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Quantum Optimization for IoT Security Detection

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Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (ISAmI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 603))

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Abstract

The digital transformation and innovation of today’s environment requires a focus on the security of systems to preserve their confidentiality, integrity, and availability. Considering these needs, in this work an efficient and high-performance intrusion detection system based on Quantum Annealing is presented. The goal is to identify attacks in an IoT environment such as Smart Home. The goal is to reduce the processing time for identifying attacks and to respond promptly to implement measures appropriate to the threat. We use Qboost, an iterative training algorithm in which a subset of weak classifiers is selected by solving a hard optimization problem in each iteration. The models were tested on a IoT Fridge dataset, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The results show an optimization of computational time and an improvement in detection accuracy.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Universitá di Bari Aldo Moro, Bari, Italy

    Vita Santa Barletta, Danilo Caivano, Mirko De Vincentiis, Alessio Magrì & Antonio Piccinno

Authors
  1. Vita Santa Barletta
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  2. Danilo Caivano
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  3. Mirko De Vincentiis
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  4. Alessio Magrì
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  5. Antonio Piccinno
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Corresponding author

Correspondence to Vita Santa Barletta .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián

  2. ISEP/GECAD, Porto, Portugal

    João Carneiro

  3. Científico Universidad de Salamanca, AIR Institute Villamayor, Parque, Salamanca, Spain

    Ricardo S. Alonso

  4. Edificio I+D+i (24.2), University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  5. Departamento de Informática, University of Minho, Braga, Portugal

    Paulo Novais

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Barletta, V.S., Caivano, D., De Vincentiis, M., Magrì, A., Piccinno, A. (2023). Quantum Optimization for IoT Security Detection. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_18

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