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| 3. |
- Almgren, Magnus, 1972, et al.
(författare)
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Cybersecurity in the Smart Grid
- 2013
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Ingår i: Chalmers Energy Conference 2013.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Almgren, Magnus, 1972, et al.
(författare)
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Detection of intrusions and malware, and vulnerability assessment
- 2015
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319205496 ; 9148
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Konferensbidrag (refereegranskat)
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| 5. |
- Bodunov, Oleh, et al.
(författare)
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The DEBS 2019 grand challenge
- 2019
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Ingår i: DEBS 2019 - Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems. - New York, NY, USA : ACM. ; , s. 205-208
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Konferensbidrag (refereegranskat)abstract
- The ACM DEBS 2019 Grand Challenge is the ninth in a series of challenges which seek to provide a common ground and evaluation criteria for a competition aimed at both research and industrial event-based systems. The focus of the 2019 Grand Challenge is on the application of machine learning to LiDAR data. The goal of the challenge is to perform classification of objects found in urban environments and sensed in several 3D scenes by the LiDAR. The applications of LIDAR and object detection go well beyond autonomous vehicles and are suitable for use in agriculture, waterway maintenance and flood prevention, and construction. This paper describes the specifics of the data streams provided in the challenge as well as the benchmarking platform that supports the testing of corresponding solutions.
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| 6. |
- Bortolussi, Luca, et al.
(författare)
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Automatic Translation of Spatio-Temporal Logics to Streaming-Based Monitoring Applications for IoT-Equipped Autonomous Agents
- 2019
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Ingår i: M4IoT 2019 - Proceedings of the 2019 Workshop on Middleware and Applications for the Internet of Things, Part of Middleware 2019 Conference. - New York, NY, USA : ACM. ; , s. 7-12
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Konferensbidrag (refereegranskat)abstract
- Environments in which IoT-equipped autonomous agents and humans tightly interact require safety rules that monitor the agents' behaviors. In this context, expressive and human-comprehensible rules based on Spatio-Temporal Logics (STLs) are desirable because they are informative and easy to maintain. Unfortunately, STLs usually build on ad-hoc platforms implementing the logic semantics. We tackle this limitation with a mechanism to transparently compile STL rules to monitoring applications composed of standard data streaming operators, thus opening up the use of high-throughput and low-latency Stream Processing Engines for monitoring rule compliance in realistic, data-rich IoT scenarios. Our contribution can favor a broader and faster adoption of STLs for IoT-equipped agent monitoring by separating the concerns of designing a rule from those of implementing its semantics. Together with our formal description of how to translate STLs to the streaming domain, we evaluate our prototype implementation based on Apache Flink, studying the effects of parameters such as time and space resolution on the monitoring performance.
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| 7. |
- Botev, Viktor, 1987, et al.
(författare)
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Detecting Non-Technical Energy Losses through Structural Periodic Patterns in AMI data
- 2016
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Ingår i: BDSG/BigData: Proceedings of the Workshop on Big Data in Smart Grids at the IEEE International Conference on BigData. ; , s. 3121-3130
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Konferensbidrag (refereegranskat)abstract
- The introduction of Advanced Metering Infrastructures in electricity networks brings new means of dealing with issues influencing financial margins and system-safety problems, thanks to the information reported continuously by smart meters. Such an issue is the detection of Non-Technical Losses (NTLs) in electric power grids. We introduce a data-driven method, called Structure&Detect, to identify possible sources of NTLs; the method is based on spectral analysis of structural periodic patterns in consumption traces, that allows for scalable processing, using features in the frequency domain. Structure&Detect uses only on consumption traces, with no need for exogenous data about customers (e.g., trust or credit history) or explicit information from domain experts. As such, it complies better with privacy concerns that may be present when processing data from different sources. Using real-world consumption traces, we show that it provides high accuracy and detection rates comparable to methods that require additional, customer-specific information. Moreover, Structure&Detect can also be used orthogonally due to its high detection rate, as a filter, providing a narrowed-down input set to methods requiring different treatment (e.g. additional data or on-site inspection) and thus make the search for NTLs more scalable. Structure&Detect also enables processing each meter trace on-the-fly, as well as in a parallel and distributed fashion. These properties make Structure& Detect suitable for online analysis that can address common big data challenges such as the need for scalable, distributed and parallel analysis close to IoT edge devices, such as smart meters.
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| 8. |
- Butun, Ismail, 1981, et al.
(författare)
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Intrusion Detection in Industrial Networks via Data Streaming
- 2020
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Ingår i: Industrial IoT: Challenges, Design Principles, Applications, and Security. - Cham : Springer International Publishing. ; , s. 213-238
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations. In this chapter we explain that stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems). We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment.
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| 9. |
- Callau-Zori, Mar, et al.
(författare)
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STONE: A stream-based DDoS defense framework
- 2013
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Ingår i: Proceedings of the ACM Symposium on Applied Computing, SAC 2013; Coimbra; Portugal; 18 March 2013 through 22 March 2013. - New York, NY, USA : ACM. - 9781450316569 ; , s. 807-812
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Konferensbidrag (refereegranskat)abstract
- An effective Distributed Denial of Service (DDoS) defense mechanism must guarantee legitimate users access to an Internet service masking the effects of possible attacks. That is, it must be able to detect threats and discard malicious packets in a online fashion. Given that emerging data streaming technology can enable such mitigation in an effective manner, in this paper we present STONE, a stream-based DDoS defense framework, which integrates anomaly-based DDoS detection and mitigation with scalable data streaming technology. With STONE, the traffic of potential targets is analyzed via continuous data streaming queries maintaining information used for both attack detection and mitigation. STONE provides minimal degradation of legitimate users traffic during DDoS attacks and it also faces effectively flash crowds. Our preliminary evaluation based on an implemented prototype and conducted with real legitimate and malicious traffic traces shows that STONE is able to provide fast detection and precise mitigation of DDoS attacks leveraging scalable data streaming technology.
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| 10. |
- Cederman, Daniel, 1981, et al.
(författare)
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Brief announcement: Concurrent data structures for efficient streaming aggregation
- 2014
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Ingår i: Annual ACM Symposium on Parallelism in Algorithms and Architectures. - New York, NY, USA : ACM. - 9781450328210 ; , s. 76-78
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Konferensbidrag (refereegranskat)abstract
- We briefly describe our study on the problem of streaming multiway aggregation [5], where large data volumes are received from multiple input streams. Multiway aggregation is a fundamental computational component in data stream management systems, requiring low-latency and high throughput solutions. We focus on the problem of designing concurrent data structures enabling for low-latency and highthroughput multiway aggregation; an issue that has been overlooked in the literature. We propose two new concurrent data structures and their lock-free linearizable implementations, supporting both order-sensitive and order-insensitive aggregate functions. Results from an extensive evaluation show significant improvement in the aggregation performance, in terms of both processing throughput and latency over the commonly-used techniques based on queues.
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