Name
Abstract | ||
|---|---|---|
While the large scale distribution and unprecedented connectivity of embedded systems in the Internet of Things (IoT) has enabled various useful application scenarios, it also poses a risk to users and infrastructure alike. Recent incidents, like the Mirai botnet, have shown that these devices are often not sufficiently protected against attacks and can therefore be abused for malicious purposes, like distributed denial of service (DDoS) attacks. While it may be an impossible task to completely secure all systems against attacks, moving target defense (MTD) has been proposed as an alternative to prevent attackers from finding devices and endpoints and eventually launching their attacks against them. One of these approaches is network-based moving target defense which relies on the obfuscation and change of network level information, like IP addresses and ports. Since most of these approaches have been developed with desktop applications in mind, their usefulness in IoT applications has not been investigated.In this paper we provide a study on the applicability of network-based MTD for low-power devices. We investigate their capabilities to regularly change addresses. We furthermore investigate their performance with multiple assigned IP addresses, for both IPv4 and IPv6. We show that although some functionality of these systems may be impeded by constantly changing addresses, network-based MTD might nonetheless be a viable option to protect Internet-connected embedded systems from attacks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-74718-7_24 | COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2017, PT I |
Field | DocType | Volume |
Port (computer networking),Denial-of-service attack,Botnet,Computer security,Computer science,Internet of Things,Shuffling,Artificial intelligence,Obfuscation,Moving target defense,Network address,Machine learning | Conference | 10671 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aljosha Judmayer | 1 | 6 | 2.70 |
| Georg Merzdovnik | 2 | 71 | 8.21 |
| Johanna Ullrich | 3 | 61 | 8.51 |
| Artemios G. Voyiatzis | 4 | 112 | 14.08 |
| Edgar Weippl | 5 | 20 | 10.62 |