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Taxonomy on Existing Techniques of Reducing False Alarms in Sensor-Based Healthcare Monitoring - Literature review Example

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The paper "Taxonomy on Existing Techniques of Reducing False Alarms in Sensor-Based Healthcare Monitoring" states that Solet and Barach (2012) presented a non-technical approach to eliminating false positives in healthcare patients with intractable problems…
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Taxonomy on Existing Techniques of Reducing False Alarms in Sensor-Based Healthcare Monitoring
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Download file to see previous pages According to Qassim, Patel and Modzhin (2014), fuzzy logic risk analysis is implemented to reduce false alarm instances and maintain a sufficient level of security against serious attacks in intrusion and detection systems. The primary purpose of an intrusion and detection system is to identify attackers trying to infiltrate a network and expose vulnerable resources. By using a Fuzzy Logic-Risk Analysis (FLRA) model, the Qassim et al. (2014) aimed to reduce instances of false positives. By calculating the significance and severity of each suspected attack, the system establishes whether an activity can be classified as an attempted attack or normal behavior miss judged by the detection system. The model comprises four layers: an integrated interface, knowledge manager, autonomic manager, and resource manager. The topmost layer represents the integration point of the system administrator and the intrusion detection system. At this level, strategies and policies are defined and implemented. The section responsible for false-positive reduction is the Intrusion Detection Manager. This second layer referred to as the Fuzzy Logic-Risk Analysis Intrusion Detection Manager is responsible comprises of four modules: monitor module, analyzer, planner module and control module. All the modules act systematically to detect any attempts of intrusion, eliminate false positives and perform necessary changes to the protected element. Fuzzy Logic-Risk Analysis model works in two mechanisms: identifying the risk and assessing the risk. Once the risk has been identified it is assessed using weighted averages and consequently categorized. Threats are categorized based on their ability to cause harm to asset elements. Once the risk has been identified, residual and exposed risks are analyzed using Fuzzy logic from which different countermeasures are applied to mitigate it. FLRA model works by taking the inputs and outputting applicable ranges which are termed as Critical or Low with intermediaries in between. ...Download file to see next pages Read More
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