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
Scalable architecture for online prioritisation of cyber threats
(NATO CCD COE Publications, 2017-12-26) Pierazzi, Fabio; Apruzzese, Giovanni; Colajanni, Michele; Guido, Alessandro; Marchetti, Mirco; Jakschis, Raik; Minarik, Tomas; Lindstrom, Lauri; Roigas, Henry; Department of Computer Science
Detecting advanced attacks is increasingly complex and no single solution can work. Defenders can leverage logs and alarms produced by network and security devices, but big data analytics solutions are necessary to transform huge volumes of raw data into useful information. Existing anomaly detection frameworks either work offline or aim to mark a host as compromised, with high risk of false alarms. We propose a novel online approach that monitors the behaviour of each internal host, detects suspicious activities possibly related to advanced attacks, and correlates these anomaly indicators to produce a list of the most likely compromised hosts. Due to the huge number of devices and traffic logs, we make scalability one of our top priorities. Therefore, most computations are independent of the number of hosts and can be naively parallelised. A large set of experiments demonstrates that our proposal can pave the way to novel forms of detection of advanced malware.
Reproducibility Companion Paper : Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks
(Association for Computing Machinery, Inc, 2025-10-27) Alimohammadzadeh, Hamed; Ghandeharizadeh, Shahram; Cunico, Federico; Springer, Joshua; Department of Computer Science
This companion paper provides artifacts and instructions on replicating the experiments in the ACM Multimedia 2024 paper entitled ''Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks.'' Swarm-based hierarchical, Swarical, is a localization technique that enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. It consists of two components, an offline planner and an online localization technique that executes on an FLS. The offline planner uses the FLS sensor specification for positioning to convert mesh files into swarms of FLSs. Some FLSs are dark and used only for localization. We reported the online localization technique to be fast and highly accurate. We describe how to reproduce this finding using our artifacts.
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection
(Association for Computing Machinery, 2021-08-17) Corsini, Andrea; Yang, Shanchieh Jay; Apruzzese, Giovanni; Department of Computer Science
Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of network traffic flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios; moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. Then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a ĝ€sequential' Long Short-Term Memory (LSTM) against a ĝ€static' Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5% F1-score; while obtaining superior performance in the CTU13, with 95.7% F1-score against 91.5%. This paper thus paves the way to future applications of sequential learning models for NIDS.
On the effectiveness of machine and deep learning for cyber security
(NATO CCD COE Publications, 2018-07-05) Apruzzese, Giovanni; Colajanni, Michele; Ferretti, Luca; Guido, Alessandro; Marchetti, Mirco; Minarik, Tomas; Lindstrom, Lauri; Jakschis, Raik; Department of Computer Science
Machine learning is adopted in a wide range of domains where it shows its superiority over traditional rule-based algorithms. These methods are being integrated in cyber detection systems with the goal of supporting or even replacing the first level of security analysts. Although the complete automation of detection and analysis is an enticing goal, the efficacy of machine learning in cyber security must be evaluated with the due diligence. We present an analysis, addressed to security specialists, of machine learning techniques applied to the detection of intrusion, malware, and spam. The goal is twofold: to assess the current maturity of these solutions and to identify their main limitations that prevent an immediate adoption of machine learning cyber detection schemes. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic.
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.
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