Carolina Pelaez

A tutorial on viruses, worms, bacteria, and other computer diseases is presented. It describes how experts classify the various forms of malicious code at work today, the ways in which they hide and work their mischief, and a few of the infamous incidents-such as the attack on the Internet network, in the fall of 1988, of a worm program that devoured massive amounts of CPU time-that have gained international notoriety. Some of the basic forms of prevention and cure are discussed.>

This paper explores the application of Fuzzy Cognitive Maps (FCM) to Failure Modes and Effects Analysis (FMEA). FMEAs are used in reliability and safety evaluations of complex systems to determine the effects of component failures on the system operation. FCMs use a digraph to show cause and effect relationships between concepts; thus, they can represent the causal relationships needed for the FMEA and provide a new strategy for predicting failure effects in a complex system.

This paper presents an overview of the main categories of malicious programs known as Trojan horses, viruses, bacteria, worms, and logic bombs. The focus is on their general behavior and the properties seen in their implementations rather than the ultimate effects or their intended destructive behavior. Possible preventive measures are also discussed

This paper presents an overview of the main categories of malicious programs known as Trojan horses, viruses, bacteria, worms, and logic bombs. The focus is on their general behavior and the properties seen in their implementations rather than the ultimate effects or their intended destructive behavior. Possible preventive measures are also discussed

Fuzzy logic provides a tool for directly manipulating the linguistic terms that an analyst employs in making a criticality assessment for a failure modes, effects and criticality analysis (FMECA). This allows an analyst to evaluate the risk associated with item failure modes in a natural way. Appropriate actions to correct or mitigate the effects of the failure can be prioritized even though the information available is vague, ambiguous, qualitative, or imprecise

This paper describes a new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA). As in a traditional criticality analysis, the assessment is based on the severity, frequency of occurrence, and detectability of an item failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskiness of the failure. This approach resolves some of the problems in traditional methods of evaluation and it has several advantages compared to strictly numerical methods: 1) it allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment; 2) ambiguous, qualitative, or imprecise information, as well as quantitative data, can be used in the assessment and they are handled in a consistent manner; and 3) it gives a more flexible structure for combining the severity, occurrence, and detectability parameters.Two fuzzy logic based approaches for assessing criticality are presented. The first is based on the numerical rankings used in a conventional Risk Priority Number (RPN) calculation and uses crisp inputs gathered from the user or extracted from a reliability analysis. The second, which can be used early in the design process when less detailed information is available, allows fuzzy inputs and also illustrates the direct use of the linguistic rankings defined for the RPN calculations.