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
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.
E-PhishGEN : Unlocking Novel Research in Phishing Email Detection
(Association for Computing Machinery, Inc, 2025-12-30) Pajola, Luca; Caripoti, Eugenio; Banzer, Stefan; Pizzi, Simeone; Conti, Mauro; Apruzzese, Giovanni; Department of Computer Science
Every day, our inboxes are flooded with unsolicited emails, ranging between annoying spam to more subtle phishing scams. Unfortunately, despite abundant prior efforts proposing solutions achieving near-perfect accuracy, the reality is that countering malicious emails still remains an unsolved dilemma. This "open problem"paper carries out a critical assessment of scientific works in the context of phishing email detection. First, we focus on the benchmark datasets that have been used to assess the methods proposed in research. We find that most prior work relied on datasets containing emails that - we argue - are not representative of current trends, and mostly encompass the English language. Based on this finding, we then re-implement and re-assess a variety of detection methods reliant on machine learning (ML), including large-language models (LLM), and release all of our codebase - an (unfortunately) uncommon practice in related research. We show that most such methods achieve near-perfect performance when trained and tested on the same dataset - a result which intrinsically hinders development (how can future research outperform methods that are already near perfect?). To foster the creation of "more challenging benchmarks"that reflect current phishing trends, we propose E-PhishGEN, an LLM-based (and privacy-savvy) framework to generate novel phishing-email datasets. We use our E-PhishGEN to create E-PhishLLM, a novel phishing-email detection dataset containing 16616 emails in three languages. We use E-PhishLLM to test the detectors we considered, showing a much lower performance than that achieved on existing benchmarks - indicating a larger room for improvement. We also validate the quality of E-PhishLLM with a user study (n=30). To sum up, we show that phishing email detection is still an open problem - and provide the means to tackle such a problem by future research.
Dynamic Current Distribution in the Electrodes of Submerged Arc Furnace Using Scalar and Vector Potentials
(Springer Verlag, 2018) Tesfahunegn, Yonatan Afework; Magnusson, Thordur; Tangstad, Merete; Saevarsdottir, Gudrun; Krzhizhanovskaya, Valeria V.; Lees, Michael Harold; Sloot, Peter M.; Dongarra, Jack; Shi, Yong; Tian, Yingjie; Fu, Haohuan; Department of Engineering
This work presents computations of electric current distributions inside an industrial submerged arc furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potentials approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase submerged arc furnace. The current distributions within electrodes due to skin and proximity effects are presented. The results show that the proposed method adequately models these phenomena.
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