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Dehlaghi Ghadim, A., Helali Moghadam, M., Balador, A. & Hansson, H. (2023). Anomaly Detection Dataset for Industrial Control Systems. IEEE Access, 11, 107982-107996
Open this publication in new window or tab >>Anomaly Detection Dataset for Industrial Control Systems
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 107982-107996Article in journal (Refereed) Published
Abstract [en]

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the ’ICS-Flow’ dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, ’ICSFlowGenerator,’ for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Data mining; Decision trees; Feature extraction; Integrated circuits; Intrusion detection; Learning systems; Network security; Neural networks; Open systems; Anomaly detection; Anomaly detection dataset; Cyber-attacks; Features extraction; Industrial control systems; Integrated circuit modeling; Intrusion-Detection; Networks flows; Telecommunications traffic; Computer crime
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-67715 (URN)10.1109/ACCESS.2023.3320928 (DOI)2-s2.0-85173045898 (Scopus ID)
Funder
EU, Horizon 2020
Note

This work has been partially supported by the H2020 ECSEL EU project Intelligent Secure Trustable Things (InSecTT).

Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-11-06Bibliographically approved
Mohammadi, S., Mohammadi, M., Sinaei, S., Balador, A., Nowroozi, E., Flammini, F. & Conti, M. (2023). Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition. Paper presented at 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS). ACSIS Annals of Computer Science and Information Systems, 35, 191-199
Open this publication in new window or tab >>Balancing Privacy and Accuracy in Federated Learning for Speech Emotion Recognition
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2023 (English)In: ACSIS Annals of Computer Science and Information Systems, Vol. 35, p. 191-199Article in journal (Refereed) Published
Abstract [en]

Context: Speech Emotion Recognition (SER) is a valuable technology that identifies human emotions from spoken language, enabling the development of context-aware and personalized intelligent systems. To protect user privacy, Federated Learning (FL) has been introduced, enabling local training of models on user devices. However, FL raises concerns about the potential exposure of sensitive information from local model parameters, which is especially critical in applications like SER that involve personal voice data. Local Differential Privacy (LDP) has prevented privacy leaks in image and video data. However, it encounters notable accuracy degradation when applied to speech data, especially in the presence of high noise levels. In this paper, we propose an approach called LDP-FL with CSS, which combines LDP with a novel client selection strategy (CSS). By leveraging CSS, we aim to improve the representatives of updates and mitigate the adverse effects of noise on SER accuracy while ensuring client privacy through LDP. Furthermore, we conducted model inversion attacks to evaluate the robustness of LDP-FL in preserving privacy. These attacks involved an adversary attempting to reconstruct individuals' voice samples using the output labels provided by the SER model. The evaluation results reveal that LDP-FL with CSS achieved an accuracy of 65-70%, which is 4% lower than the initial SER model accuracy. Furthermore, LDP-FL demonstrated exceptional resilience against model inversion attacks, outperforming the non-LDP method by a factor of 10. Overall, our analysis emphasizes the importance of achieving a balance between privacy and accuracy in accordance with the requirements of the SER application.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-68535 (URN)10.15439/2023F444 (DOI)
Conference
2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS)
Note

This work was partially supported by EU ECSEL projectDAIS that has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No.101007273.

Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved
Balador, A., Sinaei, S., Pettersson, M. & Kaya, I. (2023). DAIS Project - Distributed Artificial Intelligence Systems: Objectives and Challenges. ACM SIGAda Ada Letters, 42(2), 96-98
Open this publication in new window or tab >>DAIS Project - Distributed Artificial Intelligence Systems: Objectives and Challenges
2023 (English)In: ACM SIGAda Ada Letters, ISSN 1094-3641, E-ISSN 1557-9476, Vol. 42, no 2, p. 96-98Article in journal (Refereed) Published
Abstract [en]

DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
dais, federated learning, kdt ju, eu project, edge computing, distributed ai
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-65763 (URN)10.1145/3591335.3591348 (DOI)
Available from: 2023-08-14 Created: 2023-08-14 Last updated: 2023-08-21Bibliographically approved
Dehlaghi Ghadim, A., Balador, A., Helali Moghadam, M., Hansson, H. & Conti, M. (2023). ICSSIM — A framework for building industrial control systems security testbeds. Computers in industry (Print), 148, Article ID 103906.
Open this publication in new window or tab >>ICSSIM — A framework for building industrial control systems security testbeds
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2023 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 148, article id 103906Article in journal (Refereed) Published
Abstract [en]

