By Álvaro Herrero, Emilio Corchado
ISBN-10: 3642182984
ISBN-13: 9783642182983
This monograph includes paintings on network-based Intrusion Detection (ID) that's grounded in visualisation and hybrid man made Intelligence (AI). It has resulted in the layout of MOVICAB-IDS (MObile VIsualisation Connectionist Agent-Based IDS), a unique Intrusion Detection method (IDS), that is comprehensively defined during this book.
This novel IDS combines diverse AI paradigms to visualize community site visitors for identification at packet point. it really is according to a dynamic Multiagent procedure (MAS), which integrates an unmonitored neural projection version and the Case-Based Reasoning (CBR) paradigm by using deliberative brokers which are able to studying and evolving with the environment.
The proposed novel hybrid IDS presents safeguard team of workers with an artificial, intuitive photo of community site visitors and protocol interactions. This visualisation interface helps the easy detection of anomalous events and their next identity.
The functionality of MOVICAB-IDS used to be established via a unique mutation-based trying out approach in numerous genuine domain names which entailed a number of assaults and anomalous situations.
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Additional resources for Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System
Example text
One solution to this problem is to wrap the legacy components, providing them with an "agent layer" functionality. 1 Agent Taxonomy Several different classifications of agents have been proposed in the literature [92, 100, 101]. The most widely accepted classifications are grouped under the following headings: • Interaction with the user − − Interface agents: users can interact with the system by means of commands. Autonomous agents: there is some interaction with the user but it is the agent who decides whether actions must be taken according to changes in the user behaviour.
In contrast, for unsupervised learning only the first two elements (input and internal dynamics of the network) are available. No external information is used to check on the weight setting process. Human beings appear to be able to learn without explicit supervision. Thus, unsupervised learning mimics this aspect of human learning; hence this type of learning tends to use more biologically plausible methods than those using error descent methods. A pertinent example is the local processing at each synapse in these algorithms, which involves no global information passing through the network.
The activations of the output neurons are fed back via the same weights to the input neurons as inhibition and are therefore subtracted. Then a (nonlinear) function of the weights is calculated and used to update the weights by applying the simple Hebbian learning rule. It was shown in [72] that the use of a nonlinear function f () in the above equations creates an algorithm to find those values of W that maximize that function, subject to the constraint that W is an orthogonal matrix. This idea was applied in [79] to the previously described network in the context of the network performing EPP.
Mobile Hybrid Intrusion Detection: The MOVICAB-IDS System by Álvaro Herrero, Emilio Corchado
by Thomas
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