| 1. |
- Paladi, Nicolae, et al.
(författare)
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Protecting OpenFlow Flow Tables with Intel SGX
- 2019
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Ingår i: ACM SIGCOMM 2019 Conference on Posters and Demos. - Beijing : ACM. - 9781450368865 ; , s. 146-147
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- OpenFlow flow tables in Open vSwitch contain valuable information about installed flows, priorities, packet actions and routing policies. Their importance is emphasized when collocated tenants compete for the limited entries available to install flow rules. From a security point of view, OpenFlow flow tables are a valuable asset that requires both confidentiality and integrity guarantees. However, commodity software switch implementations - such as Open vSwitch - do not implement protection mechanisms capable to prevent attackers from either obtaining information about the installed flows or modifying the contents of flow tables. In this work, we adopt a radical approach to enabling OpenFlow flow table protection through decomposition. Based on a careful analysis of the architecture and implementation of Open vSwitch, we identify core assets requiring security guarantees, design an approach to isolating OpenFlow flow tables, and implement a prototype using Open vSwitch and Software Guard Extensions enclaves.
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| 2. |
- Fotrousi, Farnaz, et al.
(författare)
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The effect of requests for user feedback on Quality of Experience
- 2018
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Ingår i: Software quality journal. - : SPRINGER. - 0963-9314 .- 1573-1367. ; 26:2, s. 385-415
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Tidskriftsartikel (refereegranskat)abstract
- Companies are interested in knowing how users experience and perceive their products. Quality of Experience (QoE) is a measurement that is used to assess the degree of delight or annoyance in experiencing a software product. To assess QoE, we have used a feedback tool integrated into a software product to ask users about their QoE ratings and to obtain information about their rationales for good or bad QoEs. It is known that requests for feedback may disturb users; however, little is known about the subjective reasoning behind this disturbance or about whether this disturbance negatively affects the QoE of the software product for which the feedback is sought. In this paper, we present a mixed qualitative-quantitative study with 35 subjects that explore the relationship between feedback requests and QoE. The subjects experienced a requirement-modeling mobile product, which was integrated with a feedback tool. During and at the end of the experience, we collected the users' perceptions of the product and the feedback requests. Based on the users' rational for being disturbed by the feedback requests, such as "early feedback," "interruptive requests," "frequent requests," and "apparently inappropriate content," we modeled feedback requests. The model defines feedback requests using a set of five-tuple variables: "task," "timing" of the task for issuing the feedback requests, user's "expertise-phase" with the product, the "frequency" of feedback requests about the task, and the "content" of the feedback request. Configuration of these parameters might drive the participants' perceived disturbances. We also found that the disturbances generated by triggering user feedback requests have negligible impacts on the QoE of software products. These results imply that software product vendors may trust users' feedback even when the feedback requests disturb the users.
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| 3. |
- Petersen, Kai, et al.
(författare)
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Choosing Component Origins for Software Intensive Systems: In-House, COTS, OSS or Outsourcing?-A Case Survey
- 2018
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Ingår i: IEEE Transactions on Software Engineering. - : IEEE Computer Society. - 0098-5589 .- 1939-3520. ; 44:3, s. 237-261
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Tidskriftsartikel (refereegranskat)abstract
- The choice of which software component to use influences the success of a software system. Only a few empirical studies investigate how the choice of components is conducted in industrial practice. This is important to understand to tailor research solutions to the needs of the industry. Existing studies focus on the choice for off-the-shelf (OTS) components. It is, however, also important to understand the implications of the choice of alternative component sourcing options (CSOs), such as outsourcing versus the use of OTS. Previous research has shown that the choice has major implications on the development process as well as on the ability to evolve the system. The objective of this study is to explore how decision making took place in industry to choose among CSOs. Overall, 22 industrial cases have been studied through a case survey. The results show that the solutions specifically for CSO decisions are deterministic and based on optimization approaches. The non-deterministic solutions proposed for architectural group decision making appear to suit the CSO decision making in industry better. Interestingly, the final decision was perceived negatively in nine cases and positively in seven cases, while in the remaining cases it was perceived as neither positive nor negative.
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| 4. |
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| 5. |
- Andres, Bustamante, et al.
(författare)
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Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology : An Experimental Model
- 2021
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Ingår i: Photonics. - : MDPI. - 2304-6732. ; 8:4
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML) has an impressive capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied where three approaches of ML were explored. Once all images were analyzed, representative areas from every digital image were also extracted, processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning-support vector machine (TL-SVM) (AUC = 0.94, SPE 88%, SEN 100%) and transfer learning-random forest (TL- RF) method (AUC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUC = 0.84, SPE 77%, SEN 91%) and random forest (AUC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas using a small sample.
