Sharing Cyber Security Data” (available at https://www.isi.edu/~calvin/papers/Fisk15a.pdf) will appear at the International Workshop on Privacy Engineering (co-located with IEEE Symposium on Security and Privacy) on May 21, 2015 in San Jose, California.

From the abstract:

Sharing cyber security data across organizational boundaries brings both privacy risks in the exposure of personal information and data, and organizational risk in disclosing internal information. These risks occur as information leaks in network traffic or logs, and also in queries made across organizations. They are also complicated by the trade-offs in privacy preservation and utility present in anonymization to manage disclosure. In this paper, we define three principles that guide sharing security information across organizations: Least Disclosure, Qualitative Evaluation, and Forward Progress. We then discuss engineering approaches that apply these principles to a distributed security system. Application of these principles can reduce the risk of data exposure and help manage trust requirements for data sharing, helping to meet our goal of balancing privacy, organizational risk, and the ability to better respond to security with shared information.

The work in the paper is by Gina Fisk (LANL), Calvin Ardi (USC/ISI), Neale Pickett (LANL), John Heidemann (USC/ISI), Mike Fisk (LANL), and Christos Papadopoulos (Colorado State). This work is supported by DHS S&T, Cyber Security division.