With the advent of the smart industry, Industrial Control Systems (ICS) moved from isolated environments to connected platforms to meet Industry 4.0 targets. The inherent connectivity in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems (IDS) empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research IDSs due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their IDSs in a controlled environment. Although various ICS testbeds have been developed, researchers’ access to a low-cost, extendable, and customizable testbed that can accurately simulate ICSs and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds in which various cyber threats and network attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. Simulated components are deployable on actual hardware such as Raspberry Pis, containerized environments like Docker, and simulation environments such as GNS-3. ICSSIM also offers physical process modeling using software and hardware in the loop simulation. This framework reduces the time for developing ICS components and aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. We demonstrate ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied. © 2023 The Authors

Place, publisher, year, edition, pages
Elsevier B.V., 2023
Keywords
Cyberattack, Cybersecurity, Industrial control system, Network emulation, Testbed, Computer crime, Control systems, Costs, Cyber attacks, Intrusion detection, Network security, Abnormal behavior, Control system security, Cyber security, Cyber-attacks, Industrial control systems, Intrusion Detection Systems, Low-costs, Machine-learning, System components, Testbeds
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-64314 (URN)10.1016/j.compind.2023.103906 (DOI)2-s2.0-85151016386 (Scopus ID)
Note

 Correspondence Address: Dehlaghi-Ghadim, A.; RISE Research Institute of Sweden, Sweden; email: alireza.dehlaghi.ghadim@ri.se; Funding details: 876038; Funding details: Horizon 2020 Framework Programme, H2020; Funding details: Horizon 2020; Funding text 1: This work was supported by InSecTT (www.insectt.eu), which received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey, Belgium, Germany, Czech Republic, Denmark, Norway. The document reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information it contains. We would like to thank Westermo AB company for providing us with access to their test environment for conducting experiments on the physical setup.; Funding text 2: This work was supported by InSecTT ( www.insectt.eu ), which received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038 . The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey, Belgium, Germany, Czech Republic, Denmark, Norway. The document reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information it contains.

Available from: 2023-05-05 Created: 2023-05-05 Last updated: 2023-10-30Bibliographically approved
Mohammadi, S., Sinaei, S., Balador, A. & Flammini, F. (2023). Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition. In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC): . Paper presented at IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1021-1022).
Open this publication in new window or tab >>Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition
2023 (English)In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), 2023, p. 1021-1022Conference paper, Published paper (Refereed)
Abstract [en]

Federated Learning is an approach to distributed machine learning that enables collaborative model training on end devices. FL enhances privacy as devices only share local model parameters instead of raw data with a central server. However, the central server or eavesdroppers could extract sensitive information from these shared parameters. This issue is crucial in applications like speech emotion recognition (SER) that deal with personal voice data. To address this, we propose Optimized Paillier Homomorphic Encryption (OPHE) for SER applications in FL. Paillier homomorphic encryption enables computations on ciphertext, preserving privacy but with high computation and communication overhead. The proposed OPHE method can reduce this overhead by combing Paillier homomorphic encryption with pruning. So, we employ OPHE in one of the use cases of a large research project (DAIS) funded by the European Commission using a public SER dataset.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-66346 (URN)10.1109/compsac57700.2023.00156 (DOI)
Conference
IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
Note

This work was partially supported by EU ECSEL project DAIS that has received funding from the ECSEL JU under grant agreement No.101007273.