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| 6. |
- Cheddad, Abbas, et al.
(författare)
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Object recognition using shape growth pattern
- 2017
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Ingår i: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, ISPA. - : IEEE Computer Society Digital Library. - 9781509040117 ; , s. 47-52
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.
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| 7. |
- Fakhouri, Hussam N., et al.
(författare)
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A Comprehensive Study on the Role of Machine Learning in 5G Security : Challenges, Technologies, and Solutions
- 2023
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Ingår i: Electronics. - Basel : MDPI. - 2079-9292. ; 12:22, s. 1-44
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Tidskriftsartikel (refereegranskat)abstract
- Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms' role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network's security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network's underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security. © 2023 by the authors.
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| 8. |
- Gradolewski, Dawid, 1988-
(författare)
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Sensors and Algorithms in Industry 4.0 : Security and Health Preservation Applications
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Globalisation and technological digitisation have triggered an Industry 4.0. revolution. The core of this revolution is autonomisation of complex processes, which require expert knowledge. The technical foundations of Industry 4.0 are IoT, Big Data and AI technologies. Nowadays, autonomous systems are widely used to increase human and environmental safety and to prevent health degradation. Such non-industrial, life related applications demand high reliability as well as precision and accuracy, which challenge engineering science. The thesis objective is to provide suitable solutions for non-invasive, automated, and autonomous systems used for life protection and health maintenance. The proposed solutions enable non-invasive measurements by means of vision and acoustic sensors. The presented methods and systems are designed based on an analytical assessment of existing technologies and algorithms. New hardware solutions, signal and data processing methods, as well as classification and decision-making algorithms are proposed. Where required, additional customisations and modifications are applied. The systems and methods presented have been modelled and rigorously validated, and subsequently implemented and verified in a real environment. The scope of the thesis includes the assessment of functional requirements, precision, accuracy and reliability of life-related technological systems. It covers an analytical evaluation of proposed methods and algorithms of filtration, feature extraction, also detection, localization, identification, and classification of objects. The application fields are health monitoring, nature observation and facilitating collaborative frameworks in modern factories. The thesis specifically focuses on methods and algorithms of autonomous decision making concerning the risk of heart disease, the threat of fatal collision of rare birds with man-made structures and the prevention of accidents in modern robotised factories. It also deals with the implementation of the Industry 4.0 fundamentals, which are smart sensing, IoT and AI methods optimised to improve the system performance in a broad sense. The applied distributed computing method and machine-to-machine communication are aimed at limiting the data stream at an early stage of the decision-making process, and thus ensure the system’s cost-effectiveness. From the thesis, one can understand how the Industry 4.0 paradigm can contribute to autonomisation of compound processes and to increase system performance, without compromising its affordability.The thesis is divided into two parts. The first, Prolegomena provides an overview of the sensors and algorithms applicable to industrial safety along with human health and nature preservation. This part also visualizes the relationships and interactions among the articles comprising the second part named Papers. In general, each of the enclosed six papers deals with the problem of autonomisation of complex processes in real-time and in a regular environment.
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| 9. |
- Heskebeck, Frida, et al.
(författare)
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Multi-Armed Bandits in Brain-Computer Interfaces
- 2022
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Ingår i: Frontiers in Human Neuroscience. - : Frontiers Media S.A.. - 1662-5161. ; 16
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Forskningsöversikt (refereegranskat)abstract
- The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.
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| 10. |
- Kabiri, Charles, et al.
(författare)
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Outage Probability of a Cognitive Cooperative Relay Network with Multiple Primary Users Under Primary Outage Constraint
- 2016
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Ingår i: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR COMMUNICATIONS (ATC). - : IEEE. - 9781509027118 - 9781509027101 ; , s. 38-42
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Konferensbidrag (refereegranskat)abstract
- In this paper, we consider a cognitive cooperative relay network (CCRN) that utilizes a decode-and-forward relay in the presence of multiple primary users. Given this system setting, we study the effect of multiple primary transmitters (PU-Txs) and multiple primary user receivers (PU-Rxs) on the outage probability of the considered CCRN. Specifically, power allocation policies for the secondary user transmitter (SU-Tx) and the secondary relay (SR) are formulated, subject to the outage constraint which is imposed by the PU-Rxs as well as the peak transmit power limits given at the SU-Tx and SR. On the basis of these power allocation policies for the SU-Tx and SR, an expression for the outage probability of the CCRN is derived. Numerical examples are presented to show the effect of different system parameters on the outage probability of the CCRN.
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