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- Finne, Niclas, et al.
(författare)
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Multi-Trace : Multi-level Data Trace Generation with the Cooja Simulator
- 2021
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Ingår i: 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665439299 ; , s. 390-395
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Konferensbidrag (refereegranskat)abstract
- Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.
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- Kanwar, John, et al.
(författare)
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JamSense : Interference and Jamming Classification for Low-power Wireless Networks
- 2021
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Ingår i: Proceedings of the 2021 13Th IFIP Wireless and Mobile Networking Conference (WMNC). - : IEEE. - 9783903176423 ; , s. 9-16
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Konferensbidrag (refereegranskat)abstract
- Low-power wireless networks transmit at low output power and are hence susceptible to cross-technology interference. The latter may cause packet loss which may waste scarce energy resources by requiring the retransmission of packets. Jamming attacks are even more harmful than cross-technology interference in that they may totally prevent packet reception and hence disturb or even disrupt applications. Therefore, it is important to recognize such jamming attacks. In this paper, we present JamSense. JamSense extends SpeckSense, a system that is able to detect multiple sources of interference, with the ability to classify jamming attacks. As SpeckSense, JamSense runs on resource-constrained nodes. Our experimental evaluation on real hardware shows that JamSense is able to identify jamming attacks with high accuracy while not classifying Bluetooth or WiFi interference as jamming attacks.
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- Keipour, Hossein, et al.
(författare)
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Generalizing Supervised Learning for Intrusion Detection in IoT Mesh Networks
- 2021
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Ingår i: Commun. Comput. Info. Sci.. - Singapore : Springer Science and Business Media Deutschland GmbH. - 9789811904677 - 9789811904684 ; , s. 214-228
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Konferensbidrag (refereegranskat)abstract
- IoT mesh networks typically consist of resource-constrained devices that communicate wirelessly. Since such networks are exposed to numerous attacks, designing intrusion detection systems is an important task and has attracted a lot of attention from the research community. Most existing work, however, has only considered a few network topologies, often also assuming a fixed number of nodes. In this paper, we generate a new large attack dataset, using Multi-Trace, a tool that we recently devised to generate traces to train machine learning algorithms. We show that using more and more diverse training data, the resulting intrusion detection models generalize better compared to those trained with less and less diverse training data. They even generalize well for larger topologies with more IoT devices. We also show that when we train different machine learning methods on our dataset, the resulting intrusion detection systems achieve very high performance. © 2022, The Author(s),
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- Mottola, Luca, et al.
(författare)
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MakeSense : Simplifying the Integration of Wireless Sensor Networks into Business Processes
- 2019
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Ingår i: IEEE Transactions on Software Engineering. - : Institute of Electrical and Electronics Engineers Inc.. - 0098-5589 .- 1939-3520. ; 45:6, s. 576-596
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Tidskriftsartikel (refereegranskat)abstract
- A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-Also directly usable by WSN developers-And then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-Abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-Abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.
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