By Andreas Junghanns, Jonathan Schaeffer (auth.), Robert E. Mercer, Eric Neufeld (eds.)
This publication constitutes the refereed court cases of the twelfth Biennial convention of the Canadian Society for Computational reviews of Intelligence, AI'98, held in Vancouver, BC, Canada in June 1998.
The 28 revised complete papers provided including 10 prolonged abstracts have been rigorously reviewed and chosen from a complete of greater than two times as many submissions. The e-book is split in topical sections on making plans, constraints, seek and databases; purposes; genetic algorithms; studying and common language; reasoning; uncertainty; and learning.
Read or Download Advances in Artificial Intelligence: 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98 Vancouver, BC, Canada, June 18–20, 1998 Proceedings PDF
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Additional resources for Advances in Artificial Intelligence: 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98 Vancouver, BC, Canada, June 18–20, 1998 Proceedings
SAODV cannot effectively prevent the hop count from being increased by malicious nodes. This increases the chances of the route being deselected from the potential candidate routes, which is another form of route redirection attack. In LHAP, again malicious nodes can use the exact technique described in RREP Routing Loop to create this type of attack. Formation of Routing Loops Two conditions need to be satisfied in order to launch this attack. The malicious node has to impersonate a legitimate node in the network and is able to modify the metric such as hop count to be a better value in terms of less hop count in this case.
Runtime observations are incomplete, but they can determine permissions which cannot be determined statically prior to execution. 2, existing work suﬀers from diﬀerent types of overapproximation. In order to eliminate overapproximation, the diﬀerent types of overapproximation are to be treated diﬀerently. This requires distinguishing them. This distinction can be achieved by recognising user input. Indecisiveness overapproximation can be eliminated for user resources if all user-initiated access is discarded from the generated policy.
The paper is organised as follows. Section 2 discusses related work. Section 3 contributes distinction of access to system resources and user resources. It also presents the automated application policy generation process. The prototype implementation of the runtime observation approach is addressed by Section 4. Section 5 illustrates the contribution applied on an application. An evaluation of the contribution and the prototype is provided by Section 6. Section 7 addresses issues to be considered, such as embedding the presented work in the software development life cycle (SDLC) and future work.
Advances in Artificial Intelligence: 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98 Vancouver, BC, Canada, June 18–20, 1998 Proceedings by Andreas Junghanns, Jonathan Schaeffer (auth.), Robert E. Mercer, Eric Neufeld (eds.)