Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
Interactive Retrieval System for Multi-Stream Collections : MultiXview at CASTLE 2025 Interactive Grand Challenge
(Association for Computing Machinery, Inc, 2025-10-27) Khan, Omar Shahbaz; Sharma, Ujjwal; Marcelino, Gonçalo; Duane, Aaron; Rudinac, Stevan; Worring, Marcel; Jónsson, Björn Pór; Department of Computer Science
We introduce multiXview, an interactive retrieval framework for synchronized multi-camera video collections. It features a multi-index search engine that supports natural-language queries over visual embeddings, speech transcripts, and scene descriptions. It supports a synchronized multi-stream player offering parallel playback, and a timeline-based navigation view for temporal scoping and faceted exploration. These components address the redundancy and fragmentation of overlapping egocentric and exocentric video feeds and enable users to locate, aggregate, and reconstruct events across partial perspectives. This paper focuses on system design and implementation, with quantitative and qualitative evaluation to take place at the CASTLE 2025 Grand Challenge Interactive Track.
Identifying malicious hosts involved in periodic communications
(Institute of Electrical and Electronics Engineers Inc., 2017-12-08) Apruzzese, Giovanni; Marchetti, Mirco; Colajanni, Michele; Zoccoli, Gabriele Gambigliani; Guido, Alessandro; Avresky, Dimiter R.; Gkoulalas-Divanis, Aris; Avresky, Dimiter R.; Correia, Miguel P.; Department of Computer Science
After many research efforts, Network Intrusion Detection Systems still have much room for improvement. This paper proposes a novel method for automatic and timely analysis of traffic generated by large networks, which is able to identify malicious external hosts even if their activities do not raise any alert by existing defensive systems. Our proposal focuses on periodic communications, since our experimental evaluation shows that they are more related to malicious activities, and it can be easily integrated with other detection systems. We highlight that periodic network activities can occur at very different intervals ranging from seconds to hours, hence a timely analysis of long time-windows of the traffic generated by large organizations is a challenging task in itself. Existing work is primarily focused on identifying botnets, whereas the method proposed in this paper has a broader target and aims to detect external hosts that are likely involved in any malicious operation. Since malware-related network activities can be considered as rare events in the overall traffic, the output of the proposed method is a manageable graylist of external hosts that are characterized by a considerably higher likelihood of being malicious compared to the entire set of external hosts contacted by the monitored large network. A thorough evaluation on a real large network traffic demonstrates the effectiveness of our proposal, which is capable of automatically selecting only dozens of suspicious hosts from hundreds of thousands, thus allowing security operators to focus their analyses on few likely malicious targets.
High Resolution In-Situ Skin Cancer Microwave Imaging Using Super Wideband Antenna
(Institute of Electrical and Electronics Engineers Inc., 2023) Alamro, Wasan; Seet, Boon Chong; Wang, Lulu; Parthiban, Prabakar; Zhao, XiaoMing; Li, Qingli; Wang, Lipo; Department of Engineering
This paper investigates the capability of a super wideband (SWB) antenna based imaging system in early-stage skin cancer detection. The imaging system consists of eight antenna elements in a circular array positioned around a human trunk phantom modeled as five concentric layers of skin, fat, muscle, rib bone and lung tissues. The dielectric properties of the phantom tissues are calculated using Cole-Cole model. The skin tumor is modeled as cylindrical shape on the outer skin layer with 1mm thickness and 15mm diameter. The resulting S-parameters of healthy and malignant phantoms obtained over different frequency ranges are compared and used to reconstruct the 2D images of the trunk area. The results show that the proposed SWB imaging system can detect and localize in-situ skin cancer with relatively high accuracy.
Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors
(Institute of Electrical and Electronics Engineers Inc., 2019-09) Apruzzese, Giovanni; Colajanni, Michele; Marchetti, Mirco; Gkoulalas-Divanis, Aris; Marchetti, Mirco; Avresky, Dimiter R.; Department of Computer Science
Classifiers based on Machine Learning are vulnerable to adversarial attacks, which involve the creation of malicious samples that are not classified correctly. While this phenomenon has been extensively studied within the image processing domain, comprehensive analyses are scarce in the cybersecurity field. This is a critical problem because cyber-detectors are being increasingly integrated with machine learning methods, making them suitable targets for skilled attackers leveraging adversarial samples to evade detection. In this paper, we propose a thorough analysis of realistic adversarial attacks performed against network intrusion detection systems that focus on identifying botnet traffic through machine learning classifiers. Our large campaign of experiments involves the most recent public datasets, representing multiple realistic network scenarios. Moreover, we evaluate the impact of these attacks against state-of-the-art detectors relying on different machine learning algorithms, providing a clear overview of this problem. The results outline the fragility of these methods. Our study represent a stepping stone for devising suitable countermeasures to the menace of adversarial attacks against cyber-detectors.
Evading botnet detectors based on flows and random forest with adversarial samples
(Institute of Electrical and Electronics Engineers Inc., 2018-11-26) Apruzzese, Giovanni; Colajanni, Michele; Department of Computer Science
Machine learning is increasingly adopted for a wide array of applications, due to its promising results and autonomous capabilities. However, recent research efforts have shown that, especially within the image processing field, these novel techniques are susceptible to adversarial perturbations. In this paper, we present an analysis that highlights and evaluates experimentally the fragility of network intrusion detection systems based on machine learning algorithms against adversarial attacks. In particular, our study involves a random forest classifier that utilizes network flows to distinguish between botnet and benign samples. Our results, derived from experiments performed on a public real dataset of labelled network flows, show that attackers can easily evade such defensive mechanisms by applying slight and targeted modifications to the network activity generated by their controlled bots. These findings pave the way for future techniques that aim to strengthen the performance of machine learning-based network intrusion detection systems.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts