1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Ανακοίνωση σε συνέδριο; Πρακτικά συνεδρίου
Τίτλος:	Detection and classification of malicious software based on regional matching of Temporal Graphs
Συγγραφέας:	[EL] Δούναβη, Ελένη - Μαρία[EN] Dounavi, Eleni - Maria [EL] Μπαντή, Άννα[EN] Mpanti, Anna [EL] Νικολόπουλος, Σταύρος[EN] Nikolopoulos, Stavros [EL] Πολενάκης, Ιωσήφ[EN] Polenakis, Iosif
Ημερομηνία:	18/06/2021
Περίληψη:	In this paper we present an integrated graph-based framework that utilizes relations between groups of System-calls, in order to detect whether an unknown software sample is malicious or benign, and to a further extent to classify it to a known malware family. A novel graph-based approach for the representation of software samples over the depiction of the structural evolution over time, the so-called Temporal Graphs, is discussed, and a method for measuring graph similarity among specific Regions of such graphs is proposed, the so-called Regional Matching. The partitioning of the Temporal Graphs that depicts their structural evolution over time is defined by specific time-slots, while the quantitative characteristics that depict the commonalities appeared over the weights of the vertices are measured by a similarity metric in order to conduct the malware detection and classification procedures. Finally, we evaluate the detection and classification ability of our proposed graph-based framework performing an experimental study over the achieved results utilizing a set of known malicious samples that are indexed into malware families.
Γλώσσα:	Αγγλικά
Τόπος δημοσίευσης:	Ruse, Bulgaria
Σελίδες:	6
DOI:	10.1145/3472410.3472417
ISBN:	978-1-4503-8982-2
Θεματική κατηγορία:	[EL] Επιστήμη ηλεκτρονικών υπολογιστών και Πληροφορική, άλλοι τομείς[EN] Computer and Information sciences, miscellaneous
Λέξεις-κλειδιά:	Malicious software; Malware Detection; malware classification; security
Κάτοχος πνευματικών δικαιωμάτων:	© 2021 Association for Computing Machinery. ACM.
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Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://dl.acm.org/doi/10.1145/3472410.3472417
Ηλεκτρονική διεύθυνση περιοδικού:	https://dl.acm.org/doi/proceedings/10.1145/3472410
Τίτλος πηγής δημοσίευσης:	CompSysTech '21: Proceedings of the 22nd International Conference on Computer Systems and Technologies
Σελίδες τεκμηρίου (στην πηγή):	28 - 33
Όνομα εκδήλωσης:	CompSysTech '21
Τοποθεσία εκδήλωσης:	Ruse, Bulgaria
Ημ/νία έναρξης εκδήλωσης:	18/06/2021
Ημ/νία λήξης εκδήλωσης:	19/06/2021
Σημειώσεις:	Conference site: https://www.compsystech.org/_cst21/ This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014- 2020” in the context of the project “Malicious Software Detection and Classification utilizing Temporal–Graphs of Discrete and Cumulative Structural Evolution” (MIS 5047642).
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες