A number of real-time embedded systems (RTESs) are used to manage critical infrastructure such as electric grids or C 4 I systems. In these systems, it is essential to meet deadlines, for example, to avoid a power outage or loss of a life. The importance of security support is also increasing, because more RTESs are being networked. To securely transmit sensitive data, e.g., a battle field status, across the network, RTESs need to protect the data via cryptographic techniques. However, security support may cause deadline misses or unacceptable QoS degradation. As an initial effort to address this problem, we formulate the security support in RTESs as a QoS optimization problem. Also, we propose a novel adaptive approach for security support in which a RTES initially uses a relatively short cryptographic key to maximize the QoS, while increasing the key length when the security risk level is raised. In this way, we can make a possible cryptanalysis several orders of magnitude harder by requiring the attacker to search a larger key space, while meeting all deadlines by degrading the QoS in a controlled manner. To minimize the overhead, we derive the appropriate QoS levels for several key lengths via an offine polynomial time algorithm. When the risk level is raised online, a real-time task can use a longer key and adapt to the corresponding QoS level (derived offine) in O(1) time.
End of preview. The entire article is 6 pages. To view the full-text, please rent this article to continue.
/lp/association-for-computing-machinery/towards-security-and-qos-optimization-in-real-time-embedded-systems-cQkqIFhmnq
Welcome to DeepDyve! Rent Premier Research Articles and Save Up to 90%
Towards security and QoS optimization in real-time embedded systems
Kang, Kyoung-Don; Son, Sang H.
Follow ACM SIGBED Review
, Volume 3 (1)
Association for Computing Machinery – Jan 1, 2006
More Info
More Like This Article
View All dataSource[]=actageo&dataSource[]=aspet&dataSource[]=aaos&dataSource[]=aacc&dataSource[]=aacr&dataSource[]=aea&dataSource[]=aip&dataSource[]=ajnr&dataSource[]=ams&dataSource[]=aps_physical&dataSource[]=appi_book&dataSource[]=appi_journal&dataSource[]=apha&dataSource[]=asip&dataSource[]=asm&dataSource[]=asn&dataSource[]=aspb&dataSource[]=avs&dataSource[]=annual_reviews&dataSource[]=arxiv&dataSource[]=acm&dataSource[]=berghahn&dataSource[]=cabi&dataSource[]=clinical_trials&dataSource[]=dailymed&dataSource[]=degruyter&dataSource[]=du_press&dataSource[]=esa&dataSource[]=eu_press&dataSource[]=elsevier&dataSource[]=emerald&dataSource[]=ejtr&dataSource[]=emea&dataSource[]=epo&dataSource[]=faseb&dataSource[]=gsa&dataSource[]=health_affairs&dataSource[]=hindawi&dataSource[]=imanager&dataSource[]=imedpub&dataSource[]=informa_healthcare&dataSource[]=informs&dataSource[]=iop&dataSource[]=iucr&dataSource[]=iospress&dataSource[]=jbjs&dataSource[]=leftcoast&dataSource[]=lu_press&dataSource[]=mesharpe&dataSource[]=mary_ann_liebert&dataSource[]=medline&dataSource[]=mit_press&dataSource[]=nature&dataSource[]=oxford&dataSource[]=pier_professional&dataSource[]=pnas&dataSource[]=portlandpress&dataSource[]=psyc_articles&dataSource[]=psyc_books&dataSource[]=psyc_critiques&dataSource[]=plos_journal&dataSource[]=pubmed_central&dataSource[]=rsna&dataSource[]=rockefeller&dataSource[]=rcn&dataSource[]=ria&dataSource[]=rsc&dataSource[]=sage&dataSource[]=spie&dataSource[]=springer_journal&dataSource[]=springer&dataSource[]=taylor_francis&dataSource[]=aps&dataSource[]=the_scientist&dataSource[]=uc_press&dataSource[]=uspto_abstract&dataSource[]=wiley&dataSource[]=pct
© 2012 DeepDyve, Inc. All rights reserved.
Terms of Service | Privacy Policy
