Distributed Heterogeneous N-Variant Execution

Alexios Voulimeneas; Dokyung Song; Fabian Parzefall; Yeoul Na; Per Larsen; Michael Franz; Stijn Volckaert

doi:10.1007/978-3-030-52683-2_11

Distributed Heterogeneous N-Variant Execution

Alexios Voulimeneas, Dokyung Song, Fabian Parzefall, Yeoul Na, Per Larsen, Michael Franz, Stijn Volckaert

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON, a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.

Original language	English
Title of host publication	Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings
Editors	Clémentine Maurice, Leyla Bilge, Gianluca Stringhini, Nuno Neves
Publisher	Springer
Pages	217-237
Number of pages	21
ISBN (Print)	9783030526825
DOIs	https://doi.org/10.1007/978-3-030-52683-2_11
Publication status	Published - 2020
Event	17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020 - Lisbon, Portugal Duration: 2020 Jun 24 → 2020 Jun 26

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12223 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020
Country/Territory	Portugal
City	Lisbon
Period	20/6/24 → 20/6/26

Bibliographical note

Funding Information:
The authors thank Kostis Kaffes, Marios Pomonis, Georgios Detorakis, Lefteris Kokoris-Kogias, Anil Altinay, Mohaned Qunaibit, Paul Kirth, David Gens, Adrian Dabrowski and our reviewers. This material is based upon work partially supported by the Defense Advanced Research Projects Agency under contract FA8750-16-C-0260, by the United States Office of Naval Research under contract N00014-17-1-2782, and by the National Science Foundation under award CNS-161921. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Defense Advanced Research Projects Agency or its Contracting Agents, the Office of Naval Research or its Contracting Agents, the National Science Foundation, or any other agency of the U.S. Government.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-52683-2_11

Cite this

Voulimeneas, A., Song, D., Parzefall, F., Na, Y., Larsen, P., Franz, M., & Volckaert, S. (2020). Distributed Heterogeneous N-Variant Execution. In C. Maurice, L. Bilge, G. Stringhini, & N. Neves (Eds.), Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings (pp. 217-237). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12223 LNCS). Springer. https://doi.org/10.1007/978-3-030-52683-2_11

Voulimeneas, Alexios ; Song, Dokyung ; Parzefall, Fabian et al. / Distributed Heterogeneous N-Variant Execution. Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings. editor / Clémentine Maurice ; Leyla Bilge ; Gianluca Stringhini ; Nuno Neves. Springer, 2020. pp. 217-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Distributed Heterogeneous N-Variant Execution",

abstract = "N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON, a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.",

author = "Alexios Voulimeneas and Dokyung Song and Fabian Parzefall and Yeoul Na and Per Larsen and Michael Franz and Stijn Volckaert",

note = "Funding Information: The authors thank Kostis Kaffes, Marios Pomonis, Georgios Detorakis, Lefteris Kokoris-Kogias, Anil Altinay, Mohaned Qunaibit, Paul Kirth, David Gens, Adrian Dabrowski and our reviewers. This material is based upon work partially supported by the Defense Advanced Research Projects Agency under contract FA8750-16-C-0260, by the United States Office of Naval Research under contract N00014-17-1-2782, and by the National Science Foundation under award CNS-161921. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Defense Advanced Research Projects Agency or its Contracting Agents, the Office of Naval Research or its Contracting Agents, the National Science Foundation, or any other agency of the U.S. Government. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020 ; Conference date: 24-06-2020 Through 26-06-2020",

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Voulimeneas, A, Song, D, Parzefall, F, Na, Y, Larsen, P, Franz, M & Volckaert, S 2020, Distributed Heterogeneous N-Variant Execution. in C Maurice, L Bilge, G Stringhini & N Neves (eds), Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12223 LNCS, Springer, pp. 217-237, 17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020, Lisbon, Portugal, 20/6/24. https://doi.org/10.1007/978-3-030-52683-2_11

Distributed Heterogeneous N-Variant Execution. / Voulimeneas, Alexios; Song, Dokyung; Parzefall, Fabian et al.
Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings. ed. / Clémentine Maurice; Leyla Bilge; Gianluca Stringhini; Nuno Neves. Springer, 2020. p. 217-237 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12223 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Voulimeneas, Alexios

AU - Song, Dokyung

AU - Parzefall, Fabian

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AU - Franz, Michael

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N2 - N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON, a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Voulimeneas A, Song D, Parzefall F, Na Y, Larsen P, Franz M et al. Distributed Heterogeneous N-Variant Execution. In Maurice C, Bilge L, Stringhini G, Neves N, editors, Detection of Intrusions and Malware, and Vulnerability Assessment - 17th International Conference, DIMVA 2020, Proceedings. Springer. 2020. p. 217-237. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-52683-2_11

Distributed Heterogeneous N-Variant Execution

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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