Available from: 2023-09-06 Created: 2023-09-06 Last updated: 2023-09-06Bibliographically approved
Mohammadi, S., Sinaei, S., Balador, A. & Flammini, F. (2023). Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition. In: Weizhi Meng, Zheng Yan & Vincenzo Piuri (Ed.), ISPEC 2023: Information Security Practice and Experience: International Conference on Information Security Practice and Experience (pp. 1-16). Springer Nature Singapore
Open this publication in new window or tab >>Secure and Efficient Federated Learning by Combining Homomorphic Encryption and Gradient Pruning in Speech Emotion Recognition
2023 (English)In: ISPEC 2023: Information Security Practice and Experience: International Conference on Information Security Practice and Experience / [ed] Weizhi Meng, Zheng Yan & Vincenzo Piuri, Springer Nature Singapore , 2023, p. 1-16Chapter in book (Refereed)
Abstract [en]

Speech Emotion Recognition (SER) detects human emotions expressed in spoken language. SER is highly valuable in diverse fields; however, privacy concerns arise when analyzing speech data, as it reveals sensitive information like biometric identity. To address this, Federated Learning (FL) has been developed, allowing models to be trained locally and just sharing model parameters with servers. However, FL introduces new privacy concerns when transmitting local model parameters between clients and servers, as third parties could exploit these parameters and disclose sensitive information. In this paper, we introduce a novel approach called Secure and Efficient Federated Learning (SEFL) for SER applications. Our proposed method combines Paillier homomorphic encryption (PHE) with a novel gradient pruning technique. This approach enhances privacy and maintains confidentiality in FL setups for SER applications while minimizing communication and computation overhead and ensuring model accuracy. As far as we know, this is the first paper that implements PHE in FL setup for SER applications. Using a public SER dataset, we evaluated the SEFL method. Results show substantial efficiency gains with a key size of 1024, reducing computation time by up to 25% and communication traffic by up to 70%. Importantly, these improvements have minimal impact on accuracy, effectively meeting the requirements of SER applications.

Place, publisher, year, edition, pages
Springer Nature Singapore, 2023
Series
Lecture Notes in Computer Science ; 14341
National Category
Computer Sciences Telecommunications
Identifiers
urn:nbn:se:ri:diva-68451 (URN)10.1007/978-981-99-7032-2_1 (DOI)9789819970315 (ISBN)9789819970322 (ISBN)
Note

This work was partially supported by EU ECSEL project DAIS which has received funding from the ECSEL JU under grant agreement No.101007273.

Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2023-12-13Bibliographically approved
Ebrahimiyan, H., Balador, A. & Nikoui, T. (2022). A Cost-Aware Resource Management Technique for Cloud and Edge Environment. In: MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings: . Paper presented at 21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022, 14 June 2022 through 16 June 2022 (pp. 1165-1170). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A Cost-Aware Resource Management Technique for Cloud and Edge Environment
2022 (English)In: MELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 1165-1170Conference paper, Published paper (Refereed)
Abstract [en]

Fog computing plays an important role to improve the efficiency of time-sensitive applications, cost reduction, and proper data management. In this paper, the above goals are made possible by processing some time-sensitive data and aggregating the rest of the data, by the modules in the fog data manager and associated storage servers, before sending it to the cloud servers. In this paper, we set the optimal workload allocation to the cloud and fog system by considering the compromise between costs of energy consumption of cloud and fog system, transfer bandwidth and revenue losses due to the WAN propagation delay, and time constraints in the Fog and Cloud system. The mixed linear programming obtained by IBM ILOG CPLEX software is solved to achieve the lowest possible cost by optimally allocating loads to fog devices and cloud servers. In addition, we show that using this approach, the total cost can be reduced by about 29%. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
Data management, Fog computing, Mixed linear programming, Optimal workload allocation, Cloud storage, Cost reduction, Energy utilization, Fog, Linear programming, Sensitive data, Application cost, Cloud servers, Cost-aware, Costs reduction, Resource management techniques, Sensitive datas, Time sensitive applications, Workload allocation, Information management
National Category
Control Engineering
Identifiers
urn:nbn:se:ri:diva-60087 (URN)10.1109/MELECON53508.2022.9842978 (DOI)2-s2.0-85136410908 (Scopus ID)9781665442800 (ISBN)
Conference
21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022, 14 June 2022 through 16 June 2022
Available from: 2022-09-09 Created: 2022-09-09 Last updated: 2022-09-09Bibliographically approved
Balador, A., Bazzi, A., Hernandez-Jayo, U., de la Iglesia, I. & Ahmadvand, H. (2022). A survey on vehicular communication for cooperative truck platooning application. Vehicular Communications, 35, Article ID 100460.
Open this publication in new window or tab >>A survey on vehicular communication for cooperative truck platooning application
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2022 (English)In: Vehicular Communications, ISSN 2214-2096, E-ISSN 2214-210X, Vol. 35, article id 100460Article in journal (Refereed) Published
Abstract [en]

Platooning is an application where a group of vehicles move one after each other in close proximity, acting jointly as a single physical system. The scope of platooning is to improve safety, reduce fuel consumption, and increase road use efficiency. Even if conceived several decades ago as a concept, based on the new progress in automation and vehicular networking platooning has attracted particular attention in the latest years and is expected to become of common implementation in the next future, at least for trucks. The platoon system is the result of a combination of multiple disciplines, from transportation, to automation, to electronics, to telecommunications. In this survey, we consider the platooning, and more specifically the platooning of trucks, from the point of view of wireless communications. Wireless communications are indeed a key element, since they allow the information to propagate within the convoy with an almost negligible delay and really making all vehicles acting as one. Scope of this paper is to present a comprehensive survey on connected vehicles for the platooning application, starting with an overview of the projects that are driving the development of this technology, followed by a brief overview of the current and upcoming vehicular networking architecture and standards, by a review of the main open issues related to wireless communications applied to platooning, and a discussion of security threats and privacy concerns. The survey will conclude with a discussion of the main areas that we consider still open and that can drive future research directions. © 2022 The Author(s)

Place, publisher, year, edition, pages
Elsevier Inc., 2022
Keywords
C-V2X, DSRC, Security, Truck platooning, Vehicular networks, Wireless communication
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-58885 (URN)10.1016/j.vehcom.2022.100460 (DOI)2-s2.0-85126065489 (Scopus ID)
Note

Funding details: 876038; Funding details: European Commission, EC, C2017/3-6, C2017/3-8; Funding details: Horizon 2020; Funding text 1: This work was supported by the Celtic-Next projects RELIANCE ( C2017/3-8 ) and Health5G ( C2017/3-6 ), and InSecTT KDT project. InSecTT ( http://www.insectt.eu ) has received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038 . The JU receives support from the European Union's Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey. The document reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains.; Funding text 2: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ali Balador reports financial support was provided by European Commission . Ali Balador reports financial support was provided by Sweden's Innovation Agency .

Available from: 2022-03-30 Created: 2022-03-30 Last updated: 2022-03-30Bibliographically approved
Nikoui, T., Rahmani, A., Balador, A. & Haj Seyyed Javadi, H. (2022). Analytical model for task offloading in a fog computing system with batch-size-dependent service. Computer Communications, 190, 201-215
Open this publication in new window or tab >>Analytical model for task offloading in a fog computing system with batch-size-dependent service
2022 (English)In: Computer Communications, ISSN 0140-3664, E-ISSN 1873-703X, Vol. 190, p. 201-215Article in journal (Refereed) Published
Abstract [en]

Task offloading is one of the main concepts in fog computing which improves the system efficiency and decreases latency. Previously proposed models, such as exponential queue models, addressed the offloading models in a simple model. This study proposes a novel analytical model that examines batch queuing systems and the influence of batch size-dependent service time on system performance. Some of the system's properties are indicated using this model, and the correctness of the suggested model via numerical evaluations and simulations is shown. The evaluation results show that our proposed model provides acceptable accuracy and enables efficient task offloading, applied to fog computing systems. 

Place, publisher, year, edition, pages
Elsevier B.V., 2022
Keywords
Analytical approximation model, Batch processing, Fog computing, Task offloading, Analytical models, Batch data processing, Fog, Analytical approximation, Approximation modeling, Batch sizes, Computing system, Exponentials, Queue models, Size dependent, System efficiency
National Category
Economics
Identifiers
urn:nbn:se:ri:diva-59215 (URN)10.1016/j.comcom.2022.04.010 (DOI)2-s2.0-85129703685 (Scopus ID)
Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-06-10Bibliographically approved
Balador, A., Sinaei, S. & Pettersson, M. (2022). Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things. ERCIM News (129), 41-42
Open this publication in new window or tab >>Artificial Intelligence Enabled Distributed Edge Computing for Internet of Things
2022 (English)In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, no 129, p. 41-42Article in journal (Other academic) Published
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-62439 (URN)
Available from: 2023-01-23 Created: 2023-01-23 Last updated: 2023-04-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4473-7763

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