Download Computer Safety, Reliability, and Security: 23rd by Andreas Pfitzmann (auth.), Maritta Heisel, Peter PDF

By Andreas Pfitzmann (auth.), Maritta Heisel, Peter Liggesmeyer, Stefan Wittmann (eds.)

Theimportanceofsafetyandsecurityisgrowingsteadily.Safetyisaqualityc- racteristic that typically has been thought of to be very important in embedded structures, and protection is mostly an important estate in enterprise functions. there's definitely a bent to take advantage of software-based suggestions in safety-critical purposes domain names, which raises the significance of security engineering te- niques. those comprise modelling and research innovations in addition to acceptable approaches and instruments. And it truly is definitely right that the volume of con?dential facts that require safety from unauthorized entry is transforming into. consequently, defense is essential. at the one hand, the normal motivations for addressing security and safety nonetheless exist, and their relevance has more desirable. however, security and safety requisites ensue more and more within the comparable procedure. at this time, many software-based structures have interaction with technical apparatus they usually speak, e.g., with clients and different structures. destiny platforms will increasingly more have interaction with many different entities (technical structures, humans, the en- ronment). during this scenario, safety difficulties can cause safety-related disasters. it really is hence essential to handle security and safety. it really is in addition required take into consideration the interactions among those properties.

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Extra info for Computer Safety, Reliability, and Security: 23rd International Conference, SAFECOMP 2004, Potsdam, Germany, September 21-24, 2004. Proceedings

Example text

This learning law (12) moves the FSOM output y * closer to the training data output y by increasing or decreasing the influence of the runner-up rule and is described as follows: • • The influence of the runner-up rule is increased by moving runner-up spread edge towards the centre of the winner rule. The influence of the runner-up rule is decreased by moving runner-up spread edge away from the winner rule centre and towards sw ,k . sr ,k ( t + 1 ) = sr ,k ( t ) + gU ,r ( t )[ cw ,k ( t ) − sr ,k ( t ) ], sgn ( y − y * ) = sgn ( yr − yw ), sr ,k ( t + 1 ) = sr ,k ( t ) + gU ,r ( t )[ sw ,k ( t ) − sr ,k ( t ) ], otherwise (12) Where the learning coefficient of the fuzzy sets U r , j is defined as gU ,r .

The strategy is to first argue that the rule base completely covers the defined input space during generalisation. Assurance is provided through Preliminary System Safety Assessment (PSSA) which evaluates input space coverage by checking for “holes (by examining rule input sets). However, even if the input space is covered, an invalid output (missing value) may still be produced, since the output function may partially cover the output set. The solution to this is provided by the rule output bounds defined by (16).

The requirements specification as well as the system model are transition relations, describing what states are permissible/possible in the present moment of time given the system state at the previous moment of time. g. that two events are separated by a 5 seconds (say) interval. It turns out that in the present application, there is seldom a need for such specific time intervals. When they do occur they are simulated by assuming the existence of a clock which will signal when the given time interval has elapsed.

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