Collaborative Computer Security and Trust Management by Jean-Marc Seigneur, Adam Slagell

By Jean-Marc Seigneur, Adam Slagell

Collaborative laptop safety and belief administration combines views of top researchers in collaborative safety to debate fresh advances during this burgeoning new box. Practitioners, researchers, and academicians are offered with classes discovered by way of foreign specialists to satisfy the hot demanding situations of safeguard within the worldwide details age. overlaying themes equivalent to trust-based safeguard, possibility and threat research, and information sharing, this reference publication presents an entire choice of the most recent box advancements.

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Extra info for Collaborative Computer Security and Trust Management (Premier Reference Source)

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Tasks are divided between group members and roles are assigned. Nevertheless, in this phase a collaborative approach can be employed as well. In the following, the term joint intrusion detection is used when a differentiation between collaboration and cooperation is not necessary. CIMD is a part of ongoing research in the context of research activities aiming to develop autonomous intrusion detection and response techniques. This work contributes a taxonomy-based data model reflecting relevant properties of the participants of the overlay.

E. in the worst-case there exists a reasonable probability a device remains uninfected during the vulnerability interval independent of the used approach. Second, we will focus on the correlation between the total number of different scanning devices and the decreasing effectiveness of the non-cooperative scenario. Let pinf be the probability a susceptible node vx requests a web site from an infected web server and n be the number of total requests a node conducts before a signature update is available.

Teamworking for Security homogeneous detection group We already introduced ongoing research in cooperative AIS in the context of collaborative intrusion detection. , 1994). ). In a training phase, these detectors are compared to feature vectors describing the self. In the case of a match the detectors are eliminated, while the remaining detectors are considered mature. They describe the non-self and are used for the detection of anomalies. There are two challenges arising when dealing with anomaly detection schemes in general respectively with AIS in particular: on the one hand, anomaly detection often suffers from high false positive rates.

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