False alarms triggered by security sensors incur high costs for all parties involved. According to police reports, a large majority of alarms are false. Recent advances in machine learning can enable automatically classifying alarms. However, building a scalable alarm verification system is a challenge, since the system needs to: (1) process thousands of alarms in real-time, (2) classify false ...

The success and the acceptance of intrusion detection systems essentially depend on the accuracy of their analysis. Inaccurate signatures strongly trigger false alarms. In practice several thousands false alarms per month are reported which limit the successful deployment of intrusion detection systems. Most today deployed intrusion detection systems apply misuse detection as detection procedur...

Nowadays, information plays an important role in organizations. Sensitive information is often stored in databases. Traditional mechanisms such as encryption, access control, and authentication cannot provide a high level of confidence. Therefore, the existence of Intrusion Detection Systems in databases is necessary. In this paper, we propose an intrusion detection system for detecting attacks...

As computer attacks are becoming more and more difficult to identify the need for better and more efficient intrusion detection systems increases. The main problem with current intrusion detection systems is high rate of false alarms. In this paper we discuss our experience in analyzing potential benefits of distributed multi sensor systems for intrusion detection. Our purpose for this work is ...

Conventional patient monitoring is performed by generating alarms when vital signs exceed pre-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a...

Conventional patient monitoring is performed by generating alarms when vital signs exceed pre-determined thresholds, but the false-alarm rate of such monitors in hospitals is so high that alarms are typically ignored. We propose a principled, probabilistic method for combining vital signs into a multivariate model of patient state, using extreme value theory (EVT) to generate robust alarms if a...

Performing Phonetic Search Keyword Spotting (PS KWS) in new languages when language resources are scarce is an interesting and challenging task. In a previous paper we reported a methodology that enabled PS KWS under these conditions utilizing cross-language phoneme mappings from another sufficiently resourced and well-trained source language. We performed phoneme recognition in the new target ...

Age-related increases in reliance on gist-based processes can cause increased false recognition. Understanding the neural basis for this increase helps to elucidate a mechanism underlying this vulnerability in memory. We assessed age differences in gist-based false memory by increasing image set size at encoding, thereby increasing the rate of false alarms. False alarms during a recognition tes...

False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition,...

Rekindles and false alarms are unusually high in the Portuguese wildfire management system, representing a high burden on suppression resources in particular, and fire management resources in general. In 20,049 occurrences that the suppression system handled in the summer of 2010, 12.5% were false alarms and 15.0% were rekindles. We present a discreteevent simulation model of a wildfire suppres...