Guaranteeing runtime integrity of embedded system software is an open problem. Trade-offs between security and other priorities (e.g., cost or performance) are inherent, and resolving them is both challenging and important. The proliferation of runtime attacks that introduce malicious code (e.g., by injection) into embedded devices has prompted a range of mitigation techniques. One popular approach is Remote Attestation (RA), whereby a trusted entity (verifier) checks the current software state of an untrusted remote device (prover). RA yields a timely authenticated snapshot of prover state that verifier uses to decide whether an attack occurred.
Current RA schemes require verifier to explicitly initiate RA, based on some unclear criteria. Thus, in case of prover's compromise, verifier only learns about it late, upon the next RA instance. While sufficient for compromise detection, some applications would benefit from a more proactive, prevention-based approach. To this end, we construct CASU: Compromise Avoidance via Secure Updates. CASU is an inexpensive hardware/software co-design enforcing: (i) runtime software immutability, thus precluding any illegal software modification, and (ii) authenticated updates as the sole means of modifying software. In CASU, a successful RA instance serves as a proof of successful update, and continuous subsequent software integrity is implicit, due to the runtime immutability guarantee. This obviates the need for RA in between software updates and leads to unobtrusive integrity assurance with guarantees akin to those of prior RA techniques, with better overall performance.
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Detecting Suspicious Package Updates
With an increased level of automation provided by
package managers, which sometimes allow updates to be installed
automatically, malicious package updates are becoming a real
threat in software ecosystems. To address this issue, we propose
an approach based on anomaly detection, to identify suspicious
updates based on security-relevant features that attackers could
use in an attack. We evaluate our approach in the context
of Node.js/npm ecosystem, to show its feasibility in terms of
reduced review effort and the correct identification of a confirmed
malicious update attack. Although we do not expect it to be
a complete solution in isolation, we believe it is an important
security building block for software ecosystems.
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- Award ID(s):
- 1717022
- NSF-PAR ID:
- 10097611
- Date Published:
- Journal Name:
- Proceedings of the Proc. International Conference on Software Engineering -- New Ideas Track (ICSE-NIER)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICSE-NIER.2019.00